SIMULATION-BASED DISCOVERY OF ROBUST ALGORITHMS FOR TARGETING OF INFECTIOUS DISEASE SCREENING AND INTERVENTION

In settings with a high burden of infectious diseases, such as Tuberculosis (TB), there is a growing need for tools that can help public health professionals find and treat cases more quickly and effectively. In this project, we will be continuing development of a spatiotemporal simulation model of coupled household and community TB in a high-incidence setting. In our initial work on this project, we have used this model to compare different intervention scenarios that take advantage of information on the spatial location and contact networks of TB cases to target interventions. In the next phase of this project, we plan to use this simulation platform as a tool for developing adaptive interventions that can respond to changing epidemiological conditions, i.e. a sharp rise in incidence indicative of an outbreak, and modify screening and intervention strategies to deal with the types of heterogeneity that makes tackling real-world infectious disease problems highly challenging.