Bayesian calibration of simulation models for supporting management of the elimination of the macroparasitic disease, Lymphatic Filariasis
Identifieur interne : 000E07 ( Pmc/Curation ); précédent : 000E06; suivant : 000E08Bayesian calibration of simulation models for supporting management of the elimination of the macroparasitic disease, Lymphatic Filariasis
Auteurs : Brajendra K. Singh [États-Unis] ; Edwin Michael [États-Unis]Source :
- Parasites & Vectors [ 1756-3305 ] ; 2015.
Abstract
Mathematical models of parasite transmission can help integrate a large body of information into a consistent framework, which can then be used for gaining mechanistic insights and making predictions. However, uncertainty, spatial variability and complexity, can hamper the use of such models for decision making in parasite management programs.
We have adapted a Bayesian melding framework for calibrating simulation models to address the need for robust modelling tools that can effectively support management of lymphatic filariasis (LF) elimination in diverse endemic settings. We applied this methodology to LF infection and vector biting data from sites across the major LF endemic regions in order to quantify model parameters, and generate reliable predictions of infection dynamics along with credible intervals for modelled output variables. We used the locally calibrated models to estimate breakpoint values for various indicators of parasite transmission, and simulate timelines to parasite extinction as a function of local variations in infection dynamics and breakpoints, and effects of various currently applied and proposed LF intervention strategies.
We demonstrate that as a result of parameter constraining by local data, breakpoint values for all the major indicators of LF transmission varied significantly between the sites investigated. Intervention simulations using the fitted models showed that as a result of heterogeneity in local transmission and extinction dynamics, timelines to parasite elimination in response to the current Mass Drug Administration (MDA) and various proposed MDA with vector control strategies also varied significantly between the study sites. Including vector control, however, markedly reduced the duration of interventions required to achieve elimination as well as decreased the risk of recrudescence following stopping of MDA.
We have demonstrated how a Bayesian data-model assimilation framework can enhance the use of transmission models for supporting reliable decision making in the management of LF elimination. Extending this framework for delivering predictions in settings either lacking or with only sparse data to inform the modelling process, however, will require development of procedures to estimate and use spatio-temporal variations in model parameters and inputs directly, and forms the next stage of the work reported here.
The online version of this article (doi:10.1186/s13071-015-1132-7) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s13071-015-1132-7
PubMed: 26490350
PubMed Central: 4618871
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Bayesian calibration of simulation models for supporting management of the elimination of the macroparasitic disease, Lymphatic Filariasis</title><author><name sortKey="Singh, Brajendra K" sort="Singh, Brajendra K" uniqKey="Singh B" first="Brajendra K." last="Singh">Brajendra K. Singh</name><affiliation wicri:level="2"><nlm:aff id="Aff1">Department of Biological Sciences, University of Notre Dame, Notre Dame, IN USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Indiana</region></placeName><wicri:cityArea>Department of Biological Sciences, University of Notre Dame, Notre Dame</wicri:cityArea></affiliation></author><author><name sortKey="Michael, Edwin" sort="Michael, Edwin" uniqKey="Michael E" first="Edwin" last="Michael">Edwin Michael</name><affiliation wicri:level="2"><nlm:aff id="Aff1">Department of Biological Sciences, University of Notre Dame, Notre Dame, IN USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Indiana</region></placeName><wicri:cityArea>Department of Biological Sciences, University of Notre Dame, Notre Dame</wicri:cityArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">26490350</idno><idno type="pmc">4618871</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4618871</idno><idno type="RBID">PMC:4618871</idno><idno type="doi">10.1186/s13071-015-1132-7</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">000E08</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000E08</idno><idno type="wicri:Area/Pmc/Curation">000E07</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000E07</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Bayesian calibration of simulation models for supporting management of the elimination of the macroparasitic disease, Lymphatic Filariasis</title><author><name sortKey="Singh, Brajendra K" sort="Singh, Brajendra K" uniqKey="Singh B" first="Brajendra K." last="Singh">Brajendra K. Singh</name><affiliation wicri:level="2"><nlm:aff id="Aff1">Department of Biological Sciences, University of Notre Dame, Notre Dame, IN USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Indiana</region></placeName><wicri:cityArea>Department of Biological Sciences, University of Notre Dame, Notre Dame</wicri:cityArea></affiliation></author><author><name sortKey="Michael, Edwin" sort="Michael, Edwin" uniqKey="Michael E" first="Edwin" last="Michael">Edwin Michael</name><affiliation wicri:level="2"><nlm:aff id="Aff1">Department of Biological Sciences, University of Notre Dame, Notre Dame, IN USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Indiana</region></placeName><wicri:cityArea>Department of Biological Sciences, University of Notre Dame, Notre Dame</wicri:cityArea></affiliation></author></analytic><series><title level="j">Parasites & Vectors</title><idno type="eISSN">1756-3305</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Mathematical models of parasite transmission can help integrate a large body of information into a consistent framework, which can then be used for gaining mechanistic insights and making predictions. However, uncertainty, spatial variability and complexity, can hamper the use of such models for decision making in parasite management programs.</p></sec><sec><title>Methods</title><p>We have adapted a Bayesian melding framework for calibrating simulation models to address the need for robust modelling tools that can effectively support management of lymphatic filariasis (LF) elimination in diverse endemic settings. We applied this methodology to LF infection and vector biting data from sites across the major LF endemic regions in order to quantify model parameters, and generate reliable predictions of infection dynamics along with credible intervals for modelled output variables. We used the locally calibrated models to estimate breakpoint values for various indicators of parasite transmission, and simulate timelines to parasite extinction as a function of local variations in infection dynamics and breakpoints, and effects of various currently applied and proposed LF intervention strategies.