Package 'swash' reference manual

Title:	Health Geography Toolbox for Model-Based Analysis of Infections Panel Data
Description:	Within epidemic outbreaks, infections grow and decline differently between regions, and the velocity of spatial spread differs between countries. The swash library offers a set of model-based analyses for these topics. Spread velocity may be analysed with the Swash-Backwash Model for the Single Epidemic Wave and corresponding functions for bootstrap confidence intervals, country comparison, and visualization of results. Differences in epidemic growth between regions may be analysed using logistic growth models, exponential growth models, Hawkes processes and breakpoint analyses. All functionalities are accessed by the class "infpan" for infections panel data defined in this package, which is built from a data.frame provided by the user.
Authors:	Thomas Wieland [aut, cre] (ORCID: <https://orcid.org/0000-0001-5168-9846>)
Maintainer:	Thomas Wieland <[email protected]>
License:	GPL (>= 2)
Version:	2.0.1
Built:	2026-05-24 11:32:01 UTC
Source:	https://github.com/cran/swash

swash: Health Geography Toolbox for Model-Based Analysis of Infections Panel Data

Description

The R library swash provides a toolbox for quantitative analyses in health geography with a focus on the spatial spread of infectious diseases. It bundles functions developed by the author between 2020 and 2023 for the quantitative analysis of (panel) infection data during the COVID-19 pandemic. The aim was to consolidate these methods and analytical tools into a unified and coherent framework. The target audience of this R package consists of researchers and practitioners in the fields of health geography, spatial epidemiology, and statistics.

In order to use all functionalities, the user should import her/his infections panel data using the function load_infections_paneldata(), which returns an instance of class infpan. The panel data is checked whether it is balanced and whether it includes missing values. From an infpan object, the user may utilize the following built-in analysis models and visualization functions:

Swash-Backwash Model for the Single Epidemic Wave, including further analysis towards bootstrap-based inference and country comparison as well as visualization
Growth Analysis with logistic and exponential growth models for cumulative or incremental infections, whereby the former is intended for the entire infection wave, and the latter for the initial phase of the infection wave; including visualization
Hawkes process models for incremental infections; including visualization
Breakpoints analysis using the Bai-Perron algorithm implemented in strucchange::breakpoints; including visualization
Calculation of further epidemic indicators from the infections panel data such as the effective reproduction number
Plots of infection curves by region

infpan objects and objects resulting from the functions mentioned above have summary() and plot() methods. All mentioned functions may be used stand-alone as well.

Details

Based on an infpan object, several indicators may be calculated from incremental infections values, such as incidence or effective reprodction number $R_t$ . Infection curves may be plotted by plot(infpan). All built-in model analyses may be conducted based on an instance of class infpan.

The Swash-Backwash Model (SBM) for the Single Epidemic Wave is the spatial equivalent of the classic epidemiological SIR (Susceptible-Infected-Recovered) model. It was developed by Cliff and Haggett (2006) to model the velocity of spread of infectious diseases across space. Current applications can be found, for example, in Smallman-Raynor et al. (2022a,b). This package enables the calculation of the Swash-Backwash Model for user-supplied panel data on regional infections. The core of this is the swash_backwash() function, which calculates the model and creates a model object of the sbm class defined in this package. This class can be used to visualize results (summary(), plot()) and calculate bootstrap confidence intervals for the model estimates (confint(sbm)); the latter returns an object of the built-in class sbm_ci. Two sbm_ci objects for different countries may be compared with compare_countries(), which allows the estimation of mean differences of a user-specified model parameter (e.g., spatial reproduction number $R_{OA}$ ) between two countries. This makes it possible to check whether the spatial spread velocity of a communicable disease is significantly different in one country than in another country; the result is an object of class countries. To calculate the SBM model based on an infpan object, use the corresponding method swash(infpan).

The library allows for estimating growth models based on time series of infections. Logistic and exponential growth models (see, e.g., Chowell et al. 2014, 2015, Pell et al. 2018, Wieland 2020a, 2020b) as well as Hawkes process models (see, e.g. Rizoiu et al. 2018) are provided. Additionally, breakpoints in time series may be detected (see, e.g., Wieland 2020b). A model for a single time series may be estimated with the built-in functions logistic_growth(), exponential_growth(), hawkes_growth(), or breaks_growth(), respectively. These function return objects of class loggrowth, expgrowth, hawkes, and breaksgrowth, respectively, all of them defined in this package. Plotting is available via the plot method. Estimating such a model based on an infpan object is provided by the infpan methods growth(), growth_initial(), growth_hawkes(), and growth_breaks(), respectively, all of them resulting in an object of class growthmodels.

The package also contains other functions for spatio-temporal analysis, including spatial statistics (nbstat() for neighborhood statistics) and fit metrics (metrics(), binary_metrics(), binary_metrics_glm()). The package includes example data from the COVID-19 pandemic.

Author(s)

Thomas Wieland

References

Chowell G, Simonsen L, Viboud C, Yang K (2014) Is West Africa Approaching a Catastrophic Phase or is the 2014 Ebola Epidemic Slowing Down? Different Models Yield Different Answers for Liberia. PLoS currents 6. doi:10.1371/currents.outbreaks.b4690859d91684da963dc40e00f3da81

Chowell G, Viboud C, Hyman JM, Simonsen L (2015) The Western Africa ebola virus disease epidemic exhibits both global exponential and local polynomial growth rates. PLOS Currents Outbreaks, ecurrents.outbreaks.8b55f4bad99ac5c5db3663e916803261. doi:10.1371/currents.outbreaks.8b55f4bad99ac5c5db3663e916803261

Cliff AD, Haggett P (2006) A swash-backwash model of the single epidemic wave. Journal of Geographical Systems 8(3), 227-252. doi:10.1007/s10109-006-0027-8

Li, MY (2018) An Introduction to Mathematical Modeling of Infectious Diseases. doi:10.1007/978-3-319-72122-4

Nishiura H, Chowell G (2009) The effective reproduction number as a prelude to statistical estimation of time-dependent epidemic trends. In Chowell G, Hyman JM, Bettencourt LMA (eds.) Mathematical and statistical estimation approaches in epidemiology, 103–121. doi:10.1007/978-90-481-2313-1_5

Pell B, Kuang Y, Viboud C, Chowell G (2018) Using phenomenological models for forecasting the 2015 ebola challenge. Epidemics 22, 62–70. doi:10.1016/j.epidem.2016.11.002

Rizoiu MA, Mishra S, Kong Q, Carman M, Xie L. (2018) SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations. In: Proceedings of the 2018 World Wide Web Conference. WWW’18. Republic and Canton of Geneva, CHE: International World Wide Web Conferences Steering Committee, p. 419–428. doi:10.1145/3178876.3186108

Smallman-Raynor MR, Cliff AD, Stickler PJ (2022a) Meningococcal Meningitis and Coal Mining in Provincial England: Geographical Perspectives on a Major Epidemic, 1929–33. Geographical Analysis 54, 197–216. doi:10.1111/gean.12272

Smallman-Raynor MR, Cliff AD, The COVID-19 Genomics UK (COG-UK) Consortium (2022b) Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020–December 2021. Epidemiology and Infection 150, e145. doi:10.1017/S0950268822001285.

Viboud C, Bjørnstad ON, Smith DL, Simonsen L, Miller MA, Grenfell BT (2006) Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza. Science 312, 447-451. doi:10.1126/science.1125237

Wieland T (2020a) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324

Wieland T (2020b) A phenomenological approach to assessing the effectiveness of COVID-19 related nonpharmaceutical interventions in Germany. Safety Science 131, 104924. doi:10.1016/j.ssci.2020.104924

Wieland T (2022) Spatial patterns of excess mortality in the first year of the COVID-19 pandemic in Germany. European Journal of Geography 13(4), 18-33. doi:10.48088/ejg.t.wie.13.4.018.033

Wieland T (2025) Assessing the effectiveness of non-pharmaceutical interventions in the SARS-CoV-2 pandemic: results of a natural experiment regarding Baden-Württemberg (Germany) and Switzerland in the second infection wave. Journal of Public Health 33(11), 2497-2511. doi:10.1007/s10389-024-02218-x

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <-
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c(
      "Population" = "pop"
        ), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

is(infpan_CH)
# "infpan"

plot(
  infpan_CH,
  plot_rollmean = TRUE
  )
# Plot cases

infpan_CH <- calculate_Rt(
  infpan_CH,
  verbose = TRUE
  )
# Calculate effective reproduction number

summary(infpan_CH)
# Summary of infpan object

timestamps(infpan_CH)
# Time stamps of infpan object

CH_covidwave1 <-
  swash(
    infpan_CH,
    verbose = TRUE
    )
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)

summary(CH_covidwave1)
# Summary of Swash-Backwash Model
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <-
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c(
      "Population" = "pop"
        ), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

is(infpan_CH)
# "infpan"

plot(
  infpan_CH,
  plot_rollmean = TRUE
  )
# Plot cases

infpan_CH <- calculate_Rt(
  infpan_CH,
  verbose = TRUE
  )
# Calculate effective reproduction number

summary(infpan_CH)
# Summary of infpan object

timestamps(infpan_CH)
# Time stamps of infpan object

CH_covidwave1 <-
  swash(
    infpan_CH,
    verbose = TRUE
    )
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)

summary(CH_covidwave1)
# Summary of Swash-Backwash Model

Correction of Non-balanced Panel Dataset with Regional Infection Data

Description

This function corrects non-balanced input panel data by replacing missing entries with a user-given constant (e.g., 0).

Usage

as_balanced(
  data, 
  col_cases, 
  col_date, 
  col_region, 
  fill_missing = 0
  )
as_balanced(
  data, 
  col_cases, 
  col_date, 
  col_region, 
  fill_missing = 0
  )

Arguments

data

data.frame with regional infection data

col_cases

Column containing the cases (numeric)

col_date

Column containing the time points (e.g., days)

col_region

Column containing the unique identifier of the regions (e.g., name, NUTS 3 code)

fill_missing

Constant to fill missing values (default and recommended: 0)

Details

The Swash-Backwash Model for the Single Epidemic Wave does not necessarily require balanced panel data in order for the calculations to be carried out. However, for a correct estimation it is implicitly assumed that the input data is balanced. The function corrects non-balanced panel data. It is executed automatically whithin the swash_backwash() function and swash() method, respectively, but can also be used separately.

Value

data

Corrected input dataset (data.frame)

Author(s)

Thomas Wieland

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

COVID19Cases_geoRegion_balanced <- 
  is_balanced(
  data = COVID19Cases_geoRegion,
  col_cases = "entries",
  col_date = "datum",
  col_region = "geoRegion"
)
# Test whether "COVID19Cases_geoRegion" is balanced panel data 

COVID19Cases_geoRegion_balanced$data_balanced
# Balanced? TRUE or FALSE

if (COVID19Cases_geoRegion_balanced$data_balanced == FALSE) {
  COVID19Cases_geoRegion <- 
    as_balanced(
    COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion"
  )
}
# Correction of dataset "COVID19Cases_geoRegion"
# not necessary as parameter balance of is_balanced is set TRUE by default
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

COVID19Cases_geoRegion_balanced <- 
  is_balanced(
  data = COVID19Cases_geoRegion,
  col_cases = "entries",
  col_date = "datum",
  col_region = "geoRegion"
)
# Test whether "COVID19Cases_geoRegion" is balanced panel data 

COVID19Cases_geoRegion_balanced$data_balanced
# Balanced? TRUE or FALSE

if (COVID19Cases_geoRegion_balanced$data_balanced == FALSE) {
  COVID19Cases_geoRegion <- 
    as_balanced(
    COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion"
  )
}
# Correction of dataset "COVID19Cases_geoRegion"
# not necessary as parameter balance of is_balanced is set TRUE by default

Fit metrics of observed and expected binary variables

Description

Calculation of fit metrices for binary variables (Sensitivity, specificity, accuracy)

Usage

binary_metrics(
  observed, 
  expected,
  no_information_rate = "negative"
  )
binary_metrics(
  observed, 
  expected,
  no_information_rate = "negative"
  )

Arguments

observed

Numeric vector: Y observed

expected

Numeric vector: Y expected

no_information_rate

character argument which indicates whether the no-information rate is calculated based on negatives or positives

Details

The function computes model performance metrices for binary outcomes. Observed and expected data must be stated by the user. The function returns sensitivity, specificity, accurracy, and no-information rate.

Value

list with two entries:

fit_metrics:

list with fit metrics (sens, spec, ...)

observed_expected:

data.frame with observed, expected and hit (1/0)

Author(s)

Thomas Wieland

References

Altman DG, Bland JM (1994) Diagnostic tests. 1: Sensitivity and specificity. British Medical Journal 308, 1552. doi:10.1136/bmj.308.6943.1552.

Boehmke B, Greenwell B (2020) Hands-On Machine Learning with R (1 ed.). Taylor & Francis, New York, NY.

Examples

obs <- c(1,1,0,0,0,0,1,0,1)
exp <- c(0,1,0,0,0,0,1,0,0)

binary_metrics(
  obs,
  exp
)
obs <- c(1,1,0,0,0,0,1,0,1)
exp <- c(0,1,0,0,0,0,1,0,0)

binary_metrics(
  obs,
  exp
)

Fit metrics for binary logit model

Description

Calculation of fit metrices for binary variables (Sensitivity, specificity, accuracy) out of binary logit models (glm object)

Usage

binary_metrics_glm(
  logit_model, 
  threshold = 0.5
  )
binary_metrics_glm(
  logit_model, 
  threshold = 0.5
  )

Arguments

logit_model

glm object with binary logit model

threshold

Threshold for distinction of probability with respect to TRUE or FALSE

Details

The function computes model performance metrices for binary outcomes. A binary logit model (glm) must be stated by the user. The function returns sensitivity, specificity, accurracy, and no-information rate.

Value

list with two entries:

fit_metrics:

list with fit metrics (sens, spec, ...)

observed_expected:

data.frame with observed, expected and hit (1/0)

Author(s)

Thomas Wieland

References

Altman DG, Bland JM (1994) Diagnostic tests. 1: Sensitivity and specificity. British Medical Journal 308, 1552. doi:10.1136/bmj.308.6943.1552.

Boehmke B, Greenwell B (2020) Hands-On Machine Learning with R (1 ed.). Taylor & Francis, New York, NY.

Examples

dep <- c(1,1,0,0,0,0,1,0,1, 1)
x <- c(2,3,1,1,0,1,3,2,1,3)

testmodel <-
  glm(
    dep~x,
    family=binomial()
  )
  
summary(testmodel)

binary_metrics_glm(testmodel)
dep <- c(1,1,0,0,0,0,1,0,1, 1)
x <- c(2,3,1,1,0,1,3,2,1,3)

testmodel <-
  glm(
    dep~x,
    family=binomial()
  )
  
summary(testmodel)

binary_metrics_glm(testmodel)

Time Series Model with Breakpoints

Description

Estimation of breakpoints in linear regression models from incremental (e.g., daily) infections data

Usage

breaks_growth(
  y, 
  t,
  ln = FALSE,
  add_constant = 1,
  alpha = 0.05,
  ...,
  verbose = FALSE
  )
breaks_growth(
  y, 
  t,
  ln = FALSE,
  add_constant = 1,
  alpha = 0.05,
  ...,
  verbose = FALSE
  )

Arguments

y

numeric vector with cumulative infections data over time

t

vector of class numeric or Date with time points or dates

ln

bool argument which indicates whether dependent variable should be transformed by natural logarithm

add_constant

Numeric constant to be added to y if zero values occur

alpha

Significance level $\alpha$ for 1- $\alpha$ *100 confidence intervals

...

Other parameters passed to strucchange::breakpoints (see the corresponding documentation)

verbose

bool argument which indicates whether progress messages are displayed

Details

This function allows detecting breakpoints in a linear regression time series model. The user must specify the dependent variable (e.g, daily infections) and the time variable (time counter or date values). The estimation is performed using OLS. The function internally uses the function breakpoints from the strucchange package (Zeileis et al. 2003), where breakpoints are identified using the Bai-Perron algorithm (Bai & Perron 2003).

