Optimising Tuberculosis Screening Coverage in a Regional Hospital in Thailand: A Multi-Agent Simulation Approach
Abstract
Objective: To assess the impact of tuberculosis (TB) screening coverage on infection risks using a multi-agent simulation (MAS) model, based on a case study of a private hospital in northeastern Thailand.
Material and Methods: A hospital-based multi-agent simulation to evaluate TB transmission across 6 screening coverage levels (0%–100%) during peak and off-peak hours was developed. The model was informed by 200 outpatient observations and hospital data collected between October and December 2024 at a private hospital in northeastern Thailand. A risk matrix was constructed to assess clinical, financial, and reputational outcomes based on scenario-based analysis and stakeholder interviews.
Results: The simulation demonstrated that reduced screening coverage substantially increased the risk of TB transmission, with a more pronounced effect during peak hours. At full coverage, infection rates were 0.61 percent during peak hours and 0.30 percent during off-peak hours. In the absence of screening, these rates increased to 5.50 percent and 0.51 percent, respectively. The higher transmission risk during peak hours reflects the influence of increased patient density and interaction. The risk matrix indicated that limited screening during peak hours led to more severe clinical, reputational, and financial consequences than during off-peak periods.
Conclusion: The findings support the use of MAS as a dynamic tool for evaluating TB screening strategies under real-world constraints. The model highlights how adaptive screening policies, especially during peak hours, can reduce transmission risk and support operational decision-making in resource-limited hospital settings.
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