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Identifying cost‐effective dynamic policies to control epidemics
Author(s) -
Yaesoubi Reza,
Cohen Ted
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7047
Subject(s) - computer science , context (archaeology) , population , epidemic control , lift (data mining) , operations research , psychological intervention , risk analysis (engineering) , business , covid-19 , medicine , engineering , data mining , environmental health , infectious disease (medical specialty) , disease , paleontology , pathology , psychiatry , biology
We describe a mathematical decision model for identifying dynamic health policies for controlling epidemics. These dynamic policies aim to select the best current intervention based on accumulating epidemic data and the availability of resources at each decision point. We propose an algorithm to approximate dynamic policies that optimize the population's net health benefit, a performance measure which accounts for both health and monetary outcomes. We further illustrate how dynamic policies can be defined and optimized for the control of a novel viral pathogen, where a policy maker must decide (i) when to employ or lift a transmission‐reducing intervention (e.g. school closure) and (ii) how to prioritize population members for vaccination when a limited quantity of vaccines first become available. Within the context of this application, we demonstrate that dynamic policies can produce higher net health benefit than more commonly described static policies that specify a pre‐determined sequence of interventions to employ throughout epidemics. Copyright © 2016 John Wiley & Sons, Ltd.