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

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 anaylsed 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.

Author

Thomas Wieland ORCID EMail

Availability

• 📦 CRAN: swash
• 💻 GitHub Repository: swash_official
• 📄 DOI (Zenodo): 10.5281/zenodo.18652150

Citation

If you use this software, please cite:

Wieland, T. (2026). swash: Health Geography Toolbox for Model-Based Analysis of Infections Panel Data (Version 2.0.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.18652150

Installation

To install the package from the CRAN repository, use:

install.packages("swash")

To install the package from GitHub:

install.packages("remotes")
remotes::install_github("geowieland/swash_official")

Features

The R library is a toolbox for quantitative analysis in health geography towards the spatial spread of infectious diseases. 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
• Growth Analysis with logistic growth models, exponential growth models (for the initial phase of a spread), and Hawkes process models
• Breakpoints analysis using the Bai-Parron algorithm implemented in strucchange::breakpoints
• 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.

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

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

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

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

summary(infpan_CH)
# Summary of infpan object

timestamps(infpan_CH)
# Time stamps of infpan object

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

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

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

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

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

See the /tests directory for usage examples of most of the included functions.

Literature

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. https://doi.org/10.1371/currents.outbreaks.b4690859d91684da963dc40e00f3da81

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

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

Li, M. Y. (2018). An Introduction to Mathematical Modeling of Infectious Diseases. Springer. https://doi.org/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 G. Chowell, J. M. Hyman, & L. M. A. Bettencourt (Eds.), Mathematical and Statistical Estimation Approaches in Epidemiology (pp. 103–121). Springer. https://doi.org/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. https://doi.org/10.1016/j.epidem.2016.11.002

Rizoiu, M. A., Mishra, S., Kong, Q., Carman, M. & Xie, L. (2018). SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations. 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. https://doi.org/10.1145/3178876.3186108

Smallman-Raynor, M. R., Cliff, A. D., & Stickler, P. J. (2022a). Meningococcal meningitis and coal mining in provincial England: Geographical perspectives on a major epidemic, 1929–33. Geographical Analysis, 54, 197–216. https://doi.org/10.1111/gean.12272

Smallman-Raynor, M. R., Cliff, A. D., & 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. https://doi.org/10.1017/S0950268822001285

Viboud, C., Bjørnstad, O. N., Smith, D. L., Simonsen, L., Miller, M. A., & Grenfell, B. T. (2006). Synchrony, waves, and spatial hierarchies in the spread of influenza. Science, 312, 447–451. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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: From Theory to Practice, 33(11), 2497–2511. https://doi.org/10.1007/s10389-024-02218-x

What's new (v2.0.0)

• Breaking changes (Non-backwards compatible)

  • Analyses are conducted via infpan objects rather than sbm objects
  • Former method plot_regions() for class sbm is replaced by generic method plot() for infpan objects
  • Former methods growth() and growth_initial() for sbm objects are are now methods for the infpan class
  • Former function plot_breakpoints() is replaced by function breaks_growth() and the corresponding plot() method

• New features

  • Importing infections panel data via load_infections_paneldata(), which creates and instance of the new class infpan
  • Calculation of spread indicators for infpan objects such as incidence and effective reproduction number $R_t$
  • Function hawkes_growth() for parametrization of a Hawkes process equation for infections
  • Method growth_hawkes() for parametrization of Hawkes process equations based on infpan objects
  • Additional NLS estimation in function exponential_growth()
  • Option to add a constant (if values of y equal to zero occur) in functions logistic_growth() and exponential_growth(), as well as in methods growth(infpan) and growth_initial(infpan)

• Bugfixes

  • Functions metrics(), binary_metrics() and binary_metrics_glm() now return results invisible
  • Check for equal length of input vectors in logistic_growth()