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spatialEpisim: Spatial Tracking of Infectious Diseases using Mathematical Models

Overview

spatialEpisim is an open-source platform-independent browser-based interface for tracking the spatial spread of infectious diseases (ex: COVID-19, Ebola, Measles etc.).

Run the app on Shinyapp.io server by clicking http://spatialepisim.shinyapps.io/spatialepisim

Alternatively you can send a pull request to download all the files in this repository and run the app by loading global.R and clicking Run App. Note that the spatialEpisim project is not on CRAN, just on github.

Key features

  • Run deterministic/stochastic compartmental models of epidemiology (ex: SEIR, SEIRD or SVEIRD)
  • Cross-platform: It runs in a browser on Windows, Mac, and Linux
  • Generate Spatiotemporal disease prevalence maps: either for an entire country or a smaller area (state(s)/province(s)) within a country.
  • Context: Data and examples focus on mathematical modelling of infectious disease epidemics

Compartmental Models

Schematic Diagram of the SVEIRD Model

Model Parameters

Parameter Definition
α is the daily fraction that move from the susceptible compartment into the vaccinated compartment. (Vaccinated individuals are regarded as permanently immune.)
β is the daily fraction that move from the susceptible compartment into the exposed compartment.
γ is the daily fraction that move from the exposed compartment into the infectious compartment.
σ is the daily fraction that move from the infectious compartment into the recovered compartment. (Recovered individuals are regarded as permanently immune.)
δ is the daily fraction that move from the infectious into the dead compartment (the mortality rate).

Note: Setting α = 0 and δ = 0 would default to a SEIR model while setting only α = 0 would default to a SEIRD model.

Directory structure

/
|---R/
|---gadm/
|---misc/
|---observeddata/
|---seeddata/
|---tif/
|   |---cropped/
|---www/
|   |---MP4/
|
  • gadm/ folder with .RDS files with a database of Global Administrative Areas.
  • misc/ folder with spreadsheets for default epidemic parameters and ISO3 Alpha codes.
  • R/ folder with R scripts sourced in app.R.
  • seeddata/ folder with seed data for selected countries.
  • tif/ folder with the 2020 UN-Adjusted Population Count rasters downloaded from WorldPop .
  • www/ is for static compartmental model flowcharts.
    • MP4/ is where simulation MP4 animation and output images and are saved.

Credits

This interactive R Shiny app would not be possible without the help from our team of research assistants Michael Myer, Tobias Wondwossen, Khanh Le, Michael Walsh, Tom Bayliss White, Gursimran Dhaliwal, Crystal Wai, Jake Doody, Timothy Pulfer, Ryan Darby and Jason Szeto. I thank them for their time and hardwork.

We acknowledge valuable inputs from Dr. Bedrich Sousedik and Dr. Loren Cobb.

References

L. Cobb, A. Krishnamurthy, J. Mandel, and J. Beezley. Bayesian tracking of emerging epidemics using ensemble optimal statistical interpolation (EnOSI).Spatial and Spatio-temporal Epidemiology, 10:39–48, July 2014. https://doi.org/10.1016/j.sste.2014.06.004

Feedback

The app is maintained by Dr. Ashok Krishnamurthy.

Contact: Ashok Krishnamurthy, Ph.D.
Website: https://bit.ly/2YKrXjX

We welcome questions, insights, and feedback. We accept contributions via pull request. You can also open an issue if you find a bug, or have a suggestion.

Terms of use