Interpreting incoherent data in the global spread of SARS-CoV-2 and influenza

Abstract

The SARS-CoV-2 pandemic has shown the threat modern emerging viruses pose to society. Efficient epidemic management requires situational awareness and knowledge of the drivers of virus propagation. Still, data provide an incoherent picture of viral spreading dynamics. I will focus on the global scale and show how models allow combining diverse data sources to reconstruct epidemic patterns and drivers. In particular, I will discuss recent modeling and machine learning works to reconstruct the early global dissemination of the SARS-CoV-2 Alpha variant and identify the drivers of worldwide influenza decline during the COVID-19 pandemic.