Mathematical models are critical tools to characterize COVID-19 dynamics and take action accordingly. We identified 4 major challenges associated with the current modeling paradigm (SEIR) that hinder the efforts to accurately characterize the emerging COVID-19 and future epidemics. These challenges included (1) lack of consistent definition of "case"; (2) discrepancy between patient-level clinical insights and population-level modeling efforts; (3) lack of adequate inclusion of individual behavioral and social influence; and (4) allowing little flexibility of including new evidence and insights when our knowledge evolved rapidly during the pandemic. Therefore, these challenges made the current SEIR modeling paradigm less practical to handle the complex COVID-19 and future pandemics. Novel and more reliable data sources and alternative modeling paradigms are needed to address these issues.
Keywords: COVID-19; SEIR model; challenges; mathematical model.
© The Author(s) 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America.