24 July 2021
By Mathilde Griméea, Maria Bekker-Nielsen Dunbarb, Felix Hofmannb and Leonhard Held
We present an approach to extend the Endemic-Epidemic (EE) modelling framework for the analysis of infectious disease data. In its spatiotemporal application, spatial dependencies have originally been captured by a power law applied to static neighbourhood matrices. We propose to adjust these weight matrices over time to reflect changes in spatial connectivity between geographical units. We illustrate this extension by modelling the spread of coronavirus disease 2019 (COVID-19) between Swiss and bordering Italian regions in the first wave of the COVID-19 pandemic. We adjust the spatial weights with data describing the daily changes in population mobility patterns, and indicators of border closures describing the state of travel restrictions since the beginning of the pandemic. We use these time-dependent weights to fit an EE model to the region-stratified time series of new COVID-19 cases. We then adjust the weight matrices to reflect two counterfactual scenarios of border closures and draw counterfactual predictions based on these, to retrospectively assess the usefulness of border closures. We observed that predictions based on a scenario where no closure of the Swiss-Italian border occurred increased the number of cumulative cases in Switzerland by a factor of 2.5 over the study period. Conversely, a closure of the Swiss-Italian border two weeks earlier than implemented would have resulted in only a 12% decrease in the number of cases and merely delayed the epidemic spread by a couple weeks. Despite limitations in the current study, we believe it provides useful insight into modelling the effect of epidemic countermeasures on the spatiotemporal spread of COVID-19.
Read full article: medRxiv