Cost-benefit of Influenza Vaccinations in Frontline Workers: A Dynamic Modelling Study

Influenza significantly impacts public health, particularly among the elderly and those with underlying health conditions, but it also imposes substantial economic and operational burdens on the working-age population. This study introduces a novel machine learning-based Susceptible-Infected-Recovered (SIR) model solved as an agent-based model (ABM), designed to dynamically simulate influenza spread and assess the cost-benefit of vaccination programs specifically for frontline workers. Unlike traditional models, our approach accounts for the diverse contact rates and risk profiles across different job types, offering a more granular and accurate prediction of influenzas impact on workforce productivity. We utilised historical influenza data from the CDC and WHO/FluMart to model the effects of varying vaccination coverage levels on infections, sick days, and associated costs within a typical workplace. The results demonstrate that higher vaccination coverage significantly reduces both the total number of infections and the peak sickness levels, leading to substantial cost savings. Additionally, higher vaccination coverage was associated with a significantly lower peak in sickness, mitigating periods of high absenteeism and operational disruptions. The model highlights the economic advantages of vaccination programs, particularly for sectors with higher salaries and absenteeism rates. It also underscores the importance of targeting frontline workers, who have higher contact rates and contribute more significantly to influenza transmission. This models ability to capture the dynamic nature of influenza transmission and its differential effects on various work types represents a significant advancement over previous static models. It provides a robust tool for organisations to optimise vaccination strategies, ensuring business continuity and enhancing productivity during influenza seasons.