Drivers for Rift Valley fever emergence in Mayotte: A Bayesian modelling approach

Rift Valley fever (RVF) is a major zoonotic and arboviral hemorrhagic fever. The conditions leading to RVF epidemics are still unclear, and the relative role of climatic and anthropogenic factors may vary between ecosystems. Here, we estimate the most likely scenario that led to RVF emergence on the island of Mayotte, following the 2006–2007 African epidemic. We developed the first mathematical model for RVF that accounts for climate, animal imports and livestock susceptibility, which is fitted to a 12-years dataset. RVF emergence was found to be triggered by the import of infectious animals, whilst transmissibility was approximated as a linear or exponential function of vegetation density. Model forecasts indicated a very low probability of virus endemicity in 2017, and therefore of re-emergence in a closed system (i.e. without import of infected animals). However, the very high proportion of naive animals reached in 2016 implies that the island remains vulnerable to the import of infectious animals. We recommend reinforcing surveillance in livestock, should RVF be reported is neighbouring territories. Our model should be tested elsewhere, with ecosystem-specific data

Risk mapping for HPAI H5N1 in Africa - Improving surveillance for virulent bird flu: Final report and maps

More than 85 percent of households in rural Africa raise poultry for food, income, or both, and many people live in close contact with their birds. The possibility of an epidemic of highly pathogenic avian influenza (HPAI) H5N1 is therefore a major concern. Since 2006 bird fl u has been introduced into at least 11 countries in Africa, and over 600 outbreaks reported. Vigilance is key to limiting the disease but animal health personnel cannot monitor everywhere at once. This risk-mapping project was designed to help prioritize their efforts by showing in which places outbreaks are more likely to occur. A risk map is a complex, computer-generated image that shows the spatial distribution of the predicted risk of a disease. It is based on the spatial distribution of “risk factors” associated with an increased risk of disease, and the relative importance of each of these factors. In the case of virulent bird fl u, risk factors include major transport routes, markets where poultry may be traded, and wetlands with the possibility of contact between poultry and wild birds. Researchers in this project have prepared risk maps for bird fl u in Africa using multi-criteria decision modeling (MCDM). In this way they have integrated data and information from such diverse sources as published scientific literature, maps available in the public domain, field surveys and expert consultations

Amélioration de la surveillance de l’influenza aviaire de type H5N1 - Cartographie du risque d’influenza aviaire de type H5N1 en Afrique: Rapport final et cartes de risqué d’influenza aviaire

Plus de 85% des ménages ruraux en Afrique élèvent la volaille aux fins d’alimentation, de revenu ou les deux, et de nombreuses personnes vivent en contact étroit avec leurs oiseaux. La possibilité d’une épidémie de l’influenza aviaire hautement pathogène (IAHP) de type H5N1 est donc une grande préoccupation. Depuis 2006, la grippe aviaire est apparue dans au moins 11 pays africains et plus de 600 foyers d’épidémie ont été signalés. La vigilance est essentielle en vue de limiter la maladie mais le personnel de santé animale ne peut faire un suivi partout à la fois. Ce projet de cartographie de facteurs de risques a été conçu en vue d’aider à prioriser leurs efforts en indiquant les lieux où il existe un risque très élevé de flambées de la maladie. La cartographie des risques est une image complexe générée par ordinateur qui montre la répartition spatiale des facteurs de risques prévus d’une maladie. Elle est fondée sur la répartition spatiale des « facteurs de risques » associés au risque accru de maladie et à l’importance relative de chacun de ces facteurs. Dans le cas d’une grippe aviaire de type H5N1, les facteurs de risques sont les principales voies de transport, les marchés de volailles et les points d’eau avec possibilité de contact entre les oiseaux domestiques et sauvages. Pour ce projet, les chercheurs ont préparé des cartes de risques de grippe aviaire en Afrique en utilisant la modélisation de décision multicritères (MCDM). De cette façon, ils ont intégré les données et les informations de diverses sources telles que les publications scientifi ques, les cartes disponibles dans le domaine public, les études de terrain et les consultations d’expert

A hierarchical network approach for modeling Rift Valley fever epidemics with applications in North America

Rift Valley fever is a vector-borne zoonotic disease which causes high morbidity and mortality in livestock. In the event Rift Valley fever virus is introduced to the United States or other non-endemic areas, understanding the potential patterns of spread and the areas at risk based on disease vectors and hosts will be vital for developing mitigation strategies. Presented here is a general network-based mathematical model of Rift Valley fever. Given a lack of empirical data on disease vector species and their vector competence, this discrete time epidemic model uses stochastic parameters following several PERT distributions to model the dynamic interactions between hosts and likely North American mosquito vectors in dispersed geographic areas. Spatial effects and climate factors are also addressed in the model. The model is applied to a large directed asymmetric network of 3,621 nodes based on actual farms to examine a hypothetical introduction to some counties of Texas, an important ranching area in the United States of America (U.S.A.). The nodes of the networks represent livestock farms, livestock markets, and feedlots, and the links represent cattle movements and mosquito diffusion between different nodes. Cattle and mosquito (Aedes and Culex) populations are treated with different contact networks to assess virus propagation. Rift Valley fever virus spread is assessed under various initial infection conditions (infected mosquito eggs, adults or cattle). A surprising trend is fewer initial infectious organisms result in a longer delay before a larger and more prolonged outbreak. The delay is likely caused by a lack of herd immunity while the infections expands geographically before becoming an epidemic involving many dispersed farms and animals almost simultaneously