Bayesian Spatio-temporal prediction and counterfactual generation: an application in non-pharmaceutical interventions in Covid-19.

The spatio-temporal course of an epidemic (such as Covid-19) can be significantly affected by non-pharmaceutical interventions (NPIs), such as full or partial lockdowns. Bayesian Susceptible-Infected-Removed (SIR) models can be applied to the spatio-temporal spread of infectious disease (STIF) (such as Covid-19). In causal inference it is classically of interest to investigate counterfactuals. In the context of STIF it is possible to use nowcasting to assess the possible counterfactual realization of disease in incidence that would have been evidenced with no NPI. Classic lagged dependency spatio-temporal IF models will be discussed and the importance of the ST component in nowcasting will be assessed. The real example of lockdowns for Covid-19 in two US states during 2020 and 2021 is provided. The degeneracy in prediction in longer time periods is highlighted and the wide confidence intervals characterize the forecasts.