Public health decision making with stochastic individual-based models: a translational framework driven by advances in health economics, model inference and reinforcement learning (ACCELERATE) (ACCELERATE)

This project proposes a methodological framework in the context of respiratory pathogens with pandemic potential, based on historical data of SARS-CoV-2. Clustered social contact patterns have been pivotal in combination with stochasticity to explain disease spread and heterogeneous behaviour. Therefore, we focus on mathematical models that accommodate heterogeneity in infection acquisition and additional randomness at the individual level. However, estimation of key epidemiological parameters based on stochastic and computationally intensive individual-based models is challenging. Especially when we focus on multiple outcomes, which is required when evaluating the health economic impact of preventive measures. A coarse-grained cost-effectiveness analysis is possible through individual-based modelling, yet complicated by a cascade of uncertainties and stochasticity in the underlying disease process. The availability of options to define the most (cost-)effective scenario requires multi-criteria selection techniques. Machine learning methods have been proven useful for this, however, this complex modelling context requires progressive algorithms. Informing the decision making process is particularly challenging in an epidemic setting with unexpected events such as the emergence of new variants of concern. We aim to accelerate decision making in the next pandemic with a public health framework grounded in advanced statistics, health economics and computer sciences.

Keywords:INFERENCE, HEALTH ECONOMICS, INFECTIOUS DISEASES, ARTIFICIAL INTELLIGENCE (AI)

Disciplines:Machine learning and decision making, Modelling and simulation, Health economy, Health promotion and policy, Biostatistics

Project type:Collaboration project