Abstract
During an epidemic, it can be difficult to get an estimate of the actual number of people infected at any given time. This is due to multiple reasons, including some cases being asymptomatic and sick people not seeking healthcare for mild symptoms, among others. Large scale random sampling of the population for testing can be expensive, especially in the early stages of an epidemic, when tests are scarce. Here we show how an adaptive prevalence testing method can be developed to obtain a good estimate of the disease burden by learning to intelligently allocate a small number of tests for random testing of the population. Our approach uses a combination of an agent-based simulation and deep learning in an active sensing paradigm. We show that it is possible to get a good state estimate with relatively minimal prevalence testing, and that the trained system adapts quickly and performs well even if the disease parameters change.
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Acknowledgements
This work was supported by NSF Expeditions in Computing grant CCF-1918656, Virginia Department of Health award UVABIO610-GY23, University of Virginia Strategic Investment Fund award number SIF160, and the Global Infectious Diseases Institute grant “Machine Learning Efficient Behavioral Interventions for Novel Epidemics” at the University of Virginia. Our code is available at https://github.com/NSSAC/Active-Sensing-for-Epidemic-State-Estimation.
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Saliba, S., Dadgostari, F., Hoops, S., Mortveit, H.S., Swarup, S. (2024). Active Sensing for Epidemic State Estimation Using ABM-Guided Machine Learning. In: Nardin, L.G., Mehryar, S. (eds) Multi-Agent-Based Simulation XXIV. MABS 2023. Lecture Notes in Computer Science(), vol 14558. Springer, Cham. https://doi.org/10.1007/978-3-031-61034-9_3
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DOI: https://doi.org/10.1007/978-3-031-61034-9_3
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