Using dynamical modelling and inference to understand the abrupt change in invasive meningococcal disease spread during and after COVID-19 pandemic restrictions in Italy

The unprecedented social restrictions enacted in response to the COVID-19 pandemic have caused a decline in the circulation of several infectious diseases - including influenza, Respiratory syncytial virus, and invasive meningococcal disease, among others. Following the relaxation of the restrictions, infections have risen again with an altered diffusion pattern, which is hard to decipher and predict. This abrupt change in the regular diffusion dynamics of these infections is hindering public-health preparedness and response. The student will focus on invasive meningococcal disease and analyze the drop and resurgence of this infection by fitting a population dynamics model to available data on the number of cases reported in Italy. The thesis requires the design and implementation of the dynamical model and the implementation of the fitting algorithm based on the Markov Chain Monte Carlo Maximum likelihood technique. The goal of the work will be to identify the drivers of the resurgence dynamics in order to aid the anticipation of the future course of the infection.