A Bayesian approach for monitoring epidemics in presence of undetected cases

Andrea De Simone, Marco Piangerelli

Abstract: One of the key indicators used in tracking the evolution of an infectious disease is the reproduction num- ber. This quantity is usually computed using the reported number of cases, but ignoring that many more individuals may be infected (e.g. asymptomatic carriers). We develop a Bayesian procedure to quantify the impact of undetected infectious cases on the determination of the effective reproduction number. Our approach is stochastic, data-driven and not relying on any compartmental model. It is applied to the COVID-19 outbreak in eight different countries and all Italian regions, showing that the effect of unde- tected cases leads to estimates of the effective reproduction numbers larger than those obtained only with the reported cases by factors ranging from two to ten.