Value

object of class breaksgrowth-class

Author(s)

Thomas Wieland

References

Bai J, Perron P (2003) Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18(1), 1-22. doi:10.1002/jae.659

Wieland T (2020) A phenomenological approach to assessing the effectiveness of COVID-19 related nonpharmaceutical interventions in Germany. Safety Science 131, 104924. doi:10.1016/j.ssci.2020.104924

Zeileis C, Kleiber W, Krämer K, Hornik K (2003) Testing and dating of structural changes in practice. Computational Statistics & Data Analysis 44(1-2), 109-123. doi:10.1016/S0167-9473(03)00030-6

Examples

data(Infections)
# Confirmed SARS-CoV-2 cases in Germany

breakpoints_infections <- breaks_growth(
  y = Infections$infections_daily,
  t = Infections$day,
  ln = TRUE,
  verbose = TRUE
)
# Breakpoints for time series of infections

summary(breakpoints_infections)
# Summary of breakpoints

plot(breakpoints_infections)
# Plot breakpoints
data(Infections)
# Confirmed SARS-CoV-2 cases in Germany

breakpoints_infections <- breaks_growth(
  y = Infections$infections_daily,
  t = Infections$day,
  ln = TRUE,
  verbose = TRUE
)
# Breakpoints for time series of infections

summary(breakpoints_infections)
# Summary of breakpoints

plot(breakpoints_infections)
# Plot breakpoints

Class `"breaksgrowth"`

Description

The class "breaksgrowth" contains the results of the breaks_growth() function. Use summary(breaksgrowth) for results summary.

Objects from the Class

Objects can be created by the function breaks_growth.

Slots

GrowthModel_OLS:: Object of class list Results of the OLS fit (predicted, parameters)
t:: Object of class numeric Input time points data
y:: Object of class numeric Input infections data
config:: Object of class list Model fit configurations

Methods

summary: signature(object = "breaksgrowth"): Prints a summary of breaksgrowth objects
plot: signature(x = "breaksgrowth"): Plots the results of the breakpoint analysis
print: signature(x = "breaksgrowth"): Prints an breaksgrowth object; use summary(breaksgrowth) for results

Author(s)

Thomas Wieland

References

Bai J, Perron P (2003) Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18(1), 1-22. doi:10.1002/jae.659

Zeileis C, Kleiber W, Krämer K, Hornik, K (2003) Testing and dating of structural changes in practice. Computational Statistics & Data Analysis 44(1-2), 109-123. doi:10.1016/S0167-9473(03)00030-6

Examples

showClass("breaksgrowth")
showClass("breaksgrowth")

Regional cumulative COVID-19 deaths

Description

Cumulative COVID-19 deaths absolute and per 100,000 pop at NUTS3 level for 31 EU/EFTA countries

Usage

data("C19dNUTSdata")data("C19dNUTSdata")

Format

A data frame with 1,143 observations (each one represents a spatial NUTS unit).

NUTS_ID: NUTS ID of the spatial unit
CNTR_CODE: Country code (= NUTS 0 ID) of the given spatial unit
NUTS_Level: NUTS level of the given spatial unit (0 = national, 1, 2, 3)
NUTS2_ID: NUTS 2 ID of the spatial unit
NUTS1_ID: NUTS 1 ID of the spatial unit
NUTS_Name: Latin name of the spatial unit
C19deaths: Cumulative COVID-19 deaths [persons]
pop2020: Population in 2020 [persons]
C19deaths_per100000: Cumulative COVID-19 deaths [per 100,000]
annotation: Annotation

)

Details

Note: This data was originally released in the author's package C19dNUTS in 2022 (https://cran.r-project.org/package=C19dNUTS). Some of the URLs referred to here were moved or deleted.

The dataset contains cumulative COVID-19 deaths at the regional level (mostly NUTS 3, N=1,143) for 31 EU/EFTA countries (AT, BE, BG, CH, CY, CZ, DE, DK, EE, EL, ES, FI, FR, HR, HU, IE, IS, IT, LT, LU, LV, MT, NL, NO, PL, PT, RO, SE, SI, SK, UK). The C19deaths variable contains the absolute number of COVID-19 related deaths, and the variable C19deaths_per100000 equals the death numbers relative to the population (per 100,000).

Unless otherwise noted, data includes all reported COVID-19 related deaths since the beginning of the COVID-19 pandemic through June 2022. Please refer to the source section below for the exact date on which each raw dataset was retrieved. The spatial level is the current NUTS 2021 classification of the European Union (see 'https://ec.europa.eu/eurostat/web/nuts/background'), with one slight modification (see "Technical details" below). The variable NUTS_Level documents the spatial level for which the numbers apply (mostly NUTS_Level = 3 for NUTS3).

Technical details:

This dataset contains cumulative numbers and no time series, as many countries only publish cumulative data on COVID-19 deaths. In cases where countries only publish COVID-19 deaths in the form of daily data, the numbers were summed up over the entire period under consideration at the respective spatial level.

The definition of a COVID-19 death may vary between countries. The respective definition can usually be found on the website of the national health authority. In some countries, data is reported based on different definitions. For example, Lithuania uses three different definitions, namely a) based on the main cause of death in the death certificate, b) based on a mention in the death certificate and c) died within 28 days of a positive SARS-CoV-2 test (https://open-data-sets-ls-osp-sdg.hub.arcgis.com/datasets/ba35de03e111430f88a86f7d1f351de6_0/about). In England, for example, a distinction is made between the deceased who tested positive and those who died from COVID-19 based on the death certificate (https://coronavirus.data.gov.uk/details/deaths). In these cases, the definition used has always been the equivalent of the total number of COVID-19 deaths as reported by the national figures from Johns Hopkins University (https://coronavirus.jhu.edu/data/cumulative-cases).

In some cases, countries publish regional COVID-19 data directly at NUTS3 level (e.g., Germany) or NUTS2 level (e.g., Italy). In most cases, the regional level had to be linked manually using the name of the region (e.g., Bulgaria, Norway, Switzerland). Some countries even publish the relevant data on a smaller scale, i.e. below NUTS3 (e.g., Austria, Netherlands, Poland, England). In these cases, where a reference table (subnational spatial unit <-> NUTS3) was available, the lower level was linked to the NUTS3 level (e.g., England). If no reference table but geodata (shapefiles) for the lower spatial level was available (e.g., Austria, Netherlands, Poland), the lower level was linked to the NUTS3 level via a spatial join (Polygon centroids; in cases where the centroid was outside the polygon, it was placed inside the polygon manually). In these cases, the numbers were then summed up at NUTS3 level.

The spatial reference used here is the current EU NUTS Shapefile (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts; accessed 2022-06-23). The dataset can be linked directly to this shapefile, where the unique id field to which the link can be made is the column NUTS_ID. However, there is one exception: To ensure data compatibility, the UK NUTS3 regions UKM61 and UKM63 were aggregated into one region (UKM61).

The data reflects 1,309,326 COVID-19 related deaths in the 31 countries in the investigated time period. The variable C19deaths_per100000 is non-normally distributed (Shapiro-Wilk test: W = 0.92284, p < 0.01). The natural log of C19deaths_per100000 is spatially autocorrelated (Moran's I with queen contiguous spatial weighting: I = 0.65228, p < 0.01).

Data limitations:

It can be assumed that there are differences between countries and possibly also over time in the definition of a COVID-19 death (see "Technical details" above). Please check the definition on the website of the respective national health authority.

Data on COVID-19 deaths are incomplete for the following EU/EFTA countries: Bulgaria, France, Poland. In Bulgaria, regional COVID-19 deaths were only published for the years 2020 and 2021 (36,142 COVID-19 related deaths in total), i.e. the cases for 2022 are missing. France only publishes the COVID-19 patients which died in a hospital at the regional level, which equals 120,630 COVID-19 related deaths over the period under consideration (as of 2022-06-30). The total number of COVID-19 related deaths in France for the same time is equal to 149,533, which means that there is a lack of 28,903 COVID-19 fatalities (19.3 %, e.g., people which died in nursing homes). Polish deaths are missing COVID-19 deaths from the first pandemic wave. Therefore only the COVID-19 fatalities from the date 2020-11-24 are included, which equals 102,449 deaths. In the previous period, 13,780 COVID-19 deaths were reported, which are not included in the data set, i.e. 11.9% of the deaths are missing.

Of the 31 EU/EFTA countries included, regional data are only available for 24 countries. The following countries have not published sub-national data for COVID-19 deaths: Cyprus, Finland, Island, Hungary, Estonia, Latvia, Malta. The values for Finland, Hungary, Estonia and Latvia refer to the national level (NUTS 0), which is indicated by the variable NUTS_Level = 0. In the cases of Cyprus, Malta and Iceland (which are rather small countries), the NUTS 0 level also corresponds to the NUTS 2 level, which is why they are marked here in the dataset with NUTS_Level = 2. It is comparatively difficult to compare the data with Belgium because COVID-19 death figures are only published there at NUTS 1 level (3 regions; NUTS_Level = 1).

Some countries report separately persons who died of/with COVID-19 who live outside the country or cannot be assigned to a region (e.g., Greece, Norway). These cases are shown separately in the dataset, but cannot be related to population numbers and cannot be linked to the NUTS shapefile.

Norway does not provide COVID-19 data for the NUTS3 regions NO0B1 and NO0B2.

In the UK, each country (England, Wales, Scotland, and Northern Ireland) is independently responsible for publishing COVID-19 data. Therefore the data are not all available at the same spatial aggregation level (e.g. England: NUTS 3, Wales: NUTS 2).

Source

Raw data of COVID-19 deaths:

Note: Some of the URLs have been moved or deleted.

AT: https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline_GKZ.csv (accessed 2022-06-23)

BE: https://epistat.sciensano.be/Data/COVID19BE_MORT.csv (accessed 2022-06-21)

BG: https://www.nsi.bg/sites/default/files/files/data/table/COVID_2020_2021_EN.xls (accessed 2022-06-29)

CH: https://www.covid19.admin.ch/api/data/20220621-t6j901v4/downloads/sources-csv.zip (accessed 2022-06-21)

CY: https://covid19.who.int/region/euro/country/cy (accessed 2022-06-30)

CZ: https://onemocneni-aktualne.mzcr.cz/api/v2/covid-19/umrti.csv (accessed 2022-06-24)

DE: https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/917fc37a709542548cc3be077a786c17_0/about (accessed 2022-06-23)

DK: https://files.ssi.dk/covid19/overvagning/dashboard/overvaagningsdata-dashboard-covid19-28062022-byis (accessed 2022-06-29), folder: "Regionalt_DB", file: "07_antal_doede_pr_dag_pr_region"

EE: https://www.terviseamet.ee/en/coronavirus/coronavirus-dataset (accessed 2022-07-11)

EL: https://github.com/Sandbird/covid19-Greece (accessed 2022-07-02), file "regions"

ES: https://cnecovid.isciii.es/covid19/resources/casos_hosp_uci_def_sexo_edad_provres.csv (accesssed 2022-06-28)

FI: https://covid19.who.int/region/euro/country/fi (accessed 2022-07-01)

FR: https://www.data.gouv.fr/fr/datasets/synthese-des-indicateurs-de-suivi-de-lepidemie-covid-19/ (accessed 2022-07-01), file "table-indicateurs-open-data-dep-2022-06-30-19h00"

HR: https://www.koronavirus.hr/zupanije/139 (accessed 2022-06-28)

HU: https://covid19.who.int/region/euro/country/hu (accessed 2022-07-02)

IE: https://epi-covid-19-hpscireland.hub.arcgis.com/ (accessed 2022-06-29)

IS: https://www.covid.is/data (accessed 2022-06-27)

IT: https://github.com/pcm-dpc/COVID-19/tree/master/dati-regioni (accessed 2022-06-24), file "dpc-covid19-ita-regioni-latest_raw"

LV: https://covid19.gov.lv/en/node/16387 (accessed 2022-07-27)

LT: https://open-data-ls-osp-sdg.hub.arcgis.com/datasets/ba35de03e111430f88a86f7d1f351de6_0/explore (accessed 2022-06-27)

LU: https://covid19.public.lu/fr/graph.html (accessed 2022-06-27)

MT: https://covid19.who.int/table (accessed 2022-07-01)

NL: https://data.rivm.nl/covid-19/COVID-19_aantallen_gemeente_per_dag.csv (accessed 2022-06-27)

NO: https://www.fhi.no/contentassets/8a971e7b0a3c4a06bdbf381ab52e6157/vedlegg/2022/ukerapport-uke-20-16.05—22.05.22.pdf (accessed 2022-07-07)

PL: https://www.gov.pl/web/koronawirus/wykaz-zarazen-koronawirusem-sars-cov-2 (accessed 2022-06-23)

PT: https://github.com/dssg-pt/covid19pt-data/blob/master/data.csv (accessed 2022-06-29)

RO: https://covid19.geo-spatial.org/?map=decese (accessed 2022-07-01)

SE: https://experience.arcgis.com/experience/19fc7e3f61ec4e86af178fe2275029c5 (accessed 2022-06-23)

SI: https://www.nijz.si/sites/www.nijz.si/files/uploaded/tedenski_prikaz_umrli20220627.xlsx (accessed 2022-06-28)

SK: https://github.com/Institut-Zdravotnych-Analyz/covid19-data (accessed 2022-06-28), folder "Deaths", file "OpenData_Slovakia_Covid_Deaths_AgeGroup_District"

UK - England: https://coronavirus.data.gov.uk/details/deaths (accessed 2022-06-24), file "ltla_2022_06_23_cumDeaths60DaysByDeathDate_ref"

UK - Northern Ireland: https://www.nisra.gov.uk/system/files/statistics/Weekly_Deaths%20-%20w%20e%2017th%20June%202022.XLSX (accessed 2022-07-01)

UK - Scotland: https://www.nrscotland.gov.uk/files//statistics/covid19/covid-deaths-22-data-week-25.xlsx (data for 2021-2022) and https://www.nrscotland.gov.uk/files//statistics/covid19/covid-deaths-20-data-final.xlsx (data for 2022) (accessed 2022-07-01)

UK - Wales: https://public.tableau.com/app/profile/public.health.wales.health.protection/viz/COVID-19Rapidmortalitydata/Summary (accessed 2022-07-04)

Population data:

https://ec.europa.eu/eurostat/databrowser/view/DEMO_R_PJANGRP3/default/table?lang=en&category=reg.reg_dem.reg_dempoar (accessed 2022-06-22)

Examples

data(C19dNUTSdata)

# Summary:
summary(C19dNUTSdata)

# Check for normal distribution:
hist(C19dNUTSdata$C19deaths_per100000)
shapiro.test(C19dNUTSdata$C19deaths_per100000)

# no. of regions for each country:
table(C19dNUTSdata$CNTR_CODE)
# only for countries with data on at least NUTS 2 level:
table(C19dNUTSdata[C19dNUTSdata$NUTS_Level > 1,]$CNTR_CODE)

data(C19dNUTSdata)

# Summary:
summary(C19dNUTSdata)

# Check for normal distribution:
hist(C19dNUTSdata$C19deaths_per100000)
shapiro.test(C19dNUTSdata$C19deaths_per100000)

# no. of regions for each country:
table(C19dNUTSdata$CNTR_CODE)
# only for countries with data on at least NUTS 2 level:
table(C19dNUTSdata[C19dNUTSdata$NUTS_Level > 1,]$CNTR_CODE)

Cumulative Infection Numbers

Description

Calculation of the cumulative values of infection numbers for infections panel data.

