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- W4313001981 abstract "Screening tests for infectious diseases is a problem typically addressed in the field of Medicine and Epidemics. However, as the SARS-CoV-2 pandemic emerged, it became clear that there is no globally accepted strategy for optimizing such procedures, e.g. in international transportation and border checks, which policy makers can employ. In this study, the general problem of developing optimal testing strategies for infectious diseases is explored under the scope of Game Theory, sampling and estimation methods from classic Statistics, as well as Bayesian methods for the proper treatment of posterior updates, leading to the benefits of employing Machine Learning for data-driven structural risk minimization. Six main guidelines are established by this work, dictating estimated variance of prevalence and associated risk as the main minimization target, in terms of both a criterion for inflow quotas allocation between population groups, as well as optimal posterior updates via classic confidence intervals and Bayesian methods. As a result, it is established that minimum infection risk, not optimal resource allocation, is the real challenge and top priority in formalizing optimal screening strategies for such risk mitigation policies." @default.
- W4313001981 created "2023-01-05" @default.
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- W4313001981 date "2022-01-01" @default.
- W4313001981 modified "2023-09-27" @default.
- W4313001981 title "Optimal Testing Strategies for Infectious Diseases" @default.
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- W4313001981 doi "https://doi.org/10.1007/978-3-031-10031-4_8" @default.
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