Decoding Early COVID-19 Responses Using Mathematical Modeling of Social Distancing Strategies Across Multiple Countries
Primary Investigator (PI) Name
Asma Azizi
Department
CSM – Molecular and Cellular Biology
Abstract
During the early stages of the COVID-19 pandemic, before vaccines were available, countries implemented diverse combinations of social distancing (SD) measures, reflecting distinct cultural norms and levels of public acceptance. These variations produced different outcomes in controlling viral spread, offering valuable lessons for improving preparedness for future health crises. In this study, we conducted a reverse engineering analysis of early COVID-19 case data from five countries—India, Vietnam, Italy, Finland, and the United States—chosen to represent diverse sociocultural contexts and initial prevalence levels. Using mathematical modeling and data fitting, we inferred the proportions in which each nation applied three key SD interventions: face masking, quarantine, and isolation of infected individuals. We then applied an efficiency framework to compare their epidemiological impact and cost-effectiveness, integrating metrics for infection reduction and economic efficiency. Our results indicate that relying heavily on a single SD measure is suboptimal; instead, a balanced combination of masking, quarantine, and isolation yields the highest joint epidemiological and economic benefits. This comparative analysis underscores that moderate, well-coordinated applications of multiple non-pharmaceutical interventions can maximize both infection control and resource efficiency—an insight critical for pandemic response planning.
Disciplines
Applied Mathematics | Public Health
Decoding Early COVID-19 Responses Using Mathematical Modeling of Social Distancing Strategies Across Multiple Countries
During the early stages of the COVID-19 pandemic, before vaccines were available, countries implemented diverse combinations of social distancing (SD) measures, reflecting distinct cultural norms and levels of public acceptance. These variations produced different outcomes in controlling viral spread, offering valuable lessons for improving preparedness for future health crises. In this study, we conducted a reverse engineering analysis of early COVID-19 case data from five countries—India, Vietnam, Italy, Finland, and the United States—chosen to represent diverse sociocultural contexts and initial prevalence levels. Using mathematical modeling and data fitting, we inferred the proportions in which each nation applied three key SD interventions: face masking, quarantine, and isolation of infected individuals. We then applied an efficiency framework to compare their epidemiological impact and cost-effectiveness, integrating metrics for infection reduction and economic efficiency. Our results indicate that relying heavily on a single SD measure is suboptimal; instead, a balanced combination of masking, quarantine, and isolation yields the highest joint epidemiological and economic benefits. This comparative analysis underscores that moderate, well-coordinated applications of multiple non-pharmaceutical interventions can maximize both infection control and resource efficiency—an insight critical for pandemic response planning.