On the use of aggregated human mobility data to estimate the reproduction number.

The reproduction number of an infectious disease, such as CoViD-19, can be described through a modified version of the susceptible-infected-recovered (SIR) model with time-dependent contact rate, where mobility data are used as proxy of average movement trends and interpersonal distances. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 in terms of aggregated individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.

A phenomenological estimate of the true scale of CoViD-19 from primary data.

Estimation of the prevalence of undocumented SARS-CoV-2 infections is critical for understanding the overall impact of CoViD-19, and for implementing effective public policy intervention strategies. We discuss a simple yet effective approach to estimate the true number of people infected by SARS-CoV-2, using raw epidemiological data reported by official health institutions in the largest EU countries and the USA.