Usage

calculate_cum(
  object,
  col_name = NULL,
  overwrite = FALSE,
  verbose = FALSE
  )
calculate_cum(
  object,
  col_name = NULL,
  overwrite = FALSE,
  verbose = FALSE
  )

Arguments

object

object of class infpan

col_name

character value specifying the column name of the computed cumulative values

overwrite

bool argument which indicates whether the column should be overwritten if already existing

verbose

bool argument which indicates whether progress messages are displayed

Details

Calculates the cumulative values of the infections panel data for all time points for each region. If col_name is NULL, the column is defined as "<Column name of cases>_cum". Set overwrite to TRUE, if an existing column should be overwritten. The method uses the function cumsum from the base package (see the corresponding documentation).

Value

infpan object including column with cumulative values in the infections panel data

Author(s)

Thomas Wieland

References

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

infpan_CH <- calculate_cum(
  infpan_CH, 
  col_name = "cumulatives",
  verbose = TRUE
)
# Calculate rolling mean of cases as "cumulatives"

summary(infpan_CH)
# Summary of infpan object
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

infpan_CH <- calculate_cum(
  infpan_CH, 
  col_name = "cumulatives",
  verbose = TRUE
)
# Calculate rolling mean of cases as "cumulatives"

summary(infpan_CH)
# Summary of infpan object

Methods for Function `calculate_cum`

Description

Methods for function calculate_cum

Methods

signature( object = "infpan", col_name = NULL, overwrite = FALSE, verbose = FALSE): Calculates the cumulative values of the infections panel data for all time points for each region. If col_name is NULL, the column is defined as "<Column name of cases>_cum". The method uses the function cumsum from the base package (see the corresponding documentation).

Author(s)

Thomas Wieland

Incidence from Infection Numbers

Description

Calculation of the incidence from infection numbers and population for infections panel data.

Usage

calculate_incidence(
  object,
  use_column = NULL,
  col_name = NULL,
  pop_factor = 100000,
  overwrite = FALSE,
  verbose = FALSE
  )
calculate_incidence(
  object,
  use_column = NULL,
  col_name = NULL,
  pop_factor = 100000,
  overwrite = FALSE,
  verbose = FALSE
  )

Arguments

object

object of class infpan

use_column

character value specifying which column should be used for incidence calculation

col_name

character value specifying the column name of the computed incidence

pop_factor

numeric value specifying the factor with which the incidence should be multiplied (e.g., cases/pop*100000)

overwrite

bool argument which indicates whether the column should be overwritten if already existing

verbose

bool argument which indicates whether progress messages are displayed

Details

Calculates the incidence of the infections panel data for all time points for each region. Use use_column to specify which column should be used for the calculation of incidence. The following values are permitted: "Cases" (default, incremental cases), "Cum. cases" (cumulative cases), "Roll. mean" (rolling mean of cases), or "Roll. sum" (rolling sum of cases). If the specified column does not exist in the infections panel data of the infpan object, the function raises an error. If in the infpan object, no "Population" column is defined, incidence calculation is not possible. If col_name is NULL, the column is defined as "<Column name of cases>_inc". Set overwrite to TRUE, if an existing column should be overwritten.

Value

infpan object including column with incidence values in the infections panel data

Author(s)

Thomas Wieland

References

an der Heiden M, Hamouda O (2020) Schätzung der aktuellen Entwicklung der SARS-CoV-2-Epidemie in Deutschland - Nowcasting. Epidemiologisches Bulletin 17, 10-15. doi:10.25646/6692

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

infpan_CH <- calculate_incidence(
  infpan_CH, 
  col_name = "incidence",
  verbose = TRUE
)
# Calculate incidence of cases as "incidence"

summary(infpan_CH)
# Summary of infpan object
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

infpan_CH <- calculate_incidence(
  infpan_CH, 
  col_name = "incidence",
  verbose = TRUE
)
# Calculate incidence of cases as "incidence"

summary(infpan_CH)
# Summary of infpan object

Methods for Function `calculate_incidence`

Description

Methods for function calculate_incidence

Methods

signature( object, use_column = "Cases", col_name = NULL, pop_factor = 100000, overwrite = FALSE, verbose = FALSE): Calculates the incidence of the infections panel data for all time points for each region. Use use_column to specify which column should be used for the calculation of incidence. The following values are permitted: "Cases" (default, incremental cases), "Cum. cases" (cumulative cases), "Roll. mean" (rolling mean of cases), or "Roll. sum" (rolling sum of cases). If the specified column does not exist in the infections panel data of the infpan object, the function raises an error. If in the infpan object, no "Population" column is defined, incidence calculation is not possible. If col_name is NULL, the column is defined as "<Column name of cases>_inc". Set overwrite to TRUE, if an existing column should be overwritten.

Author(s)

Thomas Wieland

Rolling Means of Infection Numbers

Description

Calculation of the rolling means of infection numbers for infections panel data.

Usage

calculate_rollmean(
  object,
  k = 7,
  align = "center",
  fill = NA,
  col_name = NULL,
  overwrite = FALSE,
  verbose = FALSE
  )
calculate_rollmean(
  object,
  k = 7,
  align = "center",
  fill = NA,
  col_name = NULL,
  overwrite = FALSE,
  verbose = FALSE
  )

Arguments

object

object of class infpan

k

integer width of the rolling window (default: 7)

align

character specifying whether the rolling mean should be left- or right-aligned or centered (default) compared to the rolling window (default: center)

fill

numeric value or NA for the filling value at the left/within/right end of the data range

col_name

character value specifying the column name of the computed rolling means

overwrite

bool argument which indicates whether the column should be overwritten if already existing

verbose

bool argument which indicates whether progress messages are displayed

Details

Calculates the rolling mean of the infections panel data for all time points for each region. Set the rolling window by the parameter k (default: 7). Set the fill value for the observations left/within/right to the data range with parameter fill (default: NA). Parameter align defines whether the index of the result should be left- or right-aligned or centered (default). If col_name is NULL, the column is defined as "<Column name of cases>_rm". Set overwrite to TRUE, if an existing column should be overwritten. The method uses the function rollmean from the zoo package (see the corresponding documentation).

Value

infpan object including column with rolling means in the infections panel data

Author(s)

Thomas Wieland

References

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

infpan_CH <- calculate_rollmean(
  infpan_CH, 
  col_name = "RollingMean",
  verbose = TRUE
)
# Calculate rolling mean of cases as "RollingMean"

summary(infpan_CH)
# Summary of infpan object
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

infpan_CH <- calculate_rollmean(
  infpan_CH, 
  col_name = "RollingMean",
  verbose = TRUE
)
# Calculate rolling mean of cases as "RollingMean"

summary(infpan_CH)
# Summary of infpan object

Methods for Function `calculate_rollmean`

Description

Methods for function calculate_rollmean

Methods

signature( object = "infpan", k = 7, align = "center", fill = NA, col_name = NULL, verbose = FALSE): Calculates the rolling mean of the infections panel data for all time points for each region. Set the rolling window by the parameter k (default: 7). Set the fill value for the observations left/within/right to the data range with parameter fill (default: NA). Parameter align defines whether the index of the result should be left- or right-aligned or centered (default). If col_name is NULL, the column is defined as "<Column name of cases>_rm". The method uses the function rollmean from the zoo package (see the corresponding documentation).

Author(s)

Thomas Wieland

Rolling Sums of Infection Numbers

Description

Calculation of the rolling sums of infection numbers for infections panel data.

Usage

calculate_rollsum(
  object,
  k = 7,
  align = "center",
  fill = NA,
  col_name = NULL,
  overwrite = FALSE,
  verbose = FALSE
  )
calculate_rollsum(
  object,
  k = 7,
  align = "center",
  fill = NA,
  col_name = NULL,
  overwrite = FALSE,
  verbose = FALSE
  )

Arguments

object

object of class infpan

k

integer width of the rolling window (default: 7)

align

character specifying whether the rolling mean should be left- or right-aligned or centered (default) compared to the rolling window (default: center)

fill

numeric value or NA for the filling value at the left/within/right end of the data range

col_name

character value specifying the column name of the computed rolling sums

overwrite

bool argument which indicates whether the column should be overwritten if already existing

verbose

bool argument which indicates whether progress messages are displayed

Details

Calculates the rolling sum of the infections panel data for all time points for each region. Set the rolling window by the parameter k (default: 7). Set the fill value for the observations left/within/right to the data range with parameter fill (default: NA). Parameter align defines whether the index of the result should be left- or right-aligned or centered (default). If col_name is NULL, the column is defined as "<Column name of cases>_rs". Set overwrite to TRUE, if an existing column should be overwritten. The method uses the function rollsum from the zoo package (see the corresponding documentation).

Value

infpan object including column with rolling sums in the infections panel data

Author(s)

Thomas Wieland

References

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

infpan_CH <- calculate_rollsum(
  infpan_CH, 
  col_name = "RollingMSum",
  verbose = TRUE
)
# Calculate rolling sum of cases as "RollingSum"

summary(infpan_CH)
# Summary of infpan object
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

infpan_CH <- calculate_rollsum(
  infpan_CH, 
  col_name = "RollingMSum",
  verbose = TRUE
)
# Calculate rolling sum of cases as "RollingSum"

summary(infpan_CH)
# Summary of infpan object

Methods for Function `calculate_rollsum`

Description

Methods for function calculate_rollsum

Methods

signature( object = "infpan", k = 7, align = "center", fill = NA, col_name = NULL, verbose = FALSE): Calculates the rolling sum of the infections panel data for all time points for each region. Set the rolling window by the parameter k (default: 7). Set the fill value for the observations left/within/right to the data range with parameter fill (default: NA). Parameter align defines whether the index of the result should be left- or right-aligned or centered (default). If col_name is NULL, the column is defined as "<Column name of cases>_rs". The method uses the function rollsum from the zoo package (see the corresponding documentation).

Author(s)

Thomas Wieland

Effective Reproduction Number

Description

Calculation of the effective reproduction number for infections panel data.

Usage

calculate_Rt(
  object,
  GP = 4,
  correction = FALSE,
  col_name = NULL,
  overwrite = FALSE,
  verbose = FALSE
  )
calculate_Rt(
  object,
  GP = 4,
  correction = FALSE,
  col_name = NULL,
  overwrite = FALSE,
  verbose = FALSE
  )

Arguments

object

object of class infpan

GP

Generation period, in time units (typically days)

correction

Correction of values equal to zero? (Recommended)

col_name

character value specifying the column name of the computed rolling means

overwrite

bool argument which indicates whether the column should be overwritten if already existing

verbose

bool argument which indicates whether progress messages are displayed

Details

Calculates the effective reproduction number $R_t$ for all time points for each region in the infections panel data. Set the generation period by the parameter GP (default: 4). If correction is TRUE, values equal to zero are increased by one. The method uses the built-in function R_t().

Value

infpan object including $R_t$ column in the infections panel data

Author(s)

Thomas Wieland

References

an der Heiden M, Hamouda O (2020) Schätzung der aktuellen Entwicklung der SARS-CoV-2-Epidemie in Deutschland - Nowcasting. Epidemiologisches Bulletin 17, 10-15. doi:10.25646/6692

Bonifazi G, Lista L, Menasce D, Mezzetto M, Pedrini D, Spighi R, Zoccoli A (2021) A simplified estimate of the effective reproduction number Rt using its relation with the doubling time and application to Italian COVID-19 data. The European Physical Journal Plus 136, 386. doi:10.1140/epjp/s13360-021-01339-6

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

infpan_CH <- calculate_Rt(
  infpan_CH,
  verbose = TRUE
  )
# Calculate effective reproduction number

summary(infpan_CH)
# Summary of infpan object
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

infpan_CH <- calculate_Rt(
  infpan_CH,
  verbose = TRUE
  )
# Calculate effective reproduction number

summary(infpan_CH)
# Summary of infpan object

Methods for Function `calculate_Rt`

Description

Methods for function calculate_Rt

Methods

signature( object = "infpan", GP = 4, correction = FALSE, col_name = NULL, verbose = FALSE): Calculates the effective reproduction number $R_t$ for all time points for each region in the infections panel data. Set the generation period by the parameter GP (default: 4). If correction is TRUE, values equal to zero are increased by one. Set overwrite to TRUE, if an existing column should be overwritten. The method uses the built-in function R_t().

Author(s)

Thomas Wieland

Two-country Comparison of Swash-Backwash Model Parameters

Description

This function enables bootstrap estimates for the mean difference of Swash-Backwash Model parameters of two countries to be compared.

Usage

compare_countries(
  sbm1, 
  sbm2, 
  country_names = c("Country 1", "Country 2"), 
  indicator = "R_0A", 
  iterations = 20, 
  samples_ratio = 0.8, 
  alpha = 0.05, 
  replace = TRUE
  )
compare_countries(
  sbm1, 
  sbm2, 
  country_names = c("Country 1", "Country 2"), 
  indicator = "R_0A", 
  iterations = 20, 
  samples_ratio = 0.8, 
  alpha = 0.05, 
  replace = TRUE
  )

Arguments

sbm1

A sbm object for country 1

sbm2

A sbm object for country 2

country_names

list with user-given country names (two entries)

indicator

character, indicator to be analyzed ("S_A", "I_A", "R_A", "t_LE", "t_LE", or "R_0A" (default and recommended: "R_0A"))

iterations

Number of iterations for resampling (default: 100)

samples_ratio

Proportion of regions included in each sample (default: 0.8)

alpha

Significance level $\alpha$ for 1- $\alpha$ *100 confidence intervals (default: 0.05)

replace

Resampling with replacement (TRUE or FALSE, default: TRUE = bootstrap resampling)

Details

The combination of the Swash-Backwash Model and bootstrap resampling allows the estimation of mean differences of a user-specified model parameter (e.g., spatial reproduction number $R_{OA}$ ) between two countries. This makes it possible to check whether the spatial spread velocity of a communicable disease is significantly different in one country than in another country. Since the initial data in the Swash-Backwash Model should be balanced, entity-based bootstrap sampling is carried out in the compare_countries() function. This means that not, for example, 80% of all observations are included in each sample at a sample ratio equal to $p$ = 0.8, but rather all observations for 80% of the regions. For both countries, $B$ bootstrap samples (default: 100) are drawn for which the Swash-Backwash Model is calculated. Based on the distribution of indicators, confidence intervals are calculated at the user-specified significance level $\alpha$ . The compare_countries() function calculates the differences of the user's desired indicator between the two samples, $D$ , and also calculates $\alpha$ confidence intervals for this.

Value

object of class countries, see countries-class

Author(s)

Thomas Wieland

References

Cliff AD, Haggett P (2006) A swash-backwash model of the single epidemic wave. Journal of Geographical Systems 8(3), 227-252. doi:10.1007/s10109-006-0027-8

Efron B, Tibshirani RJ (1993) An Introduction to the Bootstrap.

Ramachandran KM, Tsokos CP (2021) Mathematical Statistics with Applications in R (Third Edition). Ch. 13.3.1 (Bootstrap confidence intervals). doi:10.1016/B978-0-12-817815-7.00013-0

Smallman-Raynor MR, Cliff AD, Stickler PJ (2022) Meningococcal Meningitis and Coal Mining in Provincial England: Geographical Perspectives on a Major Epidemic, 1929–33. Geographical Analysis 54, 197–216. doi:10.1111/gean.12272

Smallman-Raynor MR, Cliff AD, The COVID-19 Genomics UK (COG-UK) Consortium (2022) Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020–December 2021. Epidemiology and Infection 150, e145. doi:10.1017/S0950268822001285.

