Evaluating Multi-Disease Interventions

Abstract

Policymakers who work in the public health sector may rely on the help of quantitative models to support their choice of control strategy against a particular infectious disease. While policymakers have a large number of decision support models to choose from, hardly any of these tools are used to design an intervention strategy that can work well across multiple diseases. This is in part because of the need for large amounts of precise, detailed data and the presence of many unknown or confounding factors, which complicates attempts to make single-disease models, let alone multi-disease models. Is this highly data-intensive and detailed predictive approach necessary to make a good decision about how limited health resources should be allocated against multiple threats? Could the inclusion of multiple pathogens into a single decision support tool change the recommendation of an “optimal” intervention? In the following thesis, a novel multi-disease model is created. Rather than attempting to make a predictive tool to try and foresee a deeply uncertain future, this multi-disease model uses an exploratory approach to systematically evaluate the impacts of uncertain parameters. Many objective optimization techniques are used to find robust intervention strategies that work well for decision-makers who are interested in increasing the impact of their limited resources.