Evidence for henipavirus spillover into human populations in Africa.
Ontology highlight
ABSTRACT:
SUBMITTER: Pernet O
PROVIDER: S-EPMC4237230 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGuinea worm-Dracunculus medinensis-was historically one of the major parasites of humans and has been known since antiquity. Now, Guinea worm is on the brink of eradication, as efforts to interrupt transmission have reduced the annual burden of disease from millions of infections per year in the 1980s to only 54 human cases reported globally in 2019. Despite the enormous success of eradication efforts to date, one complication has arisen. Over the last few years, hundreds of dogs have been found infected with this previously apparently anthroponotic parasite, almost all in Chad. Moreover, the relative numbers of infections in humans and dogs suggests that dogs are currently the principal reservoir on infection and key to maintaining transmission in that country.Principal findingsIn an effort to shed light on this peculiar epidemiology of Guinea worm in Chad, we have sequenced and compared the genomes of worms from dog, human and other animal infections. Confirming previous work with other molecular markers, we show that all of these worms are D. medinensis, and that the same population of worms are causing both infections, can confirm the suspected transmission between host species and detect signs of a population bottleneck due to the eradication efforts. The diversity of worms in Chad appears to exclude the possibility that there were no, or very few, worms present in the country during a 10-year absence of reported cases.ConclusionsThis work reinforces the importance of adequate surveillance of both human and dog populations in the Guinea worm eradication campaign and suggests that control programs aiming to interrupt disease transmission should stay aware of the possible emergence of unusual epidemiology as pathogens approach elimination.
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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.
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Project description:BackgroundNipah virus (NiV) is an emerging disease that causes severe encephalitis and respiratory illness in humans. Pigs were identified as an intermediate host for NiV transmission in Malaysia. In Bangladesh, NiV has caused recognized human outbreaks since 2001 and three outbreak investigations identified an epidemiological association between close contact with sick or dead animals and human illness.MethodologyWe examined cattle and goats reared around Pteropus bat roosts in human NiV outbreak areas. We also tested pig sera collected under another study focused on Japanese encephalitis.Principal findingsWe detected antibodies against NiV glycoprotein in 26 (6.5%) cattle, 17 (4.3%) goats and 138 (44.2%) pigs by a Luminex-based multiplexed microsphere assay; however, these antibodies did not neutralize NiV. Cattle and goats with NiVsG antibodies were more likely to have a history of feeding on fruits partially eaten by bats or birds (PR=3.1, 95% CI 1.6-5.7) and drinking palmyra palm juice (PR=3.9, 95% CI 1.5-10.2).ConclusionsThis difference in test results may be due to the exposure of animals to one or more novel viruses with antigenic similarity to NiV. Further research may identify a novel organism of public health importance.
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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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