COVOID: Modelling COVID-19 Transmission and Interventions
Oisin Fitzgerald, Mark Hanly, Tim Churches (UNSW Medicine)
COVOID is an evolving but fully-functional R package and accompanying Shiny app for simulation modelling of both COVID-19 transmission and the interventions intended to reduce that transmission, using deterministic compartmental models (DCMs). The built-in Shiny app enables ease of use and demonstration of key concepts to those without R programming backgrounds. The package contains an expanding API for simulating and estimating homogeneous and age-structured SIR, SEIR and extended models. In particular COVOID allows the simultaneous simulation of age specific (e.g. school closures) and general interventions over varying time intervals. This is informed through incorporation of publicly available data on population demographics from the United Nations, age and setting specific contact rates from previously published research and COVID-19 incidence counts from the European CDC. Technical background documentation and vignettes are provided, illustrating the use of the package, including the extended model. Work is ongoing to add the ability to model imported cases, enhancing parameter fitting to observed data, and to expand the Shiny UI to address the extended model and to manage parameter sensitivity analyses and comparisons of intervention scenario.
Like this contribution to see it presented and awarded at the e-Rum2020 CovidR contest pre-conference event
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