Innovative Modeling Tool Enhances Worker Safety and Ensures Food Supply Security

Researchers at Cornell University have developed an advanced computer simulation model designed to assist food industry operations in managing COVID-19 outbreaks. The Food Industry COVID-19 Control Tool (FInd CoV Control) enables farm managers and food processing facilities to predict and control the spread of COVID-19 among workers, thereby safeguarding employee health and maintaining the stability of the food supply chain.

This innovative tool requires users to input detailed information about their work environment, including workforce size, age demographics, infection and vaccination histories, physical proximity of employees, and living conditions. Using this data, the model creates a simulation of potential outbreak scenarios, allowing users to evaluate the effectiveness and cost-efficiency of various intervention strategies such as testing regimes, vaccination programs, physical distancing, face coverings, and ventilation improvements.

Professor Renata Ivanek of Cornell’s College of Veterinary Medicine emphasized the flexibility of the tool, noting that it can be adapted to other respiratory diseases in future pandemics. The model's development was based on extensive research and data collection from real-world outbreaks and national databases, including CDC statistics on vaccination and infection rates.

The research team validated the model against five actual COVID-19 outbreaks within food operations, finding a high degree of accuracy in its predictions. This capability to generate thousands of simulated scenarios provides food industry leaders with evidence-based insights for decision-making, reducing reliance on anecdotal information.

Furthermore, the study revealed key insights into intervention strategies: frequent testing combined with prompt isolation can lead to quicker outbreak resolution but incurs higher initial costs and workforce downtime, while less frequent testing might be ineffective and more costly in the long run. Preventive vaccination is most effective when implemented before an outbreak occurs. Additional measures like improved ventilation, physical distancing, and mask-wearing were also confirmed as effective in reducing transmission.

The model specifically targets produce, meat, dairy processing, and fruit and vegetable farming facilities, incorporating data on workforce characteristics, vaccination, and infection rates. The researchers tested the model's predictions against real outbreak data, demonstrating its reliability in guiding public health strategies.

While the current version of FInd CoV Control requires some technical expertise, future developments aim to make it more accessible for industry adoption, with potential applications across various essential sectors. This tool represents a significant advancement in safeguarding worker health and ensuring food security during ongoing and future pandemic scenarios.

Source: https://medicalxpress.com/news/2025-04-tool-worker-health-food-chain.html