A stochastic model to study rift valley fever persistence with different seasonal patterns of vector abundance: New insights on the endemicity in the tropical island of Mayotte
Rift Valley fever (RVF) is a zoonotic vector-borne disease causing abortion storms in cattle and human epidemics in Africa. Our aim was to evaluate RVF persistence in a seasonal and isolated population and to apply it to Mayotte Island (Indian Ocean), where the virus was still silently circulating four years after its last known introduction in 2007. We proposed a stochastic model to estimate RVF persistence over several years and under four seasonal patterns of vector abundance. Firstly, the model predicted a wide range of virus spread pat- terns, from obligate persistence in a constant or tropical environment (without needing verti- cal transmission or reintroduction) to frequent extinctions in a drier climate. We then identified for each scenario of seasonality the parameters that most influenced prediction variations. Persistence was sensitive to vector lifespan and biting rate in a tropical climate, and to host viraemia duration and vector lifespan in a drier climate. The first epizootic peak was primarily sensitive to viraemia duration and thus likely to be controlled by vaccination, whereas subsequent peaks were sensitive to vector lifespan and biting rate in a tropical cli- mate, and to host birth rate and viraemia duration in arid climates. Finally, we parameterized the model according to Mayotte known environment. Mosquito captures estimated the abundance of eight potential RVF vectors. Review of RVF competence studies on these species allowed adjusting transmission probabilities per bite. Ruminant serological data since 2004 and three new cross-sectional seroprevalence studies are presented. Transmis- sion rates had to be divided by more than five to best fit observed data. Five years after introduction, RVF persisted in more than 10% of the simulations, even under this scenario of low transmission. Hence, active surveillance must be maintained to better understand the risk related to RVF persistence and to prevent new introductions.
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dig-cirad-fr-577272 |
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L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques L72 - Organismes nuisibles des animaux Virus de la fièvre de la vallée du Rift épidémiologie ruminant modèle mathématique modèle de simulation facteur climatique variation saisonnière facteur du milieu transmission des maladies surveillance épidémiologique contrôle de maladies vecteur de maladie dynamique des populations Culicidae population animale sérologie distribution spatiale étude de cas fièvre de la Vallée du Rift http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_6695 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_29554 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_2594 http://aims.fao.org/aos/agrovoc/c_2329 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_2016 http://aims.fao.org/aos/agrovoc/c_435 http://aims.fao.org/aos/agrovoc/c_27081 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_b08d44fd http://aims.fao.org/aos/agrovoc/c_4665 http://aims.fao.org/aos/agrovoc/c_3081 L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques L72 - Organismes nuisibles des animaux Virus de la fièvre de la vallée du Rift épidémiologie ruminant modèle mathématique modèle de simulation facteur climatique variation saisonnière facteur du milieu transmission des maladies surveillance épidémiologique contrôle de maladies vecteur de maladie dynamique des populations Culicidae population animale sérologie distribution spatiale étude de cas fièvre de la Vallée du Rift http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_6695 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_29554 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_2594 http://aims.fao.org/aos/agrovoc/c_2329 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_2016 http://aims.fao.org/aos/agrovoc/c_435 http://aims.fao.org/aos/agrovoc/c_27081 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_b08d44fd http://aims.fao.org/aos/agrovoc/c_4665 http://aims.fao.org/aos/agrovoc/c_3081 |
spellingShingle |
L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques L72 - Organismes nuisibles des animaux Virus de la fièvre de la vallée du Rift épidémiologie ruminant modèle mathématique modèle de simulation facteur climatique variation saisonnière facteur du milieu transmission des maladies surveillance épidémiologique contrôle de maladies vecteur de maladie dynamique des populations Culicidae population animale sérologie distribution spatiale étude de cas fièvre de la Vallée du Rift http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_6695 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_29554 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_2594 http://aims.fao.org/aos/agrovoc/c_2329 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_2016 http://aims.fao.org/aos/agrovoc/c_435 http://aims.fao.org/aos/agrovoc/c_27081 