Simulation Modelling in Public Health: Concepts, Applications and Integration with Artificial Intelligence. A Conceptual and Applied Framework for Prediction, Preparedness, and Decision-Making in Public Health Systems

Abstract

Sandhya Ahuja

Simulation modelling is an important method in public health for understanding complex health problems, testing possible scenarios, and supporting better planning and decision-making. Public health systems often work under uncertainty, especially during outbreaks, climate-related health events, disasters, hospital emergencies, and changing disease patterns. In such situations, simulation modelling helps answer practical “what if” questions without waiting for real-life events to occur. For example, it can help estimate what may happen if air quality worsens, if rainfall increases dengue risk, if vaccination coverage improves, or if hospital admissions suddenly rise during a heat wave or epidemic. This article explains the basic concept of simulation modelling in public health, its major types, and its applications across different public health division’s such as communicable diseases, non- communicable diseases, maternal and child health, immunization, nutrition, climate change and health, hospital preparedness, disaster management, health financing, and disease surveillance. It also explains how simulation models can be developed step by step, starting from defining the public health problem, identifying variables, selecting data sources, making assumptions, running scenarios, validating results, and using the findings for public health action. The article further discusses how artificial intelligence can strengthen simulation modelling by improving prediction, identifying hidden patterns, analyzing large datasets, generating real-time alerts, and supporting decision-making. When simulation modelling is combined with AI, surveillance data, hospital data, climate data, and environmental data, it can become a powerful tool for early warning systems and preparedness planning. This is especially useful for India, where large-scale public health programmers, climate-sensitive diseases, digital surveillance platforms, and hospital-based reporting systems can benefit from predictive and scenario-based analysis. Overall, simulation modelling should not be seen only as a technical or mathematical exercise. It is a practical public health tool that can help move health systems from delayed response to early preparedness, from routine reporting to predictive action, and from isolated data analysis to informed policy and programme planning.

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