Generalized Markov models of infectious disease spread: A novel framework for developing dynamic health policies
► We consider dynamic programming (DP) for the optimal control of infectious spreads. ► We discuss major limitations of using existing infectious disease models to inform dynamic decision - making. ► We propose a class of models which can be employed by DP or approximate DP. ► We demonstrate the abi...
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| Veröffentlicht in: | European journal of operational research 2011-12, Vol.215 (3), p.679-687 |
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| container_title | European journal of operational research |
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| creator | Yaesoubi, Reza Cohen, Ted |
| description | ► We consider dynamic programming (DP) for the optimal control of infectious spreads. ► We discuss major limitations of using existing infectious disease models to inform dynamic decision - making. ► We propose a class of models which can be employed by DP or approximate DP. ► We demonstrate the ability of these models to fit data from an emerging epidemic.
We propose a class of mathematical models for the transmission of infectious diseases in large populations. This class of models, which generalizes the existing discrete-time Markov chain models of infectious diseases, is compatible with efficient dynamic optimization techniques to assist real-time selection and modification of public health interventions in response to evolving epidemiological situations and changing availability of information and medical resources. While retaining the strength of existing classes of mathematical models in their ability to represent the within-host natural history of disease and between-host transmission dynamics, the proposed models possess two advantages over previous models: (1) these models can be used to generate optimal dynamic health policies for controlling spreads of infectious diseases, and (2) these models are able to approximate the spread of the disease in relatively large populations with a limited state space size and computation time. |
| doi_str_mv | 10.1016/j.ejor.2011.07.016 |
| format | Article |
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We propose a class of mathematical models for the transmission of infectious diseases in large populations. This class of models, which generalizes the existing discrete-time Markov chain models of infectious diseases, is compatible with efficient dynamic optimization techniques to assist real-time selection and modification of public health interventions in response to evolving epidemiological situations and changing availability of information and medical resources. While retaining the strength of existing classes of mathematical models in their ability to represent the within-host natural history of disease and between-host transmission dynamics, the proposed models possess two advantages over previous models: (1) these models can be used to generate optimal dynamic health policies for controlling spreads of infectious diseases, and (2) these models are able to approximate the spread of the disease in relatively large populations with a limited state space size and computation time.</description><identifier>ISSN: 0377-2217</identifier><identifier>EISSN: 1872-6860</identifier><identifier>DOI: 10.1016/j.ejor.2011.07.016</identifier><identifier>PMID: 21966083</identifier><identifier>CODEN: EJORDT</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Applied sciences ; Biological and medical sciences ; Discrete-time Markov chain ; Dynamic health policy ; Dynamic programming ; Epidemics ; Epidemiology ; Exact sciences and technology ; General aspects ; Health care policy ; Infectious disease models ; Infectious disease models Dynamic health policy Discrete-time Markov chain Dynamic programming Epidemiology ; Infectious diseases ; Markov analysis ; Mathematical models ; Mathematical programming ; Mathematics ; Medical sciences ; Operational research and scientific management ; Operational research. Management science ; Optimization techniques ; Probability and statistics ; Probability theory and stochastic processes ; Public health. Hygiene ; Public health. Hygiene-occupational medicine ; Reliability theory. Replacement problems ; Sciences and techniques of general use ; Special processes (renewal theory, markov renewal processes, semi-markov processes, statistical mechanics type models, applications) ; Studies</subject><ispartof>European journal of operational research, 2011-12, Vol.215 (3), p.679-687</ispartof><rights>2011 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Elsevier Sequoia S.A. Dec 16, 2011</rights><rights>2011 Elsevier B.V. All rights reserved. 2011</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c677t-8726a51c4002e2902ed57d04c7def9c431e64bfd25fc8d82cc658766ae1a0a353</citedby><cites>FETCH-LOGICAL-c677t-8726a51c4002e2902ed57d04c7def9c431e64bfd25fc8d82cc658766ae1a0a353</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ejor.2011.07.016$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,777,781,882,3537,3994,27905,27906,45976</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24509219$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21966083$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttp://econpapers.repec.org/article/eeeejores/v_3a215_3ay_3a2011_3ai_3a3_3ap_3a679-687.htm$$DView record in RePEc$$Hfree_for_read</backlink></links><search><creatorcontrib>Yaesoubi, Reza</creatorcontrib><creatorcontrib>Cohen, Ted</creatorcontrib><title>Generalized Markov models of infectious disease spread: A novel framework for developing dynamic health policies</title><title>European journal of operational research</title><addtitle>Eur J Oper Res</addtitle><description>► We consider dynamic programming (DP) for the optimal control of infectious spreads. ► We discuss major limitations of using existing infectious disease models to inform dynamic decision - making. ► We propose a class of models which can be employed by DP or approximate DP. ► We demonstrate the ability of these models to fit data from an emerging epidemic.
