Decoding Early COVID-19 Responses Using Mathematical Modeling of Social Distancing Strategies Across Multiple Countries

During the early phase of the COVID-19 pandemic, before vaccines were available, countries implemented diverse combinations of social-distancing (SD) measures shaped by cultural norms and public acceptance. These differences produced varied outcomes in controlling viral spread, offering lessons for future health-crisis preparedness. We performed a reverse-engineering analysis of early COVID-19 case data from five socioculturally distinct countries—India, Vietnam, Italy, Finland, and the United States—selected to capture a range of initial prevalence levels. Using mathematical modeling and data fitting, we inferred the relative contributions of three SD interventions: face masking, quarantine, and isolation of infected individuals. We then employed an efficiency framework to compare epidemiological impact and cost-effectiveness, integrating metrics for infection reduction and economic efficiency. Our analysis shows that heavy reliance on quarantine is suboptimal; instead, a balanced and well-coordinated combination of masking and isolation provides superior infection control and resource efficiency, informing future pandemic-response strategies.