Examples

data(COVID19Cases_geoRegion)
# Get Swiss COVID19 cases at NUTS 3 level

data(Oesterreich_Faelle)
# Get Austrian COVID19 cases at NUTS 3 level
# (first wave, same final date as in Swiss data: 2020-05-31)

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

CH_covidwave1 <- 
  swash_backwash(
    data = COVID19Cases_geoRegion, 
    col_cases = "entries", 
    col_date = "datum", 
    col_region = "geoRegion"
    )
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)

AT_covidwave1 <- 
  swash_backwash(
    data = Oesterreich_Faelle,
    col_cases = "Faelle",
    col_date = "Datum",
    col_region = "NUTS3"
  )
# Swash-Backwash Model for Austrian COVID19 cases
# Spatial aggregate: NUTS 3

AT_vs_CH <- 
  compare_countries(
    CH_covidwave1, 
    AT_covidwave1,
    country_names = c("Switzerland", "Austria"))
# Country comparison Switzerland vs. Austria
# default config: 20 iterations, alpha = 0.05, sample ratio = 80%,
# indicator: R_0A

summary(AT_vs_CH)
# Summary of country comparison

plot(AT_vs_CH)
# Plot of country comparison
data(COVID19Cases_geoRegion)
# Get Swiss COVID19 cases at NUTS 3 level

data(Oesterreich_Faelle)
# Get Austrian COVID19 cases at NUTS 3 level
# (first wave, same final date as in Swiss data: 2020-05-31)

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

CH_covidwave1 <- 
  swash_backwash(
    data = COVID19Cases_geoRegion, 
    col_cases = "entries", 
    col_date = "datum", 
    col_region = "geoRegion"
    )
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)

AT_covidwave1 <- 
  swash_backwash(
    data = Oesterreich_Faelle,
    col_cases = "Faelle",
    col_date = "Datum",
    col_region = "NUTS3"
  )
# Swash-Backwash Model for Austrian COVID19 cases
# Spatial aggregate: NUTS 3

AT_vs_CH <- 
  compare_countries(
    CH_covidwave1, 
    AT_covidwave1,
    country_names = c("Switzerland", "Austria"))
# Country comparison Switzerland vs. Austria
# default config: 20 iterations, alpha = 0.05, sample ratio = 80%,
# indicator: R_0A

summary(AT_vs_CH)
# Summary of country comparison

plot(AT_vs_CH)
# Plot of country comparison

Methods for Function `confint`

Description

Methods for function confint

Methods

signature(object = "sbm", iterations = 100, samples_ratio = 0.8, alpha = 0.05, replace = TRUE): Creates bootstrap confidence intervals for sbm objects. The argument iterations indicates the number of bootstrap samples which are drawn. Since the initial data in the Swash-Backwash Model should be balanced, entity-based bootstrap sampling is carried out. This means that not, for example, 80% of all observations are included in each sample at a sample ratio equal to $p$ = 0.8 (samples_ratio = 0.8), but rather all observations for 80% of the regions. The significance level for the confidence intervals $\alpha$ is set by the argument alpha (default: 0.05, which corresponds to a 95% confidence level).

Author(s)

Thomas Wieland

References

Cliff AD, Haggett P (2006) A swash-backwash model of the single epidemic wave. Journal of Geographical Systems 8(3), 227-252. doi:10.1007/s10109-006-0027-8

Efron B, Tibshirani RJ (1993) An Introduction to the Bootstrap.

Ramachandran KM, Tsokos CP (2021) Mathematical Statistics with Applications in R (Third Edition). Ch. 13.3.1 (Bootstrap confidence intervals). doi:10.1016/B978-0-12-817815-7.00013-0

Class `"countries"`

Description

The class countries contains the results of a two-country comparison analysis using the Swash-Backwash Model, including two sbm_ci objects for each country. Use summary(countries) and plot(countries) for results summary and plotting, respectively.

Objects from the Class

Objects can be created by the function compare_countries(sbm1, sbm2).

Slots

sbm_ci1:: Object of class "sbm_ci" Results of "confint(sbm1)" for country 1
sbm_ci2:: Object of class "sbm_ci" Results of "confint(sbm1)" for country 2
D:: Object of class "numeric" Results: Difference $D$ between the samples with respect to the chosen indicator
D_ci:: Object of class "numeric" Results: $\alpha$ confidence intervals of $D$
config:: Object of class "list" Configuration details for bootstrap sampling
country_names:: Object of class "character" User-stated country names
indicator:: Object of class "character" User-stated indicator to be tested

Methods

plot: signature(x = "countries"): Plots the results of a two-country comparison with the Swash-Backwash Model
show: signature(object = "countries"): Prints an countries object; use summary(sbm_ci) for results
print: signature(object = "countries"): Prints an countries object; use summary(sbm_ci) for results
summary: signature(object = "countries"): Prints a summary of a countries object (results of the two-country comparison)

Author(s)

Thomas Wieland

Examples

showClass("countries")
showClass("countries")

Switzerland Daily COVID-19 cases by region

Description

A dataset containing COVID-19 cases by region (NUTS 3 = cantons) and time periods (days) for Switzerland (Source: Federal Office of Public Health FOPH).

Usage

data(COVID19Cases_geoRegion)
data(COVID19Cases_geoRegion)

Format

A data.frame with multiple columns:

geoRegion: (character) Region for which the data was collected.
datum: (Date) Date of record.
entries: (integer) Number of reported cases on this date.
sumTotal: (integer) Cumulative case numbers.
timeframe_14d: (logical) Indicates whether the time period covers the last 14 days.
timeframe_all: (logical) Indicates whether the time period covers all previous data.
offset_last7d: (integer) Offset of the last 7 days.
sumTotal_last7d: (integer) Cumulative case numbers of the last 7 days.
offset_last14d: (integer) Offset of the last 14 days.
sumTotal_last14d: (integer) Cumulative case numbers of the last 14 days.
offset_last28d: (integer) Offset of the last 28 days.
sumTotal_last28d: (integer) Cumulative case numbers of the last 28 days.
sum7d: (numeric) Sum of the last 7 days.
sum14d: (numeric) Sum of the last 14 days.
mean7d: (numeric) Average of the last 7 days.
mean14d: (numeric) Average of the last 14 days.
entries_diff_last_age: (integer) Difference from the last age group.
pop: (integer) Population of the region.
inz_entries: (numeric) Incidence of the entries.
inzsumTotal: (numeric) Incidence of cumulative cases.
inzmean7d: (numeric) Incidence of the 7-day average.
inzmean14d: (numeric) Incidence of the 14-day average.
inzsumTotal_last7d: (numeric) Incidence of cumulative cases in the last 7 days.
inzsumTotal_last14d: (numeric) Incidence of cumulative cases in the last 14 days.
inzsumTotal_last28d: (numeric) Incidence of cumulative cases in the last 28 days.
inzsum7d: (numeric) Incidence of the last 7 days.
inzsum14d: (numeric) Incidence of the last 14 days.
sumdelta7d: (numeric) Difference in sums of the last 7 days.
inzdelta7d: (numeric) Difference in incidence of the last 7 days.
type: (character) Type of recorded data (e.g., COVID-19 cases).
type_variant: (character) Variant of the data type.
version: (character) Version of the data collection.
datum_unit: (character) Unit of date specification (e.g., day).
entries_letzter_stand: (integer) Last known count of entries.
entries_neu_gemeldet: (integer) Newly reported entries.
entries_diff_last: (integer) Difference in last entries.

Details

The data is included as it was published in by the Swiss Federal Office of Public Health (Bundesamt fuer Gesundheit, BAG). Note that the reporting date equals the date of SARS-CoV-2 testing.

Source

Federal Office of Public Health - FOPH (2023) COVID-19 Dashboard Source Data. https://www.covid19.admin.ch/api/data/documentation (retrieved 2023-06-28)

References

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

COVID19Cases_geoRegion_balanced <- 
  is_balanced(
  data = COVID19Cases_geoRegion,
  col_cases = "entries",
  col_date = "datum",
  col_region = "geoRegion"
)
# Test whether "COVID19Cases_geoRegion" is balanced panel data 

COVID19Cases_geoRegion_balanced$data_balanced
# Balanced? TRUE or FALSE
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

COVID19Cases_geoRegion_balanced <- 
  is_balanced(
  data = COVID19Cases_geoRegion,
  col_cases = "entries",
  col_date = "datum",
  col_region = "geoRegion"
)
# Test whether "COVID19Cases_geoRegion" is balanced panel data 

COVID19Cases_geoRegion_balanced$data_balanced
# Balanced? TRUE or FALSE

Results from a Difference-in-Differences Model

Description

Example data frame with results from a difference-in-differences model

Usage

data(did_fatalities_splm_coef)
data(did_fatalities_splm_coef)

Format

A data.frame with multiple columns:

Var: Coefficient name
Estimate: Coef. estimate
Std_Error_Bonferroni: Coef. standard error
t_value_Bonferroni: Coef. t value
Pr_t_Bonferroni: Coef. p value
CI_lower_Bonferroni: Coef. lower confidence interval
CI_upper_Bonferroni: Coef. upper confidence interval

Details

Data frame with results from a difference-in-differences model (SPLM model), example data

Source

Examples

data(did_fatalities_splm_coef)
# Results of a difference-in-differences model

plot_coef_ci(
  point_estimates = did_fatalities_splm_coef$Estimate,
  confint_lower = did_fatalities_splm_coef$CI_lower_Bonferroni,
  confint_upper = did_fatalities_splm_coef$CI_upper_Bonferroni,
  coef_names = did_fatalities_splm_coef$Var,
  skipvars = c(
    "Alpha_share", 
    "lambda",
    "rho",
    "log(D_Infections_daily_7dsum_per100000_lag2weeks)",
    "vacc_cum_per100000_lag2weeks"
    ),
  lwd = 13,
  pch = 19,
  auto_color = TRUE
)
# Plot with point estimates and confidence intervals
data(did_fatalities_splm_coef)
# Results of a difference-in-differences model

plot_coef_ci(
  point_estimates = did_fatalities_splm_coef$Estimate,
  confint_lower = did_fatalities_splm_coef$CI_lower_Bonferroni,
  confint_upper = did_fatalities_splm_coef$CI_upper_Bonferroni,
  coef_names = did_fatalities_splm_coef$Var,
  skipvars = c(
    "Alpha_share", 
    "lambda",
    "rho",
    "log(D_Infections_daily_7dsum_per100000_lag2weeks)",
    "vacc_cum_per100000_lag2weeks"
    ),
  lwd = 13,
  pch = 19,
  auto_color = TRUE
)
# Plot with point estimates and confidence intervals

Class `"expgrowth"`

Description

The class "expgrowth" contains the results of the exponential_growth() function. Use summary(expgrowth) for results summary.

Objects from the Class

Objects can be created by the function exponential_growth.

Slots

GrowthModel_OLS:: Object of class list Results of the OLS fit (predicted, parameters)
GrowthModel_NLS:: Object of class list Results of the NLS fit (predicted, parameters)
t:: Object of class numeric Input time points data
y:: Object of class numeric Input infections data
config:: Object of class list Model fit configurations

Methods

summary: signature(object = "expgrowth"): Prints a summary of expgrowth objects
plot: signature(x = "expgrowth"): Plots the results of the exponential growth model (observed, predicted)
print: signature(x = "expgrowth"): Prints an expgrowth object; use summary(expgrowth) for results

Author(s)

Thomas Wieland

References

Pell B, Kuang Y, Viboud C, Chowell G (2018) Using phenomenological models for forecasting the 2015 ebola challenge. Epidemics 22, 62–70. doi:10.1016/j.epidem.2016.11.002

Examples

showClass("expgrowth")
showClass("expgrowth")

Exponential Growth Model for Epidemic Data

Description

Estimation of exponential growth models from daily infections data

Usage

exponential_growth(
  y, 
  t, 
  GI = 4,
  nls = TRUE,
  nls_start = list(a = 1, b = 0.1),
  add_constant = 1,
  verbose = FALSE
  )
exponential_growth(
  y, 
  t, 
  GI = 4,
  nls = TRUE,
  nls_start = list(a = 1, b = 0.1),
  add_constant = 1,
  verbose = FALSE
  )

Arguments

y

numeric vector with cumulative infections data over time

t

vector of class numeric or Date with time points or dates

GI

Generation interval for computing $R_0$

nls

Nonlinear estimation? TRUE or FALSE

nls_start

A list with start values for the two parameters to be estimated

add_constant

Numeric constant to be added to y if zero values occur (only relevant for OLS estimation)

verbose

bool argument which indicates whether progress messages are displayed

Details

This function allows the estimation of an exponential growth model. The user must specify the dependent variable (e.g., daily infections) and the time variable (time counter or date values). The estimation is performed using a linearized model as an OLS estimator, and, if nls=TRUE, also by NLS. The results are the exponential growth rate $r$ , basic reproduction number $R_0$ , and the doubling rate.

Value

object of class expgrowth-class

Author(s)

Thomas Wieland

References

Pell B, Kuang Y, Viboud C, Chowell G (2018) Using phenomenological models for forecasting the 2015 ebola challenge. Epidemics 22, 62–70. doi:10.1016/j.epidem.2016.11.002

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_ZH <-
  COVID19Cases_geoRegion[
  (COVID19Cases_geoRegion$geoRegion == "ZH")
  & (COVID19Cases_geoRegion$sumTotal > 0)
  ,]
# COVID cases for Zurich

expgrowth_ZH <- exponential_growth(
  y = COVID19Cases_ZH$sumTotal[1:28], 
  t = COVID19Cases_ZH$datum[1:28] 
)
# Exponential growth model for the first 4 weeks

summary(expgrowth_ZH)
# Summary of exponential growth model

plot(expgrowth_ZH)
# Plot of exponential growth model

expgrowth_ZH@GrowthModel_OLS$fit_metrics
expgrowth_ZH@GrowthModel_OLS$fit_metrics
# Fit metrics for OLS and NLS models
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_ZH <-
  COVID19Cases_geoRegion[
  (COVID19Cases_geoRegion$geoRegion == "ZH")
  & (COVID19Cases_geoRegion$sumTotal > 0)
  ,]
# COVID cases for Zurich

expgrowth_ZH <- exponential_growth(
  y = COVID19Cases_ZH$sumTotal[1:28], 
  t = COVID19Cases_ZH$datum[1:28] 
)
# Exponential growth model for the first 4 weeks

summary(expgrowth_ZH)
# Summary of exponential growth model

plot(expgrowth_ZH)
# Plot of exponential growth model

expgrowth_ZH@GrowthModel_OLS$fit_metrics
expgrowth_ZH@GrowthModel_OLS$fit_metrics
# Fit metrics for OLS and NLS models

Logistic Growth Models for Regional Infections

Description

Estimates $N$ logistic growth models for $N$ regions.

Usage

growth(
  object, 
  S_iterations = 10, 
  S_start_est_method = "bisect", 
  seq_by = 10, 
  nls = TRUE,
  add_constant = 1,
  overwrite = FALSE,
  verbose = FALSE
  )
growth(
  object, 
  S_iterations = 10, 
  S_start_est_method = "bisect", 
  seq_by = 10, 
  nls = TRUE,
  add_constant = 1,
  overwrite = FALSE,
  verbose = FALSE
  )

Arguments

object

object of class infpan

S_iterations

Number of iterations for saturation value search

S_start_est_method

Method for saturation value search, either "bisect" or "trial_and_error"

seq_by

No. of segments for the "trial_and_error" estimation of the saturation value

nls

Nonlinear estimation? TRUE or FALSE

add_constant

Numeric constant to be added to y if zero values occur (only relevant for OLS estimation)

overwrite

bool argument which indicates whether the column containing cumulative cases should be overwritten if already existing

verbose

bool argument which indicates whether progress messages are displayed

Details

The function estimates logistic growth models for regional infections based on a infpan object. See logistic_growth for further details.

Value

object of class growthmodels-class

Author(s)

Thomas Wieland

References

Pell B, Kuang Y, Viboud C, Chowell G (2018) Using phenomenological models for forecasting the 2015 ebola challenge. Epidemics 22, 62–70. doi:10.1016/j.epidem.2016.11.002

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1_growth <- 
  growth(infpan_CH)
summary(CH_covidwave1_growth)
# Logistic growth models for infpan object infpan_CH
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1_growth <- 
  growth(infpan_CH)
summary(CH_covidwave1_growth)
# Logistic growth models for infpan object infpan_CH

Time Series Model with Breakpoints for Regional Infections

Description

Conducts $N$ breakpoints analyses for infection time series in $N$ regions.