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_b08d44fd http://aims.fao.org/aos/agrovoc/c_4665 http://aims.fao.org/aos/agrovoc/c_3081 L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques L72 - Organismes nuisibles des animaux Virus de la fièvre de la vallée du Rift épidémiologie ruminant modèle mathématique modèle de simulation facteur climatique variation saisonnière facteur du milieu transmission des maladies surveillance épidémiologique contrôle de maladies vecteur de maladie dynamique des populations Culicidae population animale sérologie distribution spatiale étude de cas fièvre de la Vallée du Rift http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_6695 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_29554 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_2594 http://aims.fao.org/aos/agrovoc/c_2329 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_2016 http://aims.fao.org/aos/agrovoc/c_435 http://aims.fao.org/aos/agrovoc/c_27081 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_b08d44fd http://aims.fao.org/aos/agrovoc/c_4665 http://aims.fao.org/aos/agrovoc/c_3081 Cavalerie, Lisa Charron, Maud Ezanno, Pauline Dommergues, Laure Zumbo, Betty Cardinale, Eric A stochastic model to study rift valley fever persistence with different seasonal patterns of vector abundance: New insights on the endemicity in the tropical island of Mayotte |
description |
Rift Valley fever (RVF) is a zoonotic vector-borne disease causing abortion storms in cattle and human epidemics in Africa. Our aim was to evaluate RVF persistence in a seasonal and isolated population and to apply it to Mayotte Island (Indian Ocean), where the virus was still silently circulating four years after its last known introduction in 2007. We proposed a stochastic model to estimate RVF persistence over several years and under four seasonal patterns of vector abundance. Firstly, the model predicted a wide range of virus spread pat- terns, from obligate persistence in a constant or tropical environment (without needing verti- cal transmission or reintroduction) to frequent extinctions in a drier climate. We then identified for each scenario of seasonality the parameters that most influenced prediction variations. Persistence was sensitive to vector lifespan and biting rate in a tropical climate, and to host viraemia duration and vector lifespan in a drier climate. The first epizootic peak was primarily sensitive to viraemia duration and thus likely to be controlled by vaccination, whereas subsequent peaks were sensitive to vector lifespan and biting rate in a tropical cli- mate, and to host birth rate and viraemia duration in arid climates. Finally, we parameterized the model according to Mayotte known environment. Mosquito captures estimated the abundance of eight potential RVF vectors. Review of RVF competence studies on these species allowed adjusting transmission probabilities per bite. Ruminant serological data since 2004 and three new cross-sectional seroprevalence studies are presented. Transmis- sion rates had to be divided by more than five to best fit observed data. Five years after introduction, RVF persisted in more than 10% of the simulations, even under this scenario of low transmission. Hence, active surveillance must be maintained to better understand the risk related to RVF persistence and to prevent new introductions. |
format |
article |
topic_facet |
L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques L72 - Organismes nuisibles des animaux Virus de la fièvre de la vallée du Rift épidémiologie ruminant modèle mathématique modèle de simulation facteur climatique variation saisonnière facteur du milieu transmission des maladies surveillance épidémiologique contrôle de maladies vecteur de maladie dynamique des populations Culicidae population animale sérologie distribution spatiale étude de cas fièvre de la Vallée du Rift http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_6695 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_29554 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_2594 http://aims.fao.org/aos/agrovoc/c_2329 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_2016 http://aims.fao.org/aos/agrovoc/c_435 http://aims.fao.org/aos/agrovoc/c_27081 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_b08d44fd http://aims.fao.org/aos/agrovoc/c_4665 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Cavalerie, Lisa Charron, Maud Ezanno, Pauline Dommergues, Laure Zumbo, Betty Cardinale, Eric |
author_facet |
Cavalerie, Lisa Charron, Maud Ezanno, Pauline Dommergues, Laure Zumbo, Betty Cardinale, Eric |
author_sort |
Cavalerie, Lisa |
title |
A stochastic model to study rift valley fever persistence with different seasonal patterns of vector abundance: New insights on the endemicity in the tropical island of Mayotte |
title_short |
A stochastic model to study rift valley fever persistence with different seasonal patterns of vector abundance: New insights on the endemicity in the tropical island of Mayotte |
title_full |
