A discrete-time infectious disease model for global pandemics
The current global coronavirus pandemic (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 non-pharmaceutical interventions (1). In addition, increasing global trends in the number of emerging and re-emerging infectious diseases, as evidenced by reported cases of Ebola, Zika, Chikungunya, SARS, West Nile virus and other serious infections, have dramatically increased 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 spanning the public and private sectors (2, 3). This increased demand offers the opportunity to significantly reassess the variety of existing mathematical epidemic models. Such an assessment is an important step in understanding how these models contribute to the understanding of infectious disease surveillance data and the resulting policies, programs and practices (1).
Mathematical models of infectious diseases are powerful tools that are used to expand societal understanding and prediction of disease transmission dynamics and to assess the effects of different interventions and change conditions on the ground for epidemiological outcomes. Thus, it is important that we use the full range of available models and disease data to study disease dynamics. Mathematics…