Usage

growth_breaks(
  object,
  ln = FALSE,
  add_constant = 1,
  alpha = 0.05,
  verbose = FALSE
  )
growth_breaks(
  object,
  ln = FALSE,
  add_constant = 1,
  alpha = 0.05,
  verbose = FALSE
  )

Arguments

object

object of class infpan

ln

bool argument which indicates whether the dependent variable should be transformed by natural logarithm

add_constant

Numeric constant to be added to y if zero values occur

alpha

Significance level $\alpha$ for 1- $\alpha$ *100 confidence intervals

verbose

bool argument which indicates whether progress messages are displayed

Details

The method detects breakpoints in regional infections time series based on an infpan object. The function internally uses the function breakpoints from the strucchange package (Zeileis et al. 2003), where breakpoints are identified using the Bai-Perron algorithm (Bai & Perron 2003). See breaks_growth for further details of the estimation.

Value

object of class growthmodels-class

Author(s)

Thomas Wieland

References

Bai J, Perron P (2003) Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18(1), 1-22. doi:10.1002/jae.659

Zeileis C, Kleiber W, Krämer K, Hornik, K (2003) Testing and dating of structural changes in practice. Computational Statistics & Data Analysis 44(1-2), 109-123. doi:10.1016/S0167-9473(03)00030-6

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1_breaks <- 
  growth_breaks(infpan_CH)
summary(CH_covidwave1_breaks)
# Breakpoints for infpan object infpan_CH 
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1_breaks <- 
  growth_breaks(infpan_CH)
summary(CH_covidwave1_breaks)
# Breakpoints for infpan object infpan_CH

Methods for Function `growth_breaks`

Description

Methods for function growth_breaks

Methods

signature(object = "infpan", ln = FALSE, add_constant = 1, alpha = 0.05, verbose = FALSE): Estimation of $N$ breakpoint analyses for infections panel data for $N$ regions. For details, see breaks_growth.

Author(s)

Thomas Wieland

Hawkes Processs models for Regional Infections

Description

Estimates $N$ Hawkes process models for $N$ regions.

Usage

growth_hawkes(
  object, 
  optim_method = "L-BFGS-B",
  verbose = FALSE
  )
growth_hawkes(
  object, 
  optim_method = "L-BFGS-B",
  verbose = FALSE
  )

Arguments

object

object of class infpan

optim_method

character value for the optimization method. Passed to argument method in stats function optim()

verbose

bool argument which indicates whether progress messages are displayed

Details

The function estimates Hawkes process models for regional infections based on an infpan object. See hawkes_growth for further details of the estimation.

Value

object of class growthmodels-class

Author(s)

Thomas Wieland

References

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1_Hawkes <- 
  growth_hawkes(infpan_CH)
summary(CH_covidwave1_Hawkes)
# Hawkes process models for infpan object infpan_CH 
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1_Hawkes <- 
  growth_hawkes(infpan_CH)
summary(CH_covidwave1_Hawkes)
# Hawkes process models for infpan object infpan_CH

Methods for Function `growth_hawkes`

Description

Methods for function growth_hawkes

Methods

signature(object = "infpan", optim_method = "L-BFGS-B", verbose = FALSE): Estimation of $N$ Hawkes process models for $N$ regions. Set argument optim_method for using the optimization method from stats::optim.

Author(s)

Thomas Wieland

Exponential Growth Models for Regional Infections

Description

Estimates $N$ exponential growth models for a given time period in $N$ regions.

Usage

growth_initial(
  object, 
  time_units = 10,
  GI = 4,
  nls = TRUE,
  nls_start = list(a = 1, b = 0.1),
  add_constant = 1,
  verbose = FALSE
  )
growth_initial(
  object, 
  time_units = 10,
  GI = 4,
  nls = TRUE,
  nls_start = list(a = 1, b = 0.1),
  add_constant = 1,
  verbose = FALSE
  )

Arguments

object

object of class infpan

time_units

numeric value for the analysis time (time units from start)

GI

Generation interval for computing $R_0$

nls

Nonlinear estimation? TRUE or FALSE

nls_start

A list with start values for the two parameters to be estimated

add_constant

Numeric constant to be added to y if zero values occur (only relevant for OLS estimation)

verbose

bool argument which indicates whether progress messages are displayed

Details

The method estimates exponential growth models for regional infections based on an infpan object. Such models are design for the analysis of the initial phase of an epidemic spread. The user must state how much time units (from start) are included. See exponential_growth for further details of the estimation.

Value

object of class growthmodels-class

Author(s)

Thomas Wieland

References

Bonifazi G et al. (2021) A simplified estimate of the effective reproduction number Rt using its relation with the doubling time and application to Italian COVID-19 data. The European Physical Journal Plus 136, 386. doi:10.1140/epjp/s13360-021-01339-6

Pell B, Kuang Y, Viboud C, Chowell G (2018) Using phenomenological models for forecasting the 2015 ebola challenge. Epidemics 22, 62–70. doi:10.1016/j.epidem.2016.11.002

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1_initialgrowth_3weeks <- 
  growth_initial(
    infpan_CH,
    time_units = 21
  )
summary(CH_covidwave1_initialgrowth_3weeks)
# Exponential models for infpan object CH_covidwave1 
# initial growth in the first 3 weeks
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1_initialgrowth_3weeks <- 
  growth_initial(
    infpan_CH,
    time_units = 21
  )
summary(CH_covidwave1_initialgrowth_3weeks)
# Exponential models for infpan object CH_covidwave1 
# initial growth in the first 3 weeks

Methods for Function `growth_initial`

Description

Methods for function growth_initial

Methods

signature(object = "infpan", time_units = 10, GI = 4, nls = TRUE, nls_start = list(a = 1, b = 0.1), add_constant = 1, verbose = FALSE): Estimation of $N$ exponential growth models for the initial phase of an epidemic spread for $N$ regions. Set argument GI for the calculation of the basic reproduction number, and control OLS/NLS estimation with arguments nls, nls_start, and add_constant (see exponential_growth).

Author(s)

Thomas Wieland

Methods for Function `growth`

Description

Methods for function growth

Methods

signature(object = "infpan", S_iterations = 10, S_start_est_method = "bisect", seq_by = 10, nls = TRUE, add_constant = 1, verbose = FALSE): Estimation of $N$ logistic growth models for $N$ regions. Both OLS and NLS estimation are estimated by default (set nls = FALSE to skip NLS estimation). Parameters S_iterations, S_start_est_method, and seq_by are used to control the saturation value estimation (see logistic_growth).

Author(s)

Thomas Wieland

Class `"growthmodels"`

Description

The class "growthmodels" contains the results of growth model analyses and the related input data as well as additional information. The swash package includes the following model analyses under the heading "growth models": Exponential growth models, logistic growth models, Hawkes Process models, and time series models with breakpoints. Use summary(growthmodels) for results summary. See the corresponding functions for details: exponential_growth, logistic_growth, hawkes_growth, breaks_growth.

Objects from the Class

Objects can be created by the functions exponential_growth, logistic_growth, breaks_growth, or hawkes_growth.

Slots

results:: Object of class "data.frame" Model results as a table with coefficents, fit metrics, etc.
growth_models:: Object of class "list" containing all models
model_type:: Object of class "character" describing the type of model
results_cols:: Object of class "character" Vector with column names containing results
results_cols_names:: Object of class "character" Vector with descriptions of the column names
data_statistics:: Object of class "numeric" Diagnostics of input data
time_format:: Object of class "character" Format of time points in time column
timestamp:: Object of class "list" Time stamps of any update of the instance

Methods

print: signature(x = "growthmodels"): Prints a growthmodels object; use summary(growthmodels) for results
show: signature(object = "growthmodels"): Prints a growthmodels object; use summary(growthmodels) for results
summary: signature(object = "growthmodels"): Prints a summary of growthmodels objects (model results)

Author(s)

Thomas Wieland

References

Bai J, Perron P (2003) Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18(1), 1-22. doi:10.1002/jae.659

Chowell G, Simonsen L, Viboud C, Yang K (2014) Is West Africa Approaching a Catastrophic Phase or is the 2014 Ebola Epidemic Slowing Down? Different Models Yield Different Answers for Liberia. PLoS currents 6. doi:10.1371/currents.outbreaks.b4690859d91684da963dc40e00f3da81

Pell B, Kuang Y, Viboud C, Chowell G (2018) Using phenomenological models for forecasting the 2015 ebola challenge. Epidemics 22, 62–70. doi:10.1016/j.epidem.2016.11.002

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324

Zeileis C, Kleiber W, Krämer K, Hornik, K (2003) Testing and dating of structural changes in practice. Computational Statistics & Data Analysis 44(1-2), 109-123. doi:10.1016/S0167-9473(03)00030-6

Examples

showClass("growthmodels")
showClass("growthmodels")

Hawkes Process Model for Epidemic Data

Description

Estimation of Hawkes Process models from incremental infections data

Usage

hawkes_growth(
  y,
  optim_method = "L-BFGS-B",
  verbose = FALSE
  )
hawkes_growth(
  y,
  optim_method = "L-BFGS-B",
  verbose = FALSE
  )

Arguments

y

numeric vector with incremental infections data over time (e.g., daily infections)

optim_method

character specifying the optimization algorithm, passed to stats::optim

verbose

bool argument which indicates whether progress messages are displayed

Details

This function allows the estimation of a Hawkes Process model, with the time decay being expressed as exponential function, which results in three estimated parameters: $\mu$ (background rate), $\alpha$ (excitation parameter), and $\beta$ (decay). The user must specify the dependent variable (incremental infections). The estimation is performed using nonlinear estimation via stats::optim. See the corresponding documentation for available optimization methods (default: "L-BFGS-B").

Value

object of class hawkes-class

Author(s)

Thomas Wieland

References

Examples

data(Infections)
# Confirmed SARS-CoV-2 cases in Germany

hawkes_BS <- hawkes_growth(
  y = Infections$infections_daily
)
# Hawkes Process model

summary(hawkes_BS)
# Summary of Hawkes model estimates

plot(hawkes_BS)
# Plot of Hawkes Process model
data(Infections)
# Confirmed SARS-CoV-2 cases in Germany

hawkes_BS <- hawkes_growth(
  y = Infections$infections_daily
)
# Hawkes Process model

summary(hawkes_BS)
# Summary of Hawkes model estimates

plot(hawkes_BS)
# Plot of Hawkes Process model

Class `"hawkes"`

Description

The class "hawkes" contains the results of the hawkes_growth() function. Use summary(hawkes) for results summary.

Objects from the Class

Objects can be created by the function hawkes_growth.

Slots

t:: Object of class numeric Input time points data
y:: Object of class numeric Input infections data
mu:: Object of class numeric Estimated $\mu$ parameter (background rate)
alpha:: Object of class numeric Estimated $\alpha$ parameter (excitation parameter)
beta:: Object of class numeric Estimated $\beta$ parameter (decay)
br:: Object of class numeric Estimated breaking ratio ( $\alpha$ / $\beta$ )
y_pred:: Object of class numeric Predicted values of y
fit_metrics:: Object of class list Fit metrics for model, output from built-in function fit_metrics
config:: Object of class list Model fit configurations

Methods

summary: signature(object = "hawkes"): Prints a summary of hawkes objects
print: signature(x = "hawkes"): Prints an hawkes object; use summary(hawkes) for results
plot: signature(x = "hawkes"): Plots the results of the Hawkes model (observed, predicted)

Author(s)

Thomas Wieland

References

Examples

showClass("hawkes")
showClass("hawkes")

Creating Histograms with Confidence Intervals

Description

Plot of a histogram of a given vector x and the related confidence intervals (lower, upper).

Usage

hist_ci(
  x, 
  alpha = 0.05,
  col_bars = "grey", 
  col_ci = "red",
  ...
  )
hist_ci(
  x, 
  alpha = 0.05,
  col_bars = "grey", 
  col_ci = "red",
  ...
  )

Arguments

x

A numeric vector

alpha

Significance level $\alpha$ for 1- $\alpha$ *100 confidence intervals

col_bars

Color of bars in histogram

col_ci

Color of lines for confidence interval

...

Additional arguments passed to barplot()

Details

Helper function for plot(sbm_ci), but may be used separately.

Value

Histogram plot, no returned value

Author(s)

Thomas Wieland

Examples

numeric_vector <- c(1,9,5,6,3,10,20,6,9,14,3,5,8,6,11)
# any numeric vector

hist_ci(numeric_vector)
numeric_vector <- c(1,9,5,6,3,10,20,6,9,14,3,5,8,6,11)
# any numeric vector

hist_ci(numeric_vector)

Infections

Description

Daily SARS-CoV-2 infection in Germany spring 2020

Usage

data(Infections)
data(Infections)

Format

A data.frame with multiple columns:

infectedtest_CW2: Calendar week 2020 of conducted test
infection_date: Estimated date of infection
infections_daily: Daily infections
infections_daily_lwr: Daily infections lower confidence interval
infections_daily_upr: Daily infections upper confidence interval
infections_cum: Cumulative infections
infections_cum_lwr: Cumulative infections lower confidence interval
infections_cum_upr: Cumulative infections upper confidence interval
R4: Estimated effective reproduction number $R_t$ with generation interval = 4
R4_lwr: Estimated effective reproduction number $R_t$ with generation interval = 4 lower confidence interval
R4_upr: Estimated effective reproduction number $R_t$ with generation interval = 4 upper confidence interval
R7: Estimated effective reproduction number $R_t$ with generation interval = 7
R7_lwr: Estimated effective reproduction number $R_t$ with generation interval = 7 lower confidence interval
R7_upr: Estimated effective reproduction number $R_t$ with generation interval = 7 upper confidence interval
onsets_of_symptoms: Daily onsets of symptoms
onsets_of_symptoms_lwr: Daily onsets of symptoms lower confidence interval
onsets_of_symptoms_upr: Daily onsets of symptoms upper confidence interval
reported_cases: Daily reported cases
day: Time counter (day)
ln_inf_cum: Nat. log. of cumulative infections
ln_inf_daily: Nat. log. of daily infections
ln_R4: Nat. log. of estimated effective reproduction number $R_t$ with generation interval = 4
ln_R7: Nat. log. of estimated effective reproduction number $R_t$ with generation interval = 7
infection_date_CW: Calendar week of infection data (numeric)
infection_date_CW2: Calendar week of infection data (categorical)
infectedtest_CW: Calendar week of conducted test
conducted_tests: No. of conducted tests
negative_tests: No. of negative tests
positive_tests: No. of positive tests
positive_tests_share: Share of positive tests (average per day)
conducted_tests_index: No. of conducted tests (average per day), index (CW 14 = 100)
conducted_tests_dailyaverage: No. of conducted tests, average per day
positive_tests_dailyaverage: Positive tests, average per day
infections_daily_testweighted: Daily infections weighted by test volume
ln_inf_daily_tw: Nat. log. of daily infections weighted by test volume

Details

Example data with daily SARS-CoV-2 infections in Germany. See Wieland (2020) for data sources and method of backdating infections.

Source

Examples

data(Infections)
data(Infections)

Class `"infpan"`

Description

The class "infpan" contains infections panel data for $N$ regions and $T$ time points as well as additional information. Use summary(infpan) and plot(infpan) for results summary and plotting, respectively.

Objects from the Class

Objects can be created by importing infections panel data using the function load_infections_paneldata.

