About: Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus incidence in West Africa

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About: Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus incidence in West Africa Goto Sponge NotDistinct Permalink

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus incidence in West Africa
Creator	Monagin, Corina Fichet-Calvet, Elisabeth Basinski, Andrew Barry, Peter Nuismer, Scott Sjodin, Anna Bird, Brian Jarvis, Michael Remien, Christopher Varrelman, Tanner Wolking, David Gessler, Paul Ghersi, Bruno Layman, Nathan
source	BioRxiv
abstract	Forecasting how the risk of pathogen spillover changes over space is essential for the effective deployment of interventions such as human or wildlife vaccination. However, due to the sporadic nature of spillover events and limited availability of data, developing and validating robust predictions is challenging. Recent efforts to overcome this obstacle have capitalized on machine learning to predict spillover risk. Past approaches combine infection data from both humans and reservoir to train models that assess risk across broad geographical regions. In doing so, these models blend data sources that separately describe pathogen risk posed by the reservoir and the realized rate of spillover into the human population. We develop a novel approach that models as separate stages: 1) the contributions of spillover risk from the reservoir and pathogen distribution, and 2) the resulting incidence of pathogen in the human population. Our methodology allows for a rigorous assessment of whether forecasts of spillover risk can reliably predict the realized spillover rate into humans, as measured by seroprevalence. In addition to providing a rigorous cross-validation of risk predictions, this methodology could shed light on human habits that modulate or amplify the resultant spillover. We apply our method to Lassa virus, a zoonotic pathogen that poses a high threat of emergence in West Africa. The resulting framework is the first forecast to quantify the extent to which predictions of spillover risk from the reservoir explain regional variation in human seroprevalence. We use predictions generated by the model to revise existing estimates for the annual number of new human Lassa infections. Our model predicts that between 935,200 – 3,928,000 humans are infected by Lassa virus each year, an estimate that exceeds current conventional wisdom. Author Summary The 2019 emergence of SARS-2 coronavirus is a grim reminder of the threat animal-borne pathogens pose to human health. Even prior to SARS-2, the spillover of so-called zoonotic pathogens was a persistent problem, with pathogens such as Ebola and Lassa regularly but unpredictably causing outbreaks. Machine-learning models that can anticipate when and where animal-to-human virus transmission is most likely to occur could help guide surveillance effort, as well as preemptive countermeasures to pandemics, like information campaigns or vaccination programs. We develop a novel machine learning framework that uses data-sets describing the distribution of a virus within its host and the range of its animal host, along with human immunity data, to infer rates of animal-to-human transmission across a focal region. By training the model on data from the animal host, our framework allows rigorous validation of spillover predictions on human data. We apply our framework to Lassa fever, a viral disease of West Africa that is spread to humans by rodents, and update estimates of symptomatic and asymptomatic Lassa virus infections in humans. Our results suggest that Nigeria is most at risk for the emergence of new strains of Lassa virus, and therefore should be prioritized for outbreak-surveillance.
has issue date	2020-07-15(xsd:dateTime)
bibo:doi	10.1101/2020.03.05.979658
has license	biorxiv
sha1sum (hex)	cb279b981b5ad7eb433467ffe16b08dd909c5bf3
schema:url	https://doi.org/10.1101/2020.03.05.979658
resource representing a document's title	Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus incidence in West Africa
schema:publication	bioRxiv
resource representing a document's body	covid:cb279b981b5ad7eb433467ffe16b08dd909c5bf3#body_text
is schema:about of	named entity 'geographical' named entity 'Once' named entity 'Lassa virus' named entity 'trained' named entity 'ecology' named entity 'causative' named entity 'sampling' named entity '2009' named entity 'host' named entity 'process' named entity 'region' named entity 'learned' named entity 'formally' named entity 'models' named entity 'coordinates' named entity 'case reports' named entity 'negative sense' named entity 'animal' named entity 'Machine learning' named entity 'For' named entity 'rodent' named entity 'immunity' named entity 'predicting' named entity 'hospital' named entity 'Lassa virus' named entity 'Rogers' named entity 'LASV' named entity 'ambisense' named entity 'hospