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      Hijacking Malaria Simulators with Probabilistic Programming

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          Abstract

          Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria. However, the complicated and stochastic nature of these simulators can mean their output is difficult to interpret, which reduces their usefulness to policymakers. In this paper, we introduce an approach that allows one to treat a large class of population-based epidemiology simulators as probabilistic generative models. This is achieved by hijacking the internal random number generator calls, through the use of a universal probabilistic programming system (PPS). In contrast to other methods, our approach can be easily retrofitted to simulators written in popular industrial programming frameworks. We demonstrate that our method can be used for interpretable introspection and inference, thus shedding light on black-box simulators. This reinstates much-needed trust between policymakers and evidence-based methods.

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          Practical Markov Chain Monte Carlo

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            A Guide to Monte Carlo Simulations in Statistical Physics

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              Probabilistic programming

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                Author and article information

                Journal
                29 May 2019
                Article
                1905.12432
                f1c28d8c-1e57-47b3-a3b9-277ad684d6ed

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                6 pages, 3 figures, Accepted at the International Conference on Machine Learning AI for Social Good Workshop, Long Beach, United States, 2019
                stat.ML cs.LG

                Machine learning,Artificial intelligence
                Machine learning, Artificial intelligence

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