Slots

input_data:: Object of class "data.frame" Model result: Input infections panel data
data_statistics:: Object of class "numeric" Data statistics ( $N$ regions, $T$ time points, test whether data is balanced, etc.)
index_col_names:: Object of class "character" Column names of regions and time points
cases_col_name:: Object of class "character" Column name of incremental cases
other_cols:: Object of class "character" Names of other relevant columns derived from incremental case data, e.g. effective reproduction number $R_t$
time_format:: Object of class "character" Format of time points in time column
time_unit:: Object of class "character" Time unit, default: "days"
timestamp:: Object of class "list" Time stamps of any update of the instance

Methods

plot: signature(x = "infpan"): Plots case data by region for $N$ regions and $T$ time points
calculate_Rt: signature(x = "infpan"): Calculates the effective reproduction number $R_t$ from infpan objects. Returns updated infpan instance.
calculate_cum: signature(x = "infpan"): Calculates cumulative cases from infpan objects. Returns updated infpan instance.
calculate_rollmean: signature(x = "infpan"): Calculates rolling means of cases from infpan objects. Returns updated infpan instance.
calculate_rollsum: signature(x = "infpan"): Calculates rolling sums of cases from infpan objects. Returns updated infpan instance.
calculate_incidence: signature(x = "infpan"): Calculates incidences of cases from infpan objects. Returns updated infpan instance.
print: signature(x = "infpan"): Prints an infpan object; use summary(infpan) for results
show: signature(object = "infpan"): Shows an infpan object; use summary(infpan) for results
summary: signature(object = "infpan"): Prints a summary of infpan objects
swash: signature(object = "infpan"): Performs a Swash-Backwash Model analysis from infpan objects. Returns sbm instance.
growth: signature(object = "infpan"): Estimates logistic growth models from infpan objects. Returns growthmodels instance.
growth_initial: signature(object = "infpan"): Estimates exponential growth models from infpan objects for a given time period. Returns growthmodels instance.
growth_hawkes: signature(object = "infpan"): Estimates Hawkes process models from infpan objects. Returns growthmodels instance.
breaks_growth: signature(object = "infpan"): Estimates breakpoints in linear regression models from infpan objects. Returns growthmodels instance.

Author(s)

Thomas Wieland

References

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324

Examples

showClass("infpan")
showClass("infpan")

Test whether Panel Dataset with Regional Infection Data is Balanced

Description

The function tests whether the input panel data with regional infections is balanced.

Usage

is_balanced(
  data, 
  col_cases, 
  col_date, 
  col_region, 
  as_balanced = TRUE, 
  fill_missing = 0
  )
is_balanced(
  data, 
  col_cases, 
  col_date, 
  col_region, 
  as_balanced = TRUE, 
  fill_missing = 0
  )

Arguments

data

data.frame with regional infection data

col_cases

Column containing the cases (numeric)

col_date

Column containing the time points (e.g., days)

col_region

Column containing the unique identifier of the regions (e.g., name, NUTS 3 code)

as_balanced

Boolean argument which indicates whether non-balanced panel data shall be balanced (default: TRUE)

fill_missing

Constant to fill missing values (default and recommended: 0)

Details

The Swash-Backwash Model for the Single Epidemic Wave does not necessarily require balanced panel data in order for the calculations to be carried out. However, for a correct estimation it is implicitly assumed that the input data is balanced. The function tests whether the panel data is balanced. It is executed automatically whithin the swash() function (using automatic correction with as_balanced = TRUE), but can also be used separately.

Value

List with two entries:

data_balanced

Result of test (TRUE or FALSE)

data

Input dataset (data.frame)

Author(s)

Thomas Wieland

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

COVID19Cases_geoRegion_balanced <- 
  is_balanced(
  data = COVID19Cases_geoRegion,
  col_cases = "entries",
  col_date = "datum",
  col_region = "geoRegion"
)
# Test whether "COVID19Cases_geoRegion" is balanced panel data 

COVID19Cases_geoRegion_balanced$data_balanced
# Balanced? TRUE or FALSE

if (COVID19Cases_geoRegion_balanced$data_balanced == FALSE) {
  COVID19Cases_geoRegion <- 
    as_balanced(
    COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion"
  )
}
# Correction of dataset "COVID19Cases_geoRegion"
# not necessary as parameter balance of is_balanced is set TRUE by default
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

COVID19Cases_geoRegion_balanced <- 
  is_balanced(
  data = COVID19Cases_geoRegion,
  col_cases = "entries",
  col_date = "datum",
  col_region = "geoRegion"
)
# Test whether "COVID19Cases_geoRegion" is balanced panel data 

COVID19Cases_geoRegion_balanced$data_balanced
# Balanced? TRUE or FALSE

if (COVID19Cases_geoRegion_balanced$data_balanced == FALSE) {
  COVID19Cases_geoRegion <- 
    as_balanced(
    COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion"
  )
}
# Correction of dataset "COVID19Cases_geoRegion"
# not necessary as parameter balance of is_balanced is set TRUE by default

Import of infections panel data

Description

Loading infections panel data (data.frame) and creating an object of class infpan

Usage

load_infections_paneldata(
  data,
  col_cases, 
  col_date, 
  col_region,
  other_cols = NULL,
  time_format = "%Y-%m-%d",
  time_unit = "days",
  verbose = FALSE
  )
load_infections_paneldata(
  data,
  col_cases, 
  col_date, 
  col_region,
  other_cols = NULL,
  time_format = "%Y-%m-%d",
  time_unit = "days",
  verbose = FALSE
  )

Arguments

data

data.frame with regional infection data

col_cases

character, Column containing the cases (numeric)

col_date

character, Column containing the time points (e.g., days)

col_region

character, Column containing the unique identifier of the regions (e.g., name, NUTS 3 code)

other_cols

list, Further columns in the input data

time_format

character, Time format of the values in col_date

time_unit

character, Time unit of the values in col_date, e.g., "days"

verbose

bool argument which indicates whether progress messages are displayed

Details

The function imports user-given infections panel data. The input data is checked in several ways (e.g., whether data is balanced or not). Other relevant columns from the input data may be defined in the character vector other_cols: "R_t" (Effective reproduction number), "Cum. cases" (Cumulative cases), "Incidence" Incidence (per xxx pop), "Population" (Population size of the region), "Roll. mean" (Rolling mean of cases), and "Roll. sum" (Rolling sum of cases).

The output is an object of class infpan. The results can be viewed using summary(infpan). From an instance of class infpan, all built-in analyses for infections panel data may be conducted, e.g., the Swash-Backwash Model (swash(infpan)) or logistic growth models (growth(infpan)).

Value

object of class infpan-class

Author(s)

Thomas Wieland

References

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1 <-
  swash(
    infpan_CH,
    verbose = TRUE
    )
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)

summary(CH_covidwave1)
# Summary of Swash-Backwash Model
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1 <-
  swash(
    infpan_CH,
    verbose = TRUE
    )
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)

summary(CH_covidwave1)
# Summary of Swash-Backwash Model

Class `"loggrowth"`

Description

The class "loggrowth" contains the results of the logistic_growth() function. Use summary(loggrowth) and plot(loggrowth) for results summary and plotting, respectively.

Objects from the Class

Objects can be created by the function logistic_growth.

Slots

LinModel:: Object of class list Results of the OLS helper model
GrowthModel_OLS:: Object of class list Results of the OLS fit (predicted, parameters, first derivative)
GrowthModel_NLS:: Object of class list Results of the NLS fit (predicted, parameters, first derivative)
t:: Object of class numeric Input time points data
y:: Object of class numeric Input infections data
config:: Object of class list Model fit configurations

Methods

plot: signature(x = "loggrowth"): Plots the results of the logistic growth model (observed, predicted, first derivative)
summary: signature(object = "loggrowth"): Prints a summary of loggrowth objects
print: signature(x = "loggrowth"): Prints an loggrowth object; use summary(loggrowth) for results

Author(s)

Thomas Wieland

References

Pell B, Kuang Y, Viboud C, Chowell G (2018) Using phenomenological models for forecasting the 2015 ebola challenge. Epidemics 22, 62–70. doi:10.1016/j.epidem.2016.11.002

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324

Examples

showClass("loggrowth")
showClass("loggrowth")

Logistic Growth Model for Epidemic Data

Description

Estimation of logistic growth models from cumulative infections data, linearized OLS and/or NLS

Usage

logistic_growth(
  y, 
  t, 
  S = NULL,
  S_start = NULL, 
  S_end = NULL, 
  S_iterations = 10, 
  S_start_est_method = "bisect", 
  seq_by = 10,
  nls = TRUE,
  add_constant = 1,
  verbose = FALSE
  )
logistic_growth(
  y, 
  t, 
  S = NULL,
  S_start = NULL, 
  S_end = NULL, 
  S_iterations = 10, 
  S_start_est_method = "bisect", 
  seq_by = 10,
  nls = TRUE,
  add_constant = 1,
  verbose = FALSE
  )

Arguments

y

numeric vector with cumulative infections data over time

t

vector of class numeric or Date with time points or dates

S

Saturation value for the model

S_start

Start value of the saturation value for estimation

S_end

End value of the saturation value for estimation

S_iterations

Number of iterations for saturation value search

S_start_est_method

Method for saturation value search, either "bisect" or "trial_and_error"

seq_by

No of segments for the "trial_and_error" estimation of the saturation value

nls

Nonlinear estimation? TRUE or FALSE

add_constant

Numeric constant to be added to y if zero values occur (only relevant for OLS estimation)

verbose

bool argument which indicates whether progress messages are displayed

Details

This function allows the estimation of a logistic growth model. The user must specify the dependent variable (cumulative infections) and the time variable (time counter or date values). The estimation is performed using a linearized model with an OLS estimator and nonlinear least squares. For the former, the saturation value can either be specified by the user or found using a search algorithm. The parameters from the OLS fit are used as starting values for the NLS estimation.

Value

object of class loggrowth-class

Author(s)

Thomas Wieland

References

Chowell G, Simonsen L, Viboud C, Yang K (2014) Is West Africa Approaching a Catastrophic Phase or is the 2014 Ebola Epidemic Slowing Down? Different Models Yield Different Answers for Liberia. PLoS currents 6. doi:10.1371/currents.outbreaks.b4690859d91684da963dc40e00f3da81

Pell B, Kuang Y, Viboud C, Chowell G (2018) Using phenomenological models for forecasting the 2015 ebola challenge. Epidemics 22, 62–70. doi:10.1016/j.epidem.2016.11.002

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

COVID19Cases_BS <-
  COVID19Cases_geoRegion[(COVID19Cases_geoRegion$geoRegion == "ZH")
                         & (COVID19Cases_geoRegion$sumTotal > 0),]
# COVID cases for Zurich

loggrowth_BS <- logistic_growth (
  y = as.numeric(COVID19Cases_BS$sumTotal), 
  t = COVID19Cases_BS$datum, 
  S = 5557,
  S_start = NULL, 
  S_end = NULL, 
  S_iterations = 10, 
  S_start_est_method = "bisect", 
  seq_by = 10,
  nls = TRUE
)
# Logistic growth model with stated saturation value

summary(loggrowth_BS)
# Summary of logistic growth model

plot(loggrowth_BS)
# Plot of logistic growth model
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

COVID19Cases_BS <-
  COVID19Cases_geoRegion[(COVID19Cases_geoRegion$geoRegion == "ZH")
                         & (COVID19Cases_geoRegion$sumTotal > 0),]
# COVID cases for Zurich

loggrowth_BS <- logistic_growth (
  y = as.numeric(COVID19Cases_BS$sumTotal), 
  t = COVID19Cases_BS$datum, 
  S = 5557,
  S_start = NULL, 
  S_end = NULL, 
  S_iterations = 10, 
  S_start_est_method = "bisect", 
  seq_by = 10,
  nls = TRUE
)
# Logistic growth model with stated saturation value

summary(loggrowth_BS)
# Summary of logistic growth model

plot(loggrowth_BS)
# Plot of logistic growth model

Fit metrics of observed and expected numeric variables

Description

Calculation of fit metrics for observed and expected numeric variables (e.g. $R^2$ , $RMSE$ , $MAE$ , $MAPE$ ).

Usage

metrics(
  observed,
  expected,
  plot = TRUE,
  plot.main = "Observed vs. expected",
  xlab = "Observed",
  ylab = "Expected",
  point.col = "blue",
  point.pch = 19,
  line.col = "red",
  plot_residuals.main = "Residuals",
  legend.cex = 0.7
)
metrics(
  observed,
  expected,
  plot = TRUE,
  plot.main = "Observed vs. expected",
  xlab = "Observed",
  ylab = "Expected",
  point.col = "blue",
  point.pch = 19,
  line.col = "red",
  plot_residuals.main = "Residuals",
  legend.cex = 0.7
)

Arguments

observed

Numeric vector of observed values.

expected

Numeric vector of expected or predicted values.

plot

Logical. If TRUE, diagnostic plots for observed vs. expected values and relative residual distributions are created.

plot.main

Character string. Title of the observed vs. expected plot.

xlab

Character string. Label of the x-axis.

ylab

Character string. Label of the y-axis.

point.col

Color of points in the observed vs. expected plot.

point.pch

Plotting character used for points.

line.col

Color of the identity line ( $y = x$ ).

plot_residuals.main

Character string. Title of the residuals bar plot.

legend.cex

Numeric. Character expansion factor for legends.

Details

The function computes several goodness-of-fit metrics comparing observed and expected numeric values. In addition to classical error measures such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), the coefficient of determination ( $R^2$ ) is calculated.

If plot = TRUE, the function produces:

a scatter plot of observed versus expected values including the identity line,
a bar plot of relative residual frequencies.

Value

A list with two elements:

fit_metrics

A list containing the computed fit metrics: SQR, SAR, SQT, R2, MSE, RMSE, MAE, and MAPE.

observed_expected

A data.frame containing observed values, expected values, residuals, and derived residual measures.

Author(s)

Thomas Wieland

References

Boehmke B, Greenwell B (2020). Hands-On Machine Learning with R (1st ed.). Taylor & Francis, New York, NY.

Examples

obs <- c(10, 12, 15, 18, 20)
exp <- c(11, 13, 14, 17, 21)

metrics(
  observed = obs,
  expected = exp
)
obs <- c(10, 12, 15, 18, 20)
exp <- c(11, 13, 14, 17, 21)

metrics(
  observed = obs,
  expected = exp
)

Construct Neighbourhood Matrix from Polygons

Description

Building a neighbourhood matrix based on regions (polygons) with contiguous boundaries and resulting a data frame

Usage

nbmatrix(
  polygon_sf, 
  ID_col,
  row.names = NULL
  )
nbmatrix(
  polygon_sf, 
  ID_col,
  row.names = NULL
  )

Arguments

polygon_sf

sf object with polygons

ID_col

Column of polygon_sf with unique ID of each polygon

row.names

row.names for the sf object

Details

The function is based on spdep::poly2nb, which creates neighbours lists. The input is a sf object (spatial data frame) and the results are 1) a nb list (poly2nb result) and 2) a data.frame.

Value

list with two entries:

nb:

Object of class "sb" Neighbours list; see the spdep::poly2nb documentation

nbmat:

Object of class "data.frame" Dataset neighbouring regions

Author(s)

Thomas Wieland

References

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324

Wieland T (2022) Spatial patterns of excess mortality in the first year of the COVID-19 pandemic in Germany. European Journal of Geography 13(4), 18-33. doi:10.48088/ejg.t.wie.13.4.018.033

Examples

data(RKI_Corona_counties)
# German counties (Source: Robert Koch Institute)

Corona_nbmat <- 
  nbmatrix (
    RKI_Corona_counties, 
    ID_col="AGS"
  )
# Creating neighborhood matrix
data(RKI_Corona_counties)
# German counties (Source: Robert Koch Institute)

Corona_nbmat <- 
  nbmatrix (
    RKI_Corona_counties, 
    ID_col="AGS"
  )
# Creating neighborhood matrix

Calculate Neighbourhood Statistics from Polygons

Description

Calculating descriptive neighbourhood statistics based on regions (polygons) with contiguous boundaries and resulting a data frame

Usage

nbstat(
  polygon_sf, 
    ID_col, 
    link_data, 
    data_ID_col, 
    data_col, 
    func = "sum",
    row.names = NULL
  )
nbstat(
  polygon_sf, 
    ID_col, 
    link_data, 
    data_ID_col, 
    data_col, 
    func = "sum",
    row.names = NULL
  )

Arguments

polygon_sf

sf object with polygons

ID_col

Column of polygon_sf with unique ID of each polygon

link_data

data.frame to merge with

data_ID_col

Column with unique ID of each polygon in data.frame

data_col

Column with regarded numeric values in data.frame

func

Descriptive statistic (FUN) to be computed for data_col of the neighbouring regions

row.names

row.names for the sf object

Details

The function is based on spdep::poly2nb, which creates neighbours lists. The input is a sf object (spatial data frame) and the results are 1) a nb list (poly2nb result) and 2) a data.frame.

