Regional probabilistic situational awareness and forecasting of COVID-19

Abstract

Mathematical models and statistical inference are fundamental for surveillance and control of the COVID-19 pandemic. Several aspects cause regional heterogeneity in disease spread. Individual behaviour, mobility, viral variants and transmission vary locally, temporally and with the season, and interventions and vaccination are often implemented regionally. Therefore, we developed a new regional changepoint stochastic SEIR metapopulation model. The model is informed by real-time mobility estimates from mobile phone data, laboratory-confirmed cases, and hospitalisation incidence. To estimate locally and time-varying transmissibility, case detection probabilities, and missed imported cases, we present a new sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, suitable for real-time surveillance. By comparing the reproduction number before and after lockdown, we find a national transmissibility reduction of 85% (95% CI 78%-89%). The estimated effect varied regionally and was larger for the most populated regions than in the national average.