REACT · Reliable Epidemic monitoring And Control under geographic and demographic heTerogeneities

Reliable Epidemic monitoring And Control under geographic and demographic heTerogeneities

The COVID-19 pandemic highlighted the critical need for robust and timely epidemic monitoring systems. Traditional methods often proved inadequate, unable to accurately predict disease outbreaks or provide robust and actionable insights for policymakers. These limitations stem from the inherent complexities of epidemic dynamics, including nonlinearity and heterogeneity, and the challenges of effectively handling model and data uncertainties. Moreover, the epidemic monitoring process is fraught with uncertainties, as human behavior influences every aspect of an epidemic, from the spread of the disease to the accuracy of daily reported data. This project developed a novel approach to address these shortcomings by integrating physics-informed neural networks (PINNs) with robust system-theoretic tools. PINNs offer a powerful framework for estimating unknown nonlinear functions, such as disease transmission rates, by leveraging both physical models and historical epidemiological data. Combining PINNS with rigorous system-theoretic analysis, the proposed approach enabled more accurate and reliable modeling of epidemic spread, which is essential for effective decision-making. By incorporating a closed-loop structure of nonlinear observers (state estimators), the project created a self-correcting system. Predictions generated by the PINN-based observer are compared to real-time data, allowing for continuous refinement of the model and adaptation to changing circumstances in the epidemic process. For instance, both social adaptations to virus spread and virus mutations affect the disease transmission rates. Therefore, by continuously adapting and timely predicting the changing parameters of epidemic models, the proposed feedback mechanism enhanced the robustness and accuracy of the monitoring system, ensuring that it remains relevant and effective even in the face of complexity and uncertainty. An optimal controller utilizes the information derived from the PINN-based observer to develop evidence-based policy recommendations. By considering socio-economic constraints and geographic and demographic heterogeneities, the controller helped to balance the need for epidemic mitigation with the broader societal and economic impacts of interventions. The project achieved its ultimate goal, which was to develop a comprehensive and scalable framework for epidemic monitoring and control that can be applied to a wide range of infectious diseases. By addressing the limitations of existing approaches, this work has the potential to significantly enhance Europe's capacity to respond to future public health crises and safeguard public health.

Data: CORDIS, © European Union