Value

list with three entries:

nbmat:

Object of class "data.frame" Dataset neighbouring regions

nbmat_data:

Object of class "data.frame" Dataset neighbouring regions and linked data

nbmat_data_aggreagte:

Object of class "data.frame" Dataset with statistic by region

Author(s)

Thomas Wieland

References

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. doi:10.18335/region.v7i2.324

Wieland T (2022) Spatial patterns of excess mortality in the first year of the COVID-19 pandemic in Germany. European Journal of Geography 13(4), 18-33. doi:10.48088/ejg.t.wie.13.4.018.033

Examples

data(RKI_Corona_counties)
# German counties (Source: Robert Koch Institute)

Corona_nbstat <- 
  nbstat (
    RKI_Corona_counties, 
    ID_col="AGS",
    link_data = RKI_Corona_counties, 
    data_ID_col = "AGS", 
    data_col = "EWZ", 
    func = "sum"
  )
Corona_nbstat$nbmat_data_aggregate
# Sum of population (EWZ) of neighbouring counties
data(RKI_Corona_counties)
# German counties (Source: Robert Koch Institute)

Corona_nbstat <- 
  nbstat (
    RKI_Corona_counties, 
    ID_col="AGS",
    link_data = RKI_Corona_counties, 
    data_ID_col = "AGS", 
    data_col = "EWZ", 
    func = "sum"
  )
Corona_nbstat$nbmat_data_aggregate
# Sum of population (EWZ) of neighbouring counties

Austria Daily COVID-19 cases by region 2020-02-26 to 2020-05-31

Description

A dataset containing COVID-19 cases by region (NUTS 3) and time periods (days) for Austria (Source: BMSGPK).

Usage

data(Oesterreich_Faelle)
data(Oesterreich_Faelle)

Format

A data.frame with multiple columns:

NUTS3: (character) Region for which the data was collected.
Datum: (Date) Date of record.
Faelle: (integer) Number of reported cases on this date.

Details

The data was originally published by BMSGPK at a smaller spatial scale level (political districts, "Politische Bezirke"). The data was linked to a corresponding shapefile from Statistik Austria (2022), joined to the NUTS3 level via a spatial join, and summed over the Austrian NUTS3 regions. The spatial join is based on polygon centroids of the political districts level; in cases where the centroid was outside the polygon, it was placed inside the polygon manually.

Source

BMSGPK, Oesterreichisches COVID-19 Open Data Informationsportal (2022) COVID-19: Zeitliche Darstellung von Daten zu Covid19-Faellen je Bezirk. https://www.data.gv.at/katalog/dataset/4b71eb3d-7d55-4967-b80d-91a3f220b60c (retrieved 2022-06-23)

Statistik Austria (2022) Politische Bezirke. https://www.data.gv.at/katalog/dataset/stat_gliederung-osterreichs-in-politische-bezirke131e2 (retrieved 2022-06-27, URL does not exist anymore)

Wieland T (2022) C19dNUTS: Dataset of Regional COVID-19 Deaths per 100,000 Pop (NUTS). R package v1.0.1. doi:10.32614/CRAN.package.C19dNUTS

Examples

data(Oesterreich_Faelle)
# Get Austrian COVID19 cases at NUTS 3 level
# (first wave, same final date as in Swiss data: 2020-05-31)

AT_covidwave1 <- 
  swash_backwash(
    data = Oesterreich_Faelle,
    col_cases = "Faelle",
    col_date = "Datum",
    col_region = "NUTS3"
  )
# Swash-Backwash Model for Austrian COVID19 cases
# Spatial aggregate: NUTS 3

summary(AT_covidwave1)
# Summary of model results
data(Oesterreich_Faelle)
# Get Austrian COVID19 cases at NUTS 3 level
# (first wave, same final date as in Swiss data: 2020-05-31)

AT_covidwave1 <- 
  swash_backwash(
    data = Oesterreich_Faelle,
    col_cases = "Faelle",
    col_date = "Datum",
    col_region = "NUTS3"
  )
# Swash-Backwash Model for Austrian COVID19 cases
# Spatial aggregate: NUTS 3

summary(AT_covidwave1)
# Summary of model results

Plot Point Estimates With Confidence Intervals

Description

Plotting point estimates with confidence intervals from regression results

Usage

plot_coef_ci(
  point_estimates,
  confint_lower,
  confint_upper,
  coef_names,
  p = NULL,
  estimate_colors = NULL,
  confint_colors = NULL,
  auto_color = FALSE,
  alpha = 0.05,
  set_estimate_colors = c("red", "grey", "green"),
  set_confint_colors = c("#ffcccb", "lightgray", "#CCFFCC"),
  skipvars = NULL,
  plot.xlab = "Independent variables",
  plot.main = "Point estimates with CI",
  axis.at = seq(-30, 40, by = 5),
  pch = 15,
  cex = 2,
  lwd = 5,
  y.cex = 0.8
  )
plot_coef_ci(
  point_estimates,
  confint_lower,
  confint_upper,
  coef_names,
  p = NULL,
  estimate_colors = NULL,
  confint_colors = NULL,
  auto_color = FALSE,
  alpha = 0.05,
  set_estimate_colors = c("red", "grey", "green"),
  set_confint_colors = c("#ffcccb", "lightgray", "#CCFFCC"),
  skipvars = NULL,
  plot.xlab = "Independent variables",
  plot.main = "Point estimates with CI",
  axis.at = seq(-30, 40, by = 5),
  pch = 15,
  cex = 2,
  lwd = 5,
  y.cex = 0.8
  )

Arguments

point_estimates

numeric vector containing point estimates

confint_lower

numeric vector containing lower confidence intervals

confint_upper

numeric vector containing upper confidence intervals

coef_names

character vector containing coefficient names

p

numeric vector containing $p$ values of the coefficients (optional)

estimate_colors

vector containing colors for the point estimates (optional)

confint_colors

vector containing colors for the confidence intervals (optional)

auto_color

bool value which indicates whether the colors are found automatically based on coef and CI values

alpha

Significance level $\alpha$ for 1- $\alpha$ *100 confidence intervals

set_estimate_colors

Colors for point estimates (significant negative, not significant, significant positive)

set_confint_colors

Colors for confidence intervals (significant negative, not significant, significant positive)

skipvars

List with coefficients to be dropped

plot.xlab

Label of x axis

plot.main

Plot title

axis.at

Position of y axis

pch

Point type

cex

Point size

lwd

Line width (confidence intervals)

y.cex

Font size of y axis

Details

The function checks whether the input vectors have the same length. If auto_color is TRUE, the colors from set_estimate_colors and set_confint_colors are used, and the significance level is determined based on the coefficient and confidence interval values (all three below 0 = significant negative, all three above 0 = significant positive).

Value

Coefficients plot, no returned value

Author(s)

Thomas Wieland

References

Examples

data(did_fatalities_splm_coef)
# Results of a difference-in-differences model

plot_coef_ci(
  point_estimates = did_fatalities_splm_coef$Estimate,
  confint_lower = did_fatalities_splm_coef$CI_lower_Bonferroni,
  confint_upper = did_fatalities_splm_coef$CI_upper_Bonferroni,
  coef_names = did_fatalities_splm_coef$Var,
  skipvars = c(
    "Alpha_share", 
    "lambda",
    "rho",
    "log(D_Infections_daily_7dsum_per100000_lag2weeks)",
    "vacc_cum_per100000_lag2weeks"
    ),
  lwd = 13,
  pch = 19,
  auto_color = TRUE
)
# Plot with point estimates and confidence intervals
data(did_fatalities_splm_coef)
# Results of a difference-in-differences model

plot_coef_ci(
  point_estimates = did_fatalities_splm_coef$Estimate,
  confint_lower = did_fatalities_splm_coef$CI_lower_Bonferroni,
  confint_upper = did_fatalities_splm_coef$CI_upper_Bonferroni,
  coef_names = did_fatalities_splm_coef$Var,
  skipvars = c(
    "Alpha_share", 
    "lambda",
    "rho",
    "log(D_Infections_daily_7dsum_per100000_lag2weeks)",
    "vacc_cum_per100000_lag2weeks"
    ),
  lwd = 13,
  pch = 19,
  auto_color = TRUE
)
# Plot with point estimates and confidence intervals

Methods for Function `plot`

Description

Methods for function plot for different S4 classes: infpan, sbm, sbm_ci, loggrowth, expgrowth, hawkes, and breaksgrowth.

Methods

signature(x = "infpan")

plot.infpan(x, y, ...): Plots regional infections against time

Arguments:

x: An object of class infpan including infections panel data.
y: Optional argument for additional customization, such as plot style or axis labels.
...: Additional graphical parameters that can be passed to control plot appearance.

Details: This method is used to visualize case data by region for $N$ regions and $T$ time points.

signature(x = "sbm")

plot.sbm(x, y = NULL, col_edges = "blue", xlab_edges = "Time", ylab_edges = "Regions", main_edges = "Edges", col_SIR = c("blue", "red", "green"), lty_SIR = c("solid", "solid", "solid"), lwd_SIR = c(1,1,1), xlab_SIR = "Time", ylab_SIR = "Regions", main_SIR = "SIR integrals", col_cases = "red", lty_cases = "solid", lwd_cases = 1, xlab_cases = "Time", ylab_cases = "Infections", main_cases = "Daily infections", xlab_cum = "Cases", ylab_cum = "Regions", main_cum = "Cumulative infections per region", horiz_cum = TRUE, separate_plots = FALSE):

Plots the results of the Swash-Backwash Model. This generates four plots:

Edges over time.
SIR integrals.
All-over infections.
Total infections per time unit.

Arguments:

x: An object of class sbm representing the results of the Swash-Backwash Model.
y: Optional argument for additional customization, such as plot style or axis labels.
...: Additional graphical parameters that can be passed to control plot appearance.

Details: This method is used to visualize the output of the Swash-Backwash Model, providing insight into the dynamics of the modeled epidemic.

signature(x = "sbm_ci")

plot.sbm_ci(x, y, ...): Plots the results of bootstrap confidence intervals for the Swash-Backwash Model. This generates a single figure with six subplots:

$S_A$ (susceptible population),
$I_A$ (infected population),
$R_A$ (recovered population),
$t_{FE}$ (final epidemic time),
$t_{LE}$ (last epidemic time),
$R_{0A}$ (basic reproduction number).

Arguments:

x: An object of class sbm_ci containing the bootstrap confidence intervals for the Swash-Backwash Model.
y: Optional argument for additional customization, such as plot style or axis labels.
...: Additional graphical parameters for fine-tuning the plots.

Details: This method is used to visualize the bootstrap confidence intervals for various parameters of the Swash-Backwash Model.

signature(x = "countries")

plot.sbm(x, y = NULL, col_bars = "grey", col_ci = "red"): Plots the results of the between-countries analysis via Swash-Backwash Model. This generates four plots:

Indicator for country 1
Indicator for country 2
Boxplots of the distribution of the indicator in country 1 and 2
Distribution of the difference between the indicators of country 1 and 2

Arguments:

x: An object of class countries representing the results of the Swash-Backwash Model country analysis.
y: Not relevant
col_bars: Color of bars
col_ci: Color of confidence intervals

Details: This method is used to visualize the output of the Swash-Backwash Model, providing insight into the dynamics of the modeled epidemic.

signature(x = "loggrowth")

plot.loggrowth(x, y, ...): Plots the results of the logistic growth model, including:

Observed values
Predicted values
First derivative

Arguments:

x: An object of class loggrowth containing the data for the logistic growth model.
y: Optional argument for additional customization of the plot (e.g., color, labels).
...: Additional arguments for graphical parameters.

Details: This method is useful for visualizing the observed and predicted growth patterns in an epidemic or similar phenomena modeled by logistic growth.

signature(x = "expgrowth")

plot.expgrowth(x, y, ...): Plots the results of the exponentai growth model, including:

Observed values
Predicted values

Arguments:

x: An object of class expgrowth containing the data for the exponential growth model.
y: Optional argument for additional customization of the plot (e.g., color, labels).
...: Additional arguments for graphical parameters.

Details: This method is useful for visualizing the observed and predicted growth patterns in the initial phase of an epidemic or similar phenomena modeled by exponential growth.

signature(x = "hawkes")

plot.hawkes(x, y, ...): Plots the results of the Hawkes process model, including:

Observed values
Predicted values

Arguments:

x: An object of class hawkes containing the data for the Hawkes model.
y: Optional argument for additional customization of the plot (e.g., color, labels).
...: Additional arguments for graphical parameters.

Details: This method is useful for visualizing the observed and predicted growth patterns of an epidemic or similar phenomena modeled as Hawkes processes.

signature(x = "breaksgrowth")

plot.breaksgrowth(x, y, ...): Plots the results of a breakpoint analysis, including:

Time series data
Breakpoints

Arguments:

x: An object of class breaksgrowth containing the data for the breakspoints model.
y: Optional argument for additional customization of the plot (e.g., color, labels).
...: Additional arguments for graphical parameters.

Details: This method is useful for visualizing the derived breakpoints.

Author(s)

Thomas Wieland

Methods for Function `print`

Description

Methods for function print

Methods

signature(object = "infpan"): Prints an infpan object; use summary(infpan) for results
signature(x = "sbm"): Prints a sbm object; use summary(sbm) for results
signature(x = "sbm_ci"): Prints a sbm_ci object; use summary(sbm_ci) for results
signature(object = "countries"): Prints an countries object; use summary(countries) for results
signature(x = "loggrowth"): Prints a loggrowth object; use summary(loggrowth) for results
signature(x = "expgrowth"): Prints an expgrowth object; use summary(expgrowth) for results
signature(x = "hawkes"): Prints a hawkes object; use summary(hawkes) for results
signature(x = "breaksgrowth"): Prints a breaksgrowth object; use summary(breaksgrowth) for results

Computing Quantiles for a given Numeric Vector

Description

Computes quantiles for a given vector x and the related confidence intervals (lower, upper).

Usage

quantile_ci(
  x, 
  alpha = 0.05
  )
quantile_ci(
  x, 
  alpha = 0.05
  )

Arguments

x

A numeric vector

alpha

Significance level $\alpha$ for 1- $\alpha$ *100 confidence intervals

Details

Helper function for plot(sbm_ci), but may be used separately.

Value

A numeric vector with lower and upper quantile

Author(s)

Thomas Wieland

Examples

numeric_vector <- c(1,9,5,6,3,10,20,6,9,14,3,5,8,6,11)
# any numeric vector

quantile_ci(numeric_vector)
numeric_vector <- c(1,9,5,6,3,10,20,6,9,14,3,5,8,6,11)
# any numeric vector

quantile_ci(numeric_vector)

Effective Reproduction Number for Epidemic Data

Description

Calculation of the effective reproduction number for infection/surveillance data

Usage

R_t(
  infections, 
  GP = 4,
  correction = FALSE
  )
R_t(
  infections, 
  GP = 4,
  correction = FALSE
  )

Arguments

infections

numeric vector with infection data

GP

Generation period, in time units (typically days)

correction

Correction of values equal to zero? (Recommended)

Details

The function calculates the effective reproduction number, $R_t$ , of an infections time series. Set the generation period by the parameter GP (default: 4). If correction is TRUE, values equal to zero are increased by one.

