Dynamics of interdependent epidemics: a physics approach.

Every year respiratory viruses such as influenza, respiratory syncytial virus, and human parainfluenza virus cause seasonal epidemics with peaks in hospitalizations and deaths. The SARS-CoV-2 pandemic has altered this seasonal pattern due to the severe non pharmaceutical interventions implemented against it [1]. However, with the relaxation of the interventions against SARS-CoV-2, the circulation of the other viruses is rising again, thus the understanding of the dynamics of interdependent epidemics remains critical. The cocirculation of multiple viruses is the result of factors concurring at different scales: from the microscopic scale of within-host infection mechanisms to the scale of the human encounters and mobility [2]. Extensive virological data becoming increasingly available are providing evidence of a complex network of virus-virus interactions [3]. For instance, viruses may compete through cross-immunity - i.e. immunity induced by the infection with one virus may be partially protective against another circulating virus. Still, the extent of this interaction is not clear, and its role in the epidemic dynamics is far from being understood. For convenience purposes the epidemics caused by each virus are mainly studied separately. However a proper accounting for virus-virus interactions becomes essential to understand the interdependent epidemics and anticipate their future course. This requires a new approximate theory beyond current approaches [4] to tackle the coupled system of viruses’ dynamical equations and enable scalable numerical simulations. This work introduces a new multi-pathogen dynamical model accounting for the competitive interaction between pathogens. The aim of the project is to explore the phase space of possible dynamical regimes. Understanding gained by numerical simulations will be backed up by theoretical considerations. Model trajectories will be compared with patterns observed in the real data. References: [1] E, T. et al. Increased risk of rhinovirus infection in children during the coronavirus disease-19 pandemic. Influenza and other respiratory viruses 15, (2021). [2] Opatowski L, et al. Influenza interaction with cocirculating pathogens and its impact on surveillance, pathogenesis, and epidemic profile: A key role for mathematical modeling. PLOS Pathogens 14(2): e1006770 (2018). [3] Nickbakhsh, S. et al. Virus–virus interactions impact the population dynamics of influenza and the common cold. PNAS 116, 27142–27150 (2019). [4] Kryazhimskiy S, et al. On State-Space Reduction in Multi-Strain Pathogen Models, with an Application to Antigenic Drift in Influenza A. PLOS Computational Biology 3, e159 (2007).