Pandæsim: An Epidemic Spreading Stochastic Simulator
LRI—UMR CNRS 8623, Université Paris Saclay, Bât. 650, 91190 Gif-sur-Yvette, France
Sys2Diag—UMR CNRS 9005, ALCEDIAG, Cap Gamma, 34184 Montpellier, France
Received: 28 August 2020
Revised: 15 September 2020
Accepted: 16 September 2020
Published: 18 September 2020
In order to study the efficiency of countermeasures used against the Covid-19 pandemic at the scale of a country, we designed a model and developed an efficient simulation program based on a well known discrete stochastic simulation framework along with a standard, coarse grain, spatial localisation extension. Our particular approach allows us also to implement deterministic continuous resolutions of the same model. We applied it to the Covid-19 epidemic in France where lockdown countermeasures were used. With the stochastic discrete method, we found good correlations between the simulation results and the statistics gathered from hospitals. In contrast, the deterministic continuous approach lead to very different results. We proposed an explanation based on the fact that the effects of discretisation are high for small values, but low for large values. When we add stochasticity, it can explain the differences in behaviour of those two approaches. This system is one more tool to study different countermeasures to epidemics, from lockdowns to social distancing, and also the effects of mass vaccination. It could be improved by including the possibility of individual reinfection.