Epilocal: a real-time tool for local epidemic monitoring

BACKGROUND The novel coronavirus (SARS-CoV-2) has recently emerged as a global threat. The virus is spreading around the globe at different times and rates. Within a country, such differences provide the opportunity for strategic allocations of health care resources. OBJECTIVE We aim to provide a tool to estimate and visualise differences in the spread of the pandemic at the sub-national level. Specifically, we focus on the case of Italy, a country that has been harshly hit by the virus. METHODS We model the number of SARS-CoV-2 reported cases and deaths as well as the number of hospital admissions at the Italian sub-national level with Poisson regression. We employ parametric and non-parametric functional forms for the hazard function. In the parametric approach, model selection is performed using an automatic criterion based on the statistical significance of the estimated parameters and on goodness-of- fit assessment. In the non-parametric approach, we employ out-of-sample forecasting error minimization. RESULTS For each province and region, fitted models are plotted against observed data, demon- strating the appropriateness of the modelling approach. Moreover, estimated counts and rates of change for each outcome variable are plotted on maps of the country. This provides a direct visual assessment of the geographic distribution of risk areas as well as insights on the evolution of the pandemic over time.