</p></sec><sec><title>Results</title><p>We demonstrate that as a result of parameter constraining by local data, breakpoint values for all the major indicators of LF transmission varied significantly between the sites investigated. Intervention simulations using the fitted models showed that as a result of heterogeneity in local transmission and extinction dynamics, timelines to parasite elimination in response to the current Mass Drug Administration (MDA) and various proposed MDA with vector control strategies also varied significantly between the study sites. Including vector control, however, markedly reduced the duration of interventions required to achieve elimination as well as decreased the risk of recrudescence following stopping of MDA.</p></sec><sec><title>Conclusions</title><p>We have demonstrated how a Bayesian data-model assimilation framework can enhance the use of transmission models for supporting reliable decision making in the management of LF elimination. Extending this framework for delivering predictions in settings either lacking or with only sparse data to inform the modelling process, however, will require development of procedures to estimate and use spatio-temporal variations in model parameters and inputs directly, and forms the next stage of the work reported here.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s13071-015-1132-7) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Anderson, Rm" uniqKey="Anderson R">RM Anderson</name></author><author><name sortKey="May, Rm" uniqKey="May R">RM May</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Stolk, Wa" uniqKey="Stolk W">WA Stolk</name></author><author><name sortKey="Swaminathan, S" uniqKey="Swaminathan S">S Swaminathan</name></author><author><name 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Parasit Vectors</journal-id><journal-id journal-id-type="iso-abbrev">Parasit Vectors</journal-id><journal-title-group><journal-title>Parasites & Vectors</journal-title></journal-title-group><issn pub-type="epub">1756-3305</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">26490350</article-id><article-id pub-id-type="pmc">4618871</article-id><article-id pub-id-type="publisher-id">1132</article-id><article-id pub-id-type="doi">10.1186/s13071-015-1132-7</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research</subject></subj-group></article-categories><title-group><article-title>Bayesian calibration of simulation models for supporting management of the elimination of the macroparasitic disease, Lymphatic Filariasis</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Singh</surname><given-names>Brajendra K.</given-names></name><address><email>bsingh1@nd.edu</email></address><xref ref-type="aff" rid="Aff1"></xref></contrib><contrib contrib-type="author"><name><surname>Michael</surname><given-names>Edwin</given-names></name><address><email>emichael@nd.edu</email></address><xref ref-type="aff" rid="Aff1"></xref></contrib><aff id="Aff1">Department of Biological Sciences, University of Notre Dame, Notre Dame, IN USA</aff></contrib-group><pub-date pub-type="epub"><day>22</day><month>10</month><year>2015</year></pub-date><pub-date pub-type="pmc-release"><day>22</day><month>10</month><year>2015</year></pub-date><pub-date pub-type="collection"><year>2015</year></pub-date><volume>8</volume><elocation-id>522</elocation-id><history><date date-type="received"><day>15</day><month>9</month><year>2015</year></date><date date-type="accepted"><day>2</day><month>10</month><year>2015</year></date></history><permissions><copyright-statement>© Singh and Michael. 2015</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold>This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p>Mathematical models of parasite transmission can help integrate a large body of information into a consistent framework, which can then be used for gaining mechanistic insights and making predictions. However, uncertainty, spatial variability and complexity, can hamper the use of such models for decision making in parasite management programs.</p></sec><sec><title>Methods</title><p>We have adapted a Bayesian melding framework for calibrating simulation models to address the need for robust modelling tools that can effectively support management of lymphatic filariasis (LF) elimination in diverse endemic settings. We applied this methodology to LF infection and vector biting data from sites across the major LF endemic regions in order to quantify model parameters, and generate reliable predictions of infection dynamics along with credible intervals for modelled output variables. We used the locally calibrated models to estimate breakpoint values for various indicators of parasite transmission, and simulate timelines to parasite extinction as a function of local variations in infection dynamics and breakpoints, and effects of various currently applied and proposed LF intervention strategies.</p></sec><sec><title>Results</title><p>We demonstrate that as a result of parameter constraining by local data, breakpoint values for all the major indicators of LF transmission varied significantly between the sites investigated. Intervention simulations using the fitted models showed that as a result of heterogeneity in local transmission and extinction dynamics, timelines to parasite elimination in response to the current Mass Drug Administration (MDA) and various proposed MDA with vector control strategies also varied significantly between the study sites. Including vector control, however, markedly reduced the duration of interventions required to achieve elimination as well as decreased the risk of recrudescence following stopping of MDA.</p></sec><sec><title>Conclusions</title><p>We have demonstrated how a Bayesian data-model assimilation framework can enhance the use of transmission models for supporting reliable decision making in the management of LF elimination. Extending this framework for delivering predictions in settings either lacking or with only sparse data to inform the modelling process, however, will require development of procedures to estimate and use spatio-temporal variations in model parameters and inputs directly, and forms the next stage of the work reported here.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s13071-015-1132-7) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Vector-borne neglected tropical diseases</kwd><kwd>Lymphatic filariasis</kwd><kwd>Parasite transmission heterogeneity</kwd><kwd>Uncertainty</kwd><kwd>Ecological complexity</kwd><kwd>Transmission models</kwd><kwd>Infection breakpoints</kwd><kwd>Indicator dynamics</kwd><kwd>Data-model assimilation</kwd><kwd>Bayesian melding</kwd><kwd>Mass drug administration</kwd><kwd>Vector control</kwd><kwd>Extinction dynamics</kwd><kwd>Decision support</kwd><kwd>Parasite elimination programmes</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2015</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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