Dynamics of interdependent epidemics: a physics approach

Abstract

Respiratory viruses are a major source of public health and societal burden. Influenza, rhinovirus, respiratory syncytial virus, among the others, cause seasonal epidemics with peaks in hospitalizations and deaths. In addition, new viruses recurrently emerge leading to devastating pandemics, such as the ongoing SARS-CoV-2 pandemic. Virus spread is the result of multiple factors concurring at different scales – from the microscopic scale of within-host infection mechanisms to the scale of societies and global interconnects. Importantly, viruses do not spread independently. Extensive virological data becoming increasingly available are providing evidence of a complex web of virus-virus interactions [1]. Interactions are highly heterogeneous in their nature and strength. Viruses may compete through cross-immunity - i.e. immunity induced by the infection with one virus may be partially protective against another circulating virus -, or even cooperate in certain cases. Still, these mechanisms are poorly characterised, and their role on the epidemic dynamics, despite being recognised, is far from being understood. For convenience purposes the epidemics caused by each virus are mainly studied separately.

In the present days, the emergence and rapid spread of SARS-CoV-2 is urging for a holistic perspective. The strong preventive measures adopted to mitigate the spread of SARS-CoV-2 have completely altered the seasonal pattern of previously-circulating viruses in a complex and unpredictable way [2]. Certain viruses have been locally suppressed, while others have resurged forming anomalous waves [3,4]. 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 [5,6] to tackle the coupled system of viruses’ dynamical equations, provide fundamental understanding on the role of virus-virus interactions on the dynamics, and enable scalable numerical simulations. The goal is to reconstruct the phase space of possible dynamical regimes and establish the causal link between the microscopical interactions and the macroscopical patterns observed in the data. The student will work on this project combining analytical approaches, Montecarlo simulations and time series analysis of incidence data.

References

  1. Nickbakhsh, S. et al. Virus–virus interactions impact the population dynamics of influenza and the common cold. PNAS 116, 27142–27150 (2019).

  2. Baker, R. E. et al. The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections. PNAS 117, 30547–30553 (2020).

  3. Lumley, S. F. et al. Changes in paediatric respiratory infections at a UK teaching hospital 2016–2021; impact of the SARS-CoV-2 pandemic. Journal of Infection (2021) doi:10.1016/j.jinf.2021.10.022.

  4. E, T. et al. Increased risk of rhinovirus infection in children during the coronavirus disease-19 pandemic. Influenza and other respiratory viruses 15, (2021).

  5. Gog, J. R. & Grenfell, B. T. Dynamics and selection of many-strain pathogens. PNAS 99, 17209–17214 (2002).

  6. Kryazhimskiy, S., Dieckmann, U., Levin, S. A. & Dushoff, J. On State-Space Reduction in Multi-Strain Pathogen Models, with an Application to Antigenic Drift in Influenza A. PLOS Computational Biology 3, e159 (2007).

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Laboratory of Interdisciplinary Physics

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