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dc.contributor.authorCosgun, Ozlem
dc.contributor.authorBuyuktahtakin, Esra
dc.date.accessioned2018-05-10T14:44:01Z
dc.date.available2018-05-10T14:44:01Z
dc.date.issued2018-04
dc.identifier.citationCosgun, Ozlem; Buyuktahtakin, Esra. 2018. Stochastic dynamic resource allocation for HIV prevention and treatment: An approximate dynamic programming approach. Computers & Industrial Engineering, vol. 118:pp 423-439en_US
dc.identifier.issn0360-8352
dc.identifier.otherWOS:000430785500035
dc.identifier.urihttp://dx.doi.org/10.1016/j.cie.2018.01.018
dc.identifier.urihttp://hdl.handle.net/10057/15212
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractHuman immunodeficiency virus (HIV) is a key global health concern, with 33 million people living with HIV worldwide and 2.7 million new infections occurring annually. To prevent the spread of this widely prevalent epidemic disease, prevention and treatment intervention strategies urgently need to be implemented. The goal of this study is to propose stochastic dynamic programming (SDP) and approximate dynamic programming (ADP) algorithms that will optimally allocate the limited intervention budget among the HIV disease compartments and determine the best set of interventions that should be applied to each disease compartment, while minimizing the number of HIV-infected and people diagnosed with acquired immune deficiency syndrome (AIDS) as well as related deaths over a multi-year planning horizon. A compartmental model is constructed and formulated as a nonstationary Markov decision process (MDP) in order to capture the progression of the disease among the highest risk group-African American/black men who have sex with men (BMSM). In order to alleviate the computational difficulties arising from the exponentially growing state space in the SDP, we propose ADP algorithms that determine the approximately optimal budget allocation policies over six years. Our results suggest a greater allocation of the limited budget to prevention measures rather than treatment interventions, such as antiretroviral therapy (ART). As opposed to traditional policies that allocate the budget only once at the beginning of the time horizon, the ADP model suggests using a dynamic proportional budget strategy, allocating the budget dynamically over a multi-period planning period as the uncertainty in disease transmission is revealed. Results show that our ADP approach provides significant increases in health benefits and cost savings in HIV prevention and intervention.en_US
dc.description.sponsorshipNational Science Foundation CAREER Award under Grant No. CBET-1554018.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesComputers & Industrial Engineering;v.118
dc.subjectHIV epidemic diseaseen_US
dc.subjectResource allocationen_US
dc.subjectInterventionsen_US
dc.subjectApproximate dynamic programmingen_US
dc.subjectStochastic dynamic programmingen_US
dc.subjectDisease transmission uncertaintyen_US
dc.subjectMarkov decision process (MDP)en_US
dc.titleStochastic dynamic resource allocation for HIV prevention and treatment: An approximate dynamic programming approachen_US
dc.typeArticleen_US
dc.rights.holder© 2018 Elsevier Ltd. All rights reserved.en_US


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