We propose a class of mathematical models for the transmission of infectious diseases in large populations. This class of models, which generalizes the existing discrete-time Markov chain models of infectious diseases, is compatible with efficient dynamic optimization techniques to assist real-time selection and modification of public health interventions in response to evolving epidemiological situations and changing availability of information and medical resources. While retaining the strength of existing classes of mathematical models in their ability to represent the within-host natural history of disease and between-host transmission dynamics, the proposed models possess two advantages over previous models: (1) these models can be used to generate optimal dynamic health policies for controlling spreads of infectious diseases, and (2) these models are able to approximate the spread of the disease in relatively large populations with a limited state space size and computation time.</description><subject>Applied sciences</subject><subject>Biological and medical sciences</subject><subject>Discrete-time Markov chain</subject><subject>Dynamic health policy</subject><subject>Dynamic programming</subject><subject>Epidemics</subject><subject>Epidemiology</subject><subject>Exact sciences and technology</subject><subject>General aspects</subject><subject>Health care policy</subject><subject>Infectious disease models</subject><subject>Infectious disease models Dynamic health policy Discrete-time Markov chain Dynamic programming Epidemiology</subject><subject>Infectious diseases</subject><subject>Markov analysis</subject><subject>Mathematical models</subject><subject>Mathematical programming</subject><subject>Mathematics</subject><subject>Medical sciences</subject><subject>Operational research and scientific management</subject><subject>Operational research. 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Replacement problems</topic><topic>Sciences and techniques of general use</topic><topic>Special processes (renewal theory, markov renewal processes, semi-markov processes, statistical mechanics type models, applications)</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yaesoubi, Reza</creatorcontrib><creatorcontrib>Cohen, Ted</creatorcontrib><collection>Pascal-Francis</collection><collection>PubMed</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>European journal of operational research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yaesoubi, Reza</au><au>Cohen, Ted</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generalized Markov models of infectious disease spread: A novel framework for developing dynamic health policies</atitle><jtitle>European journal of operational research</jtitle><addtitle>Eur J Oper Res</addtitle><date>2011-12-16</date><risdate>2011</risdate><volume>215</volume><issue>3</issue><spage>679</spage><epage>687</epage><pages>679-687</pages><issn>0377-2217</issn><eissn>1872-6860</eissn><coden>EJORDT</coden><abstract>► We consider dynamic programming (DP) for the optimal control of infectious spreads. ► We discuss major limitations of using existing infectious disease models to inform dynamic decision - making. ► We propose a class of models which can be employed by DP or approximate DP. ► We demonstrate the ability of these models to fit data from an emerging epidemic.
We propose a class of mathematical models for the transmission of infectious diseases in large populations. This class of models, which generalizes the existing discrete-time Markov chain models of infectious diseases, is compatible with efficient dynamic optimization techniques to assist real-time selection and modification of public health interventions in response to evolving epidemiological situations and changing availability of information and medical resources. While retaining the strength of existing classes of mathematical models in their ability to represent the within-host natural history of disease and between-host transmission dynamics, the proposed models possess two advantages over previous models: (1) these models can be used to generate optimal dynamic health policies for controlling spreads of infectious diseases, and (2) these models are able to approximate the spread of the disease in relatively large populations with a limited state space size and computation time.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><pmid>21966083</pmid><doi>10.1016/j.ejor.2011.07.016</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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| source | RePEc; Elsevier ScienceDirect Journals |
| subjects | Applied sciences Biological and medical sciences Discrete-time Markov chain Dynamic health policy Dynamic programming Epidemics Epidemiology Exact sciences and technology General aspects Health care policy Infectious disease models Infectious disease models Dynamic health policy Discrete-time Markov chain Dynamic programming Epidemiology Infectious diseases Markov analysis Mathematical models Mathematical programming Mathematics Medical sciences Operational research and scientific management Operational research. Management science Optimization techniques Probability and statistics Probability theory and stochastic processes Public health. Hygiene Public health. Hygiene-occupational medicine Reliability theory. Replacement problems Sciences and techniques of general use Special processes (renewal theory, markov renewal processes, semi-markov processes, statistical mechanics type models, applications) Studies |
| title | Generalized Markov models of infectious disease spread: A novel framework for developing dynamic health policies |
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