Bayesian calibration of simulation models for supporting management of the elimination of the macroparasitic disease, Lymphatic Filariasis
Identifieur interne : 002089 ( Main/Curation ); précédent : 002088; suivant : 002090Bayesian 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.
Descripteurs français
- KwdFr :
- Animaux, Calibrage, Filariose lymphatique (), Filariose lymphatique (traitement médicamenteux), Filariose lymphatique (transmission), Filariose lymphatique (épidémiologie), Humains, Modèles théoriques, Simulation numérique, Théorème de Bayes, Vecteurs insectes (parasitologie), Vecteurs insectes (physiologie).
- MESH :
- parasitologie : Vecteurs insectes.
- physiologie : Vecteurs insectes.
- traitement médicamenteux : Filariose lymphatique.
- épidémiologie : Filariose lymphatique.
- Animaux, Calibrage, Filariose lymphatique, Humains, Modèles théoriques, Simulation numérique, Théorème de Bayes.
English descriptors
- KwdEn :
- Animals, Bayes Theorem, Calibration, Computer Simulation, Elephantiasis, Filarial (drug therapy), Elephantiasis, Filarial (epidemiology), Elephantiasis, Filarial (prevention & control), Elephantiasis, Filarial (transmission), Humans, Insect Vectors (parasitology), Insect Vectors (physiology), Models, Theoretical.
- MESH :
- drug therapy : Elephantiasis, Filarial.
- epidemiology : Elephantiasis, Filarial.
- parasitology : Insect Vectors.
- physiology : Insect Vectors.
- prevention & control : Elephantiasis, Filarial.
- transmission : Elephantiasis, Filarial.
- Animals, Bayes Theorem, Calibration, Computer Simulation, Humans, Models, Theoretical.
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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PMC:4618871Le document en format XML
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<author><name sortKey="Singh, Brajendra K" sort="Singh, Brajendra K" uniqKey="Singh B" first="Brajendra K." last="Singh">Brajendra K. Singh</name>
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<series><title level="j">Parasites & Vectors</title>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term>
<term>Bayes Theorem</term>
<term>Calibration</term>
<term>Computer Simulation</term>
<term>Elephantiasis, Filarial (drug therapy)</term>
<term>Elephantiasis, Filarial (epidemiology)</term>
<term>Elephantiasis, Filarial (prevention & control)</term>
<term>Elephantiasis, Filarial (transmission)</term>
<term>Humans</term>
<term>Insect Vectors (parasitology)</term>
<term>Insect Vectors (physiology)</term>
<term>Models, Theoretical</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Animaux</term>
<term>Calibrage</term>
<term>Filariose lymphatique ()</term>
<term>Filariose lymphatique (traitement médicamenteux)</term>
<term>Filariose lymphatique (transmission)</term>
<term>Filariose lymphatique (épidémiologie)</term>
<term>Humains</term>
<term>Modèles théoriques</term>
<term>Simulation numérique</term>
<term>Théorème de Bayes</term>
<term>Vecteurs insectes (parasitologie)</term>
<term>Vecteurs insectes (physiologie)</term>
</keywords>
<keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>Elephantiasis, Filarial</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Elephantiasis, Filarial</term>
</keywords>
<keywords scheme="MESH" qualifier="parasitologie" xml:lang="fr"><term>Vecteurs insectes</term>
</keywords>
<keywords scheme="MESH" qualifier="parasitology" xml:lang="en"><term>Insect Vectors</term>
</keywords>
<keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Vecteurs insectes</term>
</keywords>
<keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Insect Vectors</term>
</keywords>
<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Elephantiasis, Filarial</term>
</keywords>
<keywords scheme="MESH" qualifier="traitement médicamenteux" xml:lang="fr"><term>Filariose lymphatique</term>
</keywords>
<keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Elephantiasis, Filarial</term>
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<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Filariose lymphatique</term>
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<term>Bayes Theorem</term>
<term>Calibration</term>
<term>Computer Simulation</term>
<term>Humans</term>
<term>Models, Theoretical</term>
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<term>Calibrage</term>
<term>Filariose lymphatique</term>
<term>Humains</term>
<term>Modèles théoriques</term>
<term>Simulation numérique</term>
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<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>
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