Value

list with two entries:

R_t:

Object of class "numeric" $R_t$ values

infections_data:

Object of class "data.frame" Dataset with infections data and $R_t$

Author(s)

Thomas Wieland

References

an der Heiden M, Hamouda O (2020) Schätzung der aktuellen Entwicklung der SARS-CoV-2-Epidemie in Deutschland - Nowcasting. Epidemiologisches Bulletin 17, 10-15. doi:10.25646/6692

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

COVID19Cases_BS <-
  COVID19Cases_geoRegion[(COVID19Cases_geoRegion$geoRegion == "ZH")
                         & (COVID19Cases_geoRegion$sumTotal > 0),]
# COVID cases for Zurich

Rt_BS <- R_t(infections = COVID19Cases_BS$entries)
# Effective reproduction number

Rt_BS
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

COVID19Cases_BS <-
  COVID19Cases_geoRegion[(COVID19Cases_geoRegion$geoRegion == "ZH")
                         & (COVID19Cases_geoRegion$sumTotal > 0),]
# COVID cases for Zurich

Rt_BS <- R_t(infections = COVID19Cases_BS$entries)
# Effective reproduction number

Rt_BS

German Counties with COVID-19 Cases

Description

A dataset containing German counties (NUTS 3) with COVID-19 cases (Source: Robert Koch Institute).

Usage

data(RKI_Corona_counties)
data(RKI_Corona_counties)

Format

A data.frame with multiple columns:

OBJECTID: unknown/not necessary
ADE: (unknown/not necessary
GF: unknown/not necessary
BSG: unknown/not necessary
RS: (character) County code 1
AGS: (character) County code 2
SDV_RS: (character) County code 3
GEN: (character) County name
BEZ: (character) County type
IBZ: unknown/not necessary
BEM: unknown/not necessary
NBD: unknown/not necessary
SN_L: unknown/not necessary
SN_R: unknown/not necessary
SN_K: unknown/not necessary
SN_V1: unknown/not necessary
SN_V2: unknown/not necessary
SN_G: unknown/not necessary
FK_S3: unknown/not necessary
NUTS: (character) NUTS 3 code
RS_0: unknown/not necessary
AGS_0: unknown/not necessary
WSK: unknown/not necessary
EWZ: (numeric) Population
KFL: (numeric) Area in sq. km
DEBKG_ID: unknown/not necessary
Shape__Are: unknown/not necessary
Shape__Len: unknown/not necessary
death_rate
cases: (numeric) COVID-19 cases
deaths: (numeric) COVID-19 associated deaths
cases_per_: (numeric) COVID-19 cases per 100,000 inhabitants
cases_pe_1: unknown/not necessary
BL: (character) Federal state
BL_ID: (integer) Federal state ID
county: (character) County name
last_updat: Date of last update
geometry: Geometry

Details

The data is included as it was published in by the Robert Koch Institute (Robert Koch-Institut, RKI) but extended by the geometry column (Original data: shapefile).

Source

RKI (2020) RKI Corona Landkreise. Robert Koch-Institut (RKI), dl-de/by-2-0. Attribution: Robert Koch-Institut, Bundesamt für Kartographie und Geodäsie. https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/917fc37a7095 (retrieved 2020-03-30)

Examples

data(RKI_Corona_counties)
# German counties (Source: Robert Koch Institute)

Corona_nbstat <- 
  nbstat (
    RKI_Corona_counties, 
    ID_col="AGS",
    link_data = RKI_Corona_counties, 
    data_ID_col = "AGS", 
    data_col = "EWZ", 
    func = "sum"
  )
Corona_nbstat$nbmat_data_aggregate
# Sum of population (EWZ) of neighbouring counties
data(RKI_Corona_counties)
# German counties (Source: Robert Koch Institute)

Corona_nbstat <- 
  nbstat (
    RKI_Corona_counties, 
    ID_col="AGS",
    link_data = RKI_Corona_counties, 
    data_ID_col = "AGS", 
    data_col = "EWZ", 
    func = "sum"
  )
Corona_nbstat$nbmat_data_aggregate
# Sum of population (EWZ) of neighbouring counties

Class `"sbm_ci"`

Description

The class "sbm_ci" contains the results of the Swash-Backwash Model, confidence intervals for the model estimates, and the related input data as well as additional information. Use summary(sbm_ci) and plot(sbm_ci) for results summary and plotting, respectively.

Objects from the Class

Objects can be created by the function confint(sbm).

Slots

R_0A:: Object of class "numeric" Model result: spatial reproduction number $R_{0A}$
integrals:: Object of class "numeric" Model result: integrals $S_A$ , $I_A$ , and $R_A$
velocity:: Object of class "numeric" Model result: velocity measures $t_{FE}$ and $t_{LE}$
occ_regions:: Object of class "data.frame" Model result: Occurence at regional level
cases_by_date:: Object of class "data.frame" Total cases by date
cases_by_region:: Object of class "data.frame" Cumulative cases by region
input_data:: Object of class "data.frame" Input data
data_statistics:: Object of class "numeric" Diagnostics of input data
col_names:: Object of class "character" Column names in input data
integrals_ci:: Object of class "list" Confidence intervals for integrals $S_A$ , $I_A$ , and $R_A$
velocity_ci:: Object of class "list" Confidence intervals for velocity measures $t_{FE}$ and $t_{LE}$
R_0A_ci:: Object of class "numeric" Confidence intervals for spatial reproduction number $R_{0A}$
iterations:: Object of class "data.frame" Results of bootstrap sampling iterations
ci:: Object of class "numeric" Lower and upper confidence intervals based on user input
config:: Object of class "list" Configuration details for bootstrap sampling

Methods

plot: signature(x = "sbm_ci"): Plots the results of bootstrap confidence intervals for the Swash-Backwash Model; one figure with six plots: $S_A$ , $I_A$ , $R_A$ , $t_{FE}$ , $t_{LE}$ , and $R_{0A}$
print: signature(x = "sbm_ci"): Prints an sbm_ci object; use summary(sbm_ci) for results
show: signature(object = "sbm_ci"): Prints an sbm_ci object; use summary(sbm_ci) for results
summary: signature(object = "sbm_ci"): Prints a summary of sbm_ci objects (bootstrap confidence intervals for Swash-Backwash Model estimates)

Author(s)

Thomas Wieland

References

Cliff AD, Haggett P (2006) A swash-backwash model of the single epidemic wave. Journal of Geographical Systems 8(3), 227-252. doi:10.1007/s10109-006-0027-8

Efron B, Tibshirani RJ (1993) An Introduction to the Bootstrap.

Ramachandran KM, Tsokos CP (2021) Mathematical Statistics with Applications in R (Third Edition). Ch. 13.3.1 (Bootstrap confidence intervals). doi:10.1016/B978-0-12-817815-7.00013-0

Examples

showClass("sbm_ci")
showClass("sbm_ci")

Class `"sbm"`

Description

The class "sbm" contains the results of the Swash-Backwash Model and the related input data as well as additional information. Use summary(sbm) and plot(sbm) for results summary and plotting, respectively.

Objects from the Class

Objects can be created by the function swash_backwash and the infpan method swash, respectively.

Slots

R_0A:: Object of class "numeric" Model result: spatial reproduction number $R_{0A}$
integrals:: Object of class "numeric" Model result: integrals $S_A$ , $I_A$ , and $R_A$
velocity:: Object of class "numeric" Model result: velocity measures $t_{FE}$ and $t_{LE}$
occ_regions:: Object of class "data.frame" Model result: Occurence at regional level
SIR_regions:: Object of class "data.frame" Model result: Susceptible, infected and recovered regions over time
cases_by_date:: Object of class "data.frame" Total cases by date
cases_by_region:: Object of class "data.frame" Cumulative cases by region
input_data:: Object of class "data.frame" Input data
data_statistics:: Object of class "numeric" Diagnostics of input data
col_names:: Object of class "character" Original column names in input data
timestamp:: Object of class "list" Time stamps of any update of the instance

Methods

confint: signature(object = "sbm"): Creates bootstrap confidence intervals for sbm objects.
plot: signature(x = "sbm"): Plots the results of the Swash-Backwash Model; two plots: edges over time, total infections per time unit
print: signature(x = "sbm"): Prints an sbm object; use summary(sbm) for results
show: signature(object = "sbm"): Prints an sbm object; use summary(sbm) for results
summary: signature(object = "sbm"): Prints a summary of sbm objects (results of the Swash-Backwash Model)

Author(s)

Thomas Wieland

References

Cliff AD, Haggett P (2006) A swash-backwash model of the single epidemic wave. Journal of Geographical Systems 8(3), 227-252. doi:10.1007/s10109-006-0027-8

Examples

showClass("sbm")
showClass("sbm")

Methods for Function `show`

Description

Methods for function show

Methods

signature(object = "infpan"): Prints an infpan object; use summary(infpan) for results
signature(object = "sbm"): Prints an sbm object; use summary(sbm) for results
signature(object = "sbm_ci"): Prints an sbm_ci object; use summary(sbm_ci) for results
signature(object = "countries"): Prints an countries object; use summary(countries) for results
signature(object = "loggrowth"): Prints an loggrowth object; use summary(loggrowth) for results
signature(object = "expgrowth"): Prints an expgrowth object; use summary(expgrowth) for results

Methods for Function `summary`

Description

Methods for function summary

Methods

signature(object = "sbm"): Prints a summary of sbm objects (results of the Swash-Backwash Model)
signature(object = "sbm_ci"): Prints a summary of sbm_ci objects (bootstrap confidence intervals for Swash-Backwash Model estimates)
signature(object = "countries"): Prints a summary of a countries object built with the function compare_countries
signature(object = "loggrowth"): Prints a summary of a loggrowth object built with the function logistic_growth
signature(object = "expgrowth"): Prints a summary of a expgrowth object built with the function exponential_growth
signature(object = "hawkes"): Prints a summary of a hawkes object built with the function hawkes_growth
signature(object = "breaksgrowth"): Prints a summary of a breaksgrowth object built with the function breaks_growth

Swash-Backwash Model for the Single Epidemic Wave

Description

Analysis of regional infection/surveillance data stored in infpan object using the Swash-Backwash Model for the single epidemic wave by Cliff and Haggett (2006).

Usage

swash(
  object, 
  verbose = FALSE
  )
swash(
  object, 
  verbose = FALSE
  )

Arguments

object

object of class infpan

verbose

bool argument which indicates whether progress messages are displayed

Details

The method performs the analysis of the input panel data with $N$ regions and $T$ time points using the Swash-Backwash Model based on an infpan object. The output is an object of class sbm. The results can be viewed using summary(sbm). The built-in function swash_backwash is used for the analysis. See swash_backwash for further details.

Value

object of class sbm-class

Author(s)

Thomas Wieland

References

Cliff AD, Haggett P (2006) A swash-backwash model of the single epidemic wave. Journal of Geographical Systems 8(3), 227-252. doi:10.1007/s10109-006-0027-8

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1 <-
  swash(
    infpan_CH,
    verbose = TRUE
    )
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)

summary(CH_covidwave1)
# Summary of Swash-Backwash Model
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

CH_covidwave1 <-
  swash(
    infpan_CH,
    verbose = TRUE
    )
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)

summary(CH_covidwave1)
# Summary of Swash-Backwash Model

Swash-Backwash Model for the Single Epidemic Wave

Description

Analysis of regional infection/surveillance data using the Swash-Backwash Model for the Single Epidemic Wave by Cliff and Haggett (2006).

Usage

swash_backwash(
  infpan = NULL,
  data = NULL,
  col_cases = NULL, 
  col_date = NULL, 
  col_region = NULL,
  time_format = "%Y-%m-%d",
  verbose = FALSE
  )
swash_backwash(
  infpan = NULL,
  data = NULL,
  col_cases = NULL, 
  col_date = NULL, 
  col_region = NULL,
  time_format = "%Y-%m-%d",
  verbose = FALSE
  )

Arguments

infpan

infpan object containing regional infection data

data

data.frame with regional infection data

col_cases

Column containing the cases (numeric)

col_date

Column containing the time points (e.g., days)

col_region

Column containing the unique identifier of the regions (e.g., name, NUTS 3 code)

time_format

character, Time format of the values in col_date

verbose

bool argument which indicates whether progress messages are displayed

Details

The function performs the analysis of the input panel data with $N$ regions and $T$ time points using the Swash-Backwash Model. The user must state panel data with daily infections.

The Swash-Backwash Model (SBM) for the Single Epidemic Wave is the spatial equivalent of the classic epidemiological SIR (Susceptible-Infected-Recovered) model. It was developed by Cliff and Haggett (2006) to model the velocity of spread of infectious diseases across space. Current applications can be found, for example, in Smallman-Raynor et al. (2022a,b). The function swash_backwash() enables the calculation of the Swash-Backwash Model for user-supplied panel data on regional infections. It calculates the model and creates a model object of the sbm class defined in this package. This class can be used to visualize results (summary(), plot()) and calculate bootstrap confidence intervals for the model estimates (confint(sbm)); the latter returns an object of class sbm_ci as defined in this package. Two sbm_ci objects for different countries may be compared with compare_countries(), which allows the estimation of mean differences of a user-specified model parameter (e.g., spatial reproduction number $R_{OA}$ ) between two countries. This makes it possible to check whether the spatial spread velocity of a communicable disease is significantly different in one country than in another country; the result is an object of class countries.

To calculate the SBM model based on an infpan object, use the corresponding method swash(infpan).

Value

object of class sbm-class

Author(s)

Thomas Wieland

References

Cliff AD, Haggett P (2006) A swash-backwash model of the single epidemic wave. Journal of Geographical Systems 8(3), 227-252. doi:10.1007/s10109-006-0027-8

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

CH_covidwave1 <- 
  swash_backwash(
    data = COVID19Cases_geoRegion, 
    col_cases = "entries", 
    col_date = "datum", 
    col_region = "geoRegion"
    )
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)

summary(CH_covidwave1)
# Summary of Swash-Backwash Model

plot(CH_covidwave1)
# Plot of Swash-Backwash Model edges and total epidemic curve
data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

CH_covidwave1 <- 
  swash_backwash(
    data = COVID19Cases_geoRegion, 
    col_cases = "entries", 
    col_date = "datum", 
    col_region = "geoRegion"
    )
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)

summary(CH_covidwave1)
# Summary of Swash-Backwash Model

plot(CH_covidwave1)
# Plot of Swash-Backwash Model edges and total epidemic curve

Methods for Function `swash`

Description

Methods for function swash

Methods

signature( object = "infpan", verbose = FALSE): Performs the analysis of the input panel data with $N$ regions and $T$ time points using the Swash-Backwash Model based on an infpan object. The output is an object of class "sbm". The results can be viewed using summary(sbm). See swash_backwash for further details of the model analysis.

Author(s)

Thomas Wieland

Show timestamps

Description

Print timestamps stored in an object.

Usage

  timestamps(object)
timestamps(object)

Arguments

object

An object with a timestamp slot.

Value

Prints formatted timestamps to the console.

Package 'swash'

Help Index

swash: Health Geography Toolbox for Model-Based Analysis of Infections Panel Data

Description

Details

Author(s)

References

Examples

Correction of Non-balanced Panel Dataset with Regional Infection Data

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Fit metrics of observed and expected binary variables

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Fit metrics for binary logit model

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Time Series Model with Breakpoints

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Class "breaksgrowth"

Description

Objects from the Class

Slots

Methods

Author(s)

References

Examples

Regional cumulative COVID-19 deaths

Description

Usage

Format

Details

Source

Examples

Cumulative Infection Numbers

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Methods for Function calculate_cum

Description

Methods

Author(s)

Incidence from Infection Numbers

Description

Usage

Arguments

Class `"breaksgrowth"`

Methods for Function `calculate_cum`

Methods for Function `calculate_incidence`

Methods for Function `calculate_rollmean`

Methods for Function `calculate_rollsum`

Methods for Function `calculate_Rt`

Methods for Function `confint`

Class `"countries"`