Stochastic network to model the global spreading of respiratory diseases: From SARS-CoV-2 to pathogen X pandemic

Abstract

The recent COVID-19 pandemic has underscored the vulnerability of global health systems. Emerging in November 2019 in Hubei, China, COVID-19 has had far-reaching consequences, affecting every corner of the globe. The impact has been particularly severe, causing widespread collapse of public health systems and contraction of the world economy. The imposition of stringent sanitary restrictions by the majority of countries, in response to SARS-CoV-2, disrupted various economic sectors on a massive scale. The existing gap between developed and underdeveloped countries further complicates the global scenario, raising uncertainties. This concern is amplified when considering the potential threat of other infectious diseases with dynamics akin to SARS-CoV-2, such as a new recombining H5N1 flu strain. Such a strain, if easily transmissible among humans, could lead to another pandemic. In this study, we introduce a stochastic network model designed to assess control strategies on a global scale. This model enables us to project how new variants, evading immunity, might respond to either a coordinated global response from governments or a complete lack of coordination. Our connectivity model between countries is based on a network of contacts derived from actual commercial air connectivity data. The disease dynamics within each country are simulated using a population-based approach with differential equations. The epidemiological model is fine-tuned using real SARS-CoV-2 data reported by various countries from 2019 to 2023.