A stochastic model to study rift valley fever persistence with different seasonal patterns of vector abundance: New insights on the endemicity in the tropical island of Mayotte |
title_fullStr |
A stochastic model to study rift valley fever persistence with different seasonal patterns of vector abundance: New insights on the endemicity in the tropical island of Mayotte |
title_full_unstemmed |
A stochastic model to study rift valley fever persistence with different seasonal patterns of vector abundance: New insights on the endemicity in the tropical island of Mayotte |
title_sort |
stochastic model to study rift valley fever persistence with different seasonal patterns of vector abundance: new insights on the endemicity in the tropical island of mayotte |
url |
http://agritrop.cirad.fr/577272/ http://agritrop.cirad.fr/577272/1/577272.pdf |
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spelling |
dig-cirad-fr-5772722024-01-28T22:51:11Z http://agritrop.cirad.fr/577272/ http://agritrop.cirad.fr/577272/ A stochastic model to study rift valley fever persistence with different seasonal patterns of vector abundance: New insights on the endemicity in the tropical island of Mayotte. Cavalerie Lisa, Charron Maud, Ezanno Pauline, Dommergues Laure, Zumbo Betty, Cardinale Eric. 2015. PloS One, 10 (7):e0130838, 26 p.https://doi.org/10.1371/journal.pone.0130838 <https://doi.org/10.1371/journal.pone.0130838> A stochastic model to study rift valley fever persistence with different seasonal patterns of vector abundance: New insights on the endemicity in the tropical island of Mayotte Cavalerie, Lisa Charron, Maud Ezanno, Pauline Dommergues, Laure Zumbo, Betty Cardinale, Eric eng 2015 PloS One L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques L72 - Organismes nuisibles des animaux Virus de la fièvre de la vallée du Rift épidémiologie ruminant modèle mathématique modèle de simulation facteur climatique variation saisonnière facteur du milieu transmission des maladies surveillance épidémiologique contrôle de maladies vecteur de maladie dynamique des populations Culicidae population animale sérologie distribution spatiale étude de cas fièvre de la Vallée du Rift http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_6695 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_29554 http://aims.fao.org/aos/agrovoc/c_24894 http://aims.fao.org/aos/agrovoc/c_2594 http://aims.fao.org/aos/agrovoc/c_2329 http://aims.fao.org/aos/agrovoc/c_16411 http://aims.fao.org/aos/agrovoc/c_2327 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_2016 http://aims.fao.org/aos/agrovoc/c_435 http://aims.fao.org/aos/agrovoc/c_27081 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_b08d44fd Mayotte France http://aims.fao.org/aos/agrovoc/c_4665 http://aims.fao.org/aos/agrovoc/c_3081 Rift Valley fever (RVF) is a zoonotic vector-borne disease causing abortion storms in cattle and human epidemics in Africa. Our aim was to evaluate RVF persistence in a seasonal and isolated population and to apply it to Mayotte Island (Indian Ocean), where the virus was still silently circulating four years after its last known introduction in 2007. We proposed a stochastic model to estimate RVF persistence over several years and under four seasonal patterns of vector abundance. Firstly, the model predicted a wide range of virus spread pat- terns, from obligate persistence in a constant or tropical environment (without needing verti- cal transmission or reintroduction) to frequent extinctions in a drier climate. We then identified for each scenario of seasonality the parameters that most influenced prediction variations. Persistence was sensitive to vector lifespan and biting rate in a tropical climate, and to host viraemia duration and vector lifespan in a drier climate. The first epizootic peak was primarily sensitive to viraemia duration and thus likely to be controlled by vaccination, whereas subsequent peaks were sensitive to vector lifespan and biting rate in a tropical cli- mate, and to host birth rate and viraemia duration in arid climates. Finally, we parameterized the model according to Mayotte known environment. Mosquito captures estimated the abundance of eight potential RVF vectors. Review of RVF competence studies on these species allowed adjusting transmission probabilities per bite. Ruminant serological data since 2004 and three new cross-sectional seroprevalence studies are presented. Transmis- sion rates had to be divided by more than five to best fit observed data. Five years after introduction, RVF persisted in more than 10% of the simulations, even under this scenario of low transmission. Hence, active surveillance must be maintained to better understand the risk related to RVF persistence and to prevent new introductions. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/577272/1/577272.pdf text Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1371/journal.pone.0130838 10.1371/journal.pone.0130838 info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0130838 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1371/journal.pone.0130838 |