Real World Data-Driven Agent-Based Modeling for Health Policy Insights During Epidemics

Pandemics and epidemics pose significant threats due to their rapid global spread, overwhelming healthcare systems, and causing high mortality and morbidity rates. The recent COVID-19 pandemic has underscored the urgent need for advanced tools to predict and manage infectious disease transmission in complex populations. While Agent-Based Models (ABMs) have become crucial in this context, many existing models oversimplify simulation environments and often fail to consider disease transmission through public transportation. In this study, we propose a framework that integrates a realistic environment and transportation model into the ABM, enhancing its accuracy in simulating airborne disease spread. Our framework is designed to be adaptable and generalizable, making it suitable for modeling various diseases. As a case study, we simulate COVID-19 transmission in the Kandy District, demonstrating how incorporating a realistic environment and transportation network improves the model's ability to reflect real-world conditions. This approach provides a more precise tool for disease propagation simulations within ABMs, offering valuable insights for public health strategies.