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1.
Int J Infect Dis ; 131: 57-64, 2023 Jun.
Article in English | MEDLINE | ID: covidwho-2258519

ABSTRACT

BACKGROUND: Sarbecoviruses are a subgenus of Coronaviridae that mostly infect bats with known potential to infect humans (SARS-CoV and SARS-CoV-2). Populations in Southeast Asia, where these viruses are most likely to emerge, have been undersurveyed to date. METHODS: We surveyed communities engaged in extractive industries and bat guano harvesting from rural areas in Myanmar. Participants were screened for exposure to sarbecoviruses, and their interactions with wildlife were evaluated to determine the factors associated with exposure to sarbecoviruses. RESULTS: Of 693 people screened between July 2017 and February 2020, 12.1% were seropositive for sarbecoviruses. Individuals were significantly more likely to have been exposed to sarbecoviruses if their main livelihood involved working in extractive industries (logging, hunting, or harvesting of forest products; odds ratio [OR] = 2.71, P = 0.019) or had been hunting/slaughtering bats (OR = 6.09, P = 0.020). Exposure to a range of bat and pangolin sarbecoviruses was identified. CONCLUSION: Exposure to diverse sarbecoviruses among high-risk human communities provides epidemiologic and immunologic evidence that zoonotic spillover is occurring. These findings inform risk mitigation efforts needed to decrease disease transmission at the bat-human interface, as well as future surveillance efforts warranted to monitor isolated populations for viruses with pandemic potential.


Subject(s)
COVID-19 , Chiroptera , Severe acute respiratory syndrome-related coronavirus , Animals , Humans , Animals, Wild , SARS-CoV-2 , COVID-19/epidemiology , Zoonoses , Phylogeny
2.
Lancet Glob Health ; 10(4): e579-e584, 2022 04.
Article in English | MEDLINE | ID: covidwho-1740333

ABSTRACT

The COVID-19 pandemic has underscored the need to strengthen national surveillance systems to protect a globally connected world. In low-income and middle-income countries, zoonotic disease surveillance has advanced considerably in the past two decades. However, surveillance efforts often prioritise urban and adjacent rural communities. Communities in remote rural areas have had far less support despite having routine exposure to zoonotic diseases due to frequent contact with domestic and wild animals, and restricted access to health care. Limited disease surveillance in remote rural areas is a crucial gap in global health security. Although this point has been made in the past, practical solutions on how to implement surveillance efficiently in these resource-limited and logistically challenging settings have yet to be discussed. We highlight why investing in disease surveillance in remote rural areas of low-income and middle-income countries will benefit the global community and review current approaches. Using semi-arid regions in Kenya as a case study, we provide a practical approach by which surveillance in remote rural areas can be strengthened and integrated into existing systems. This Viewpoint represents a transition from simply highlighting the need for a more holistic approach to disease surveillance to a solid plan for how this outcome might be achieved.


Subject(s)
COVID-19 , Global Health , Developing Countries , Humans , Pandemics , Poverty
4.
Proc Natl Acad Sci U S A ; 118(15)2021 04 13.
Article in English | MEDLINE | ID: covidwho-1171893

ABSTRACT

The death toll and economic loss resulting from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic are stark reminders that we are vulnerable to zoonotic viral threats. Strategies are needed to identify and characterize animal viruses that pose the greatest risk of spillover and spread in humans and inform public health interventions. Using expert opinion and scientific evidence, we identified host, viral, and environmental risk factors contributing to zoonotic virus spillover and spread in humans. We then developed a risk ranking framework and interactive web tool, SpillOver, that estimates a risk score for wildlife-origin viruses, creating a comparative risk assessment of viruses with uncharacterized zoonotic spillover potential alongside those already known to be zoonotic. Using data from testing 509,721 samples from 74,635 animals as part of a virus discovery project and public records of virus detections around the world, we ranked the spillover potential of 887 wildlife viruses. Validating the risk assessment, the top 12 were known zoonotic viruses, including SARS-CoV-2. Several newly detected wildlife viruses ranked higher than known zoonotic viruses. Using a scientifically informed process, we capitalized on the recent wealth of virus discovery data to systematically identify and prioritize targets for investigation. The publicly accessible SpillOver platform can be used by policy makers and health scientists to inform research and public health interventions for prevention and rapid control of disease outbreaks. SpillOver is a living, interactive database that can be refined over time to continue to improve the quality and public availability of information on viral threats to human health.


Subject(s)
COVID-19 , Communicable Diseases, Emerging , Pandemics , SARS-CoV-2 , Zoonoses , Animals , COVID-19/epidemiology , COVID-19/transmission , Communicable Diseases, Emerging/epidemiology , Communicable Diseases, Emerging/transmission , Humans , Zoonoses/epidemiology , Zoonoses/transmission
5.
PLoS Negl Trop Dis ; 15(2): e0009143, 2021 02.
Article in English | MEDLINE | ID: covidwho-1097205

ABSTRACT

A majority of emerging infectious diseases (EIDs) are zoonotic, mainly caused through spillover events linked to human-animal interactions. We conducted a survey-based human behavioral study in Laikipia County, Kenya, which is characterized by a dynamic human-wildlife-livestock interface. Questionnaires that assessed human-animal interactions, sanitation, and illnesses experienced within the past year were distributed to 327 participants among five communities in Laikipia. This study aimed to 1) describe variation in reported high-risk behaviors by community type and 2) assess the relationship between specific behaviors and self-reported illnesses. Behavioral trends were assessed in R via Fisher's exact tests. A generalized linear mixed model with Lasso penalization (GLMMLasso) was used to assess correlations between behaviors and participants' self-reported illness within the past year, with reported behaviors as independent variables and reported priority symptoms as the outcome. Reported behaviors varied significantly among the study communities. Participants from one community (Pastoralist-1) were significantly more likely to report eating a sick animal in the past year (p< 0.001), collecting an animal found dead to sell in the past year (p<0.0001), and not having a designated location for human waste (p<0.0001) when compared to participants from other communities. The GLMMLasso revealed that reports of an ill person in the household in the past year was significantly associated with self-reported illness. Sixty-eight percent of participants reported that bushmeat is available within the communities. Our study demonstrates community-level variation in behaviors that may influence zoonotic pathogen exposure. We further recommend development of targeted studies that explore behavioral variations among land use systems in animal production contexts.


Subject(s)
Communicable Diseases, Emerging , Zoonoses , Adolescent , Animals , Animals, Wild , Child , Female , Humans , Kenya , Livestock , Male , Risk Factors , Sanitation , Surveys and Questionnaires
6.
PLoS One ; 15(4): e0230802, 2020.
Article in English | MEDLINE | ID: covidwho-46041

ABSTRACT

The recent emergence of bat-borne zoonotic viruses warrants vigilant surveillance in their natural hosts. Of particular concern is the family of coronaviruses, which includes the causative agents of severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and most recently, Coronavirus Disease 2019 (COVID-19), an epidemic of acute respiratory illness originating from Wuhan, China in December 2019. Viral detection, discovery, and surveillance activities were undertaken in Myanmar to identify viruses in animals at high risk contact interfaces with people. Free-ranging bats were captured, and rectal and oral swabs and guano samples collected for coronaviral screening using broadly reactive consensus conventional polymerase chain reaction. Sequences from positives were compared to known coronaviruses. Three novel alphacoronaviruses, three novel betacoronaviruses, and one known alphacoronavirus previously identified in other southeast Asian countries were detected for the first time in bats in Myanmar. Ongoing land use change remains a prominent driver of zoonotic disease emergence in Myanmar, bringing humans into ever closer contact with wildlife, and justifying continued surveillance and vigilance at broad scales.


Subject(s)
Chiroptera/virology , Coronavirus/classification , Coronavirus/isolation & purification , Anal Canal/virology , Animals , Coronavirus/genetics , Feces/virology , Mouth/virology , Myanmar , Population Surveillance
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