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Evidence for henipavirus spillover into human populations in Africa.

ABSTRACT: Zoonotic transmission of lethal henipaviruses (HNVs) from their natural fruit bat reservoirs to humans has only been reported in Australia and South/Southeast Asia. However, a recent study discovered numerous HNV clades in African bat samples. To determine the potential for HNV spillover events among humans in Africa, here we examine well-curated sets of bat (Eidolon helvum, n = 44) and human (n = 497) serum samples from Cameroon for Nipah virus (NiV) cross-neutralizing antibodies (NiV-X-Nabs). Using a vesicular stomatitis virus (VSV)-based pseudoparticle seroneutralization assay, we detect NiV-X-Nabs in 48% and 3-4% of the bat and human samples, respectively. Seropositive human samples are found almost exclusively in individuals who reported butchering bats for bushmeat. Seropositive human sera also neutralize Hendra virus and Gh-M74a (an African HNV) pseudoparticles, as well as live NiV. Butchering bat meat and living in areas undergoing deforestation are the most significant risk factors associated with seropositivity. Evidence for HNV spillover events warrants increased surveillance efforts.

SUBMITTER: Pernet O

PROVIDER: S-EPMC4237230 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Evidence for henipavirus spillover into human populations in Africa.

Pernet Olivier O Schneider Bradley S BS Beaty Shannon M SM LeBreton Matthew M Yun Tatyana E TE Park Arnold A Zachariah Trevor T TT Bowden Thomas A TA Hitchens Peta P Ramirez Christina M CM Daszak Peter P Mazet Jonna J Freiberg Alexander N AN Wolfe Nathan D ND Lee Benhur B

Nature communications 20141118

Zoonotic transmission of lethal henipaviruses (HNVs) from their natural fruit bat reservoirs to humans has only been reported in Australia and South/Southeast Asia. However, a recent study discovered numerous HNV clades in African bat samples. To determine the potential for HNV spillover events among humans in Africa, here we examine well-curated sets of bat (Eidolon helvum, n = 44) and human (n = 497) serum samples from Cameroon for Nipah virus (NiV) cross-neutralizing antibodies (NiV-X-Nabs). ...[more]

PMID: 25405640

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Project description:Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.

Project description:The increasing global emergence of zoonoses warrants improved awareness of activities that predispose vulnerable communities to greater risk of disease. Zoonotic disease outbreaks regularly occur within Myanmar and at its borders partly due to insufficient knowledge of behavioral risks, hindering participatory surveillance and reporting. This study employed a behavioral surveillance strategy among high-risk populations to understand the behavioral risks for zoonotic disease transmission in an effort to identify risk factors for pathogen spillover. To explore behavioral mechanisms of spillover in Myanmar, we aimed to: (1) evaluate the details around animal contact and types of interaction, (2) assess the association between self-reported unusual symptoms (i.e., any illness or sickness that is not known or recognized in the community or diagnosed by medical providers) and animal contact activities and (3) identify the potential risk factors including behavioral practices of self-reported illness. Participants were enrolled at two community sites: Hpa-An and Hmawbi in Southern Myanmar. A behavioral questionnaire was administered to understand participants' animal exposures, behaviors and self-reported illnesses. From these responses, associations between (1) animal contact activities and self-reported unusual illnesses, and (2) potential risk factors and self-reported unusual illness were tested. Contact with poultry seemed to be very frequent (91.1%) and many participants reported raising, handling and having poultry in their houses as well as slaughtering or being scratched/bitten by them, followed by contact with rodents (57.8%) and swine (17.9%). Compared to participants who did not have any unusual symptoms, participants who had unusual symptoms in the past year were more likely to have sold dead animals (OR = 13.6, 95% CI 6.8-27.2), slaughtered (OR = 2.4, 95% CI 1.7-3.3), raised (OR = 3.4, 95% CI 2.3-5.0) or handled animals (OR = 2.1, 95% CI 1.2-3.6), and had eaten sick (OR = 4.4, 95% CI 3.0-6.4) and/or dead animals (OR = 6.0, 95% CI 4.1-8.8) in the same year. Odds of having reported unusual symptoms was higher among those involved in animal production business (OR = 3.4, 95% CI 1.9-6.2) and animal-involved livelihoods (OR = 3.3, 95% CI 1.5-7.2) compared to other livelihoods. The results suggest that there is a high level of interaction between humans, livestock and wild animals in communities we investigated in Myanmar. The study highlights the specific high-risk behaviors as they relate to animal contact and demographic risk factors for zoonotic spillover. Our findings contribute to human behavioral data needed to develop targeted interventions to prevent zoonotic disease transmission at human-animal interfaces.

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Evidence for henipavirus spillover into human populations in Africa.

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Evidence for henipavirus spillover into human populations in Africa.

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