A discrete-time infectious disease model for global pandemics

Abstract

The ongoing global pandemic of coronavirus (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has raised concerns about the effectiveness of current preventive pharmaceutical and nonpharmaceutical interventions (1). In addition, the upward global trends in the numbers of emerging and reemerging infectious diseases, as evidenced by the reported cases of Ebola, Zika, Chikungunya, SARS, West Nile virus, and other serious infections, have dramatically expanded the demand for mathematical models of infectious diseases across multiple entities that include the pharmaceutical industry, health and medical organizations, and local and international governments, and that span the public and private sectors (2, 3). With this increased demand comes the opportunity to meaningfully reassess the variety of existing mathematical epidemic models. Such an assessment is an important step in capturing the ways in which these models contribute to the understanding of infectious disease surveillance data and the policies, programs, and practices that emerge from these data (1). Mathematical models of infectious diseases are powerful tools that are used in extending societal understanding and forecasting of disease transmission dynamics and for evaluating the effects of different interventions and changing on-the-ground conditions for epidemiological outcomes. Thus, it is important that we make use of the full range of the available models and disease data to study disease dynamics. Mathematical … [↵][1]1 Email: ayakubu{at}howard.edu. [1]: #xref-corresp-1-1