Publication date: Jun 18, 2024

The COVID-19 pandemic exposed challenges of balancing public health and economic goals of infection control in essential industries like food production. To enhance decision-making during future outbreaks, we developed a customizable agent-based model (FInd CoV Control) that predicts and counterfactually compares COVID-19 transmission in a food production operation under various interventions. The model tracks the number of infections as well as economic outcomes (e.g., number of unavailable workers, direct expenses, production losses). The results revealed strong trade-offs between public health and economic impacts of interventions. Temperature screening and virus testing protect public health but have substantial economic downsides. Vaccination, while inexpensive, is too slow as a reactive strategy. Intensive physical distancing and biosafety interventions prove cost-effective. The variability and bimodality in predicted impacts of interventions caution against relying on single-operation real-world data for decision-making. These findings underscore the need for a proactive infrastructure capable of rapidly developing integrated infection-economic mechanistic models to guide infection control, policy-making, and socially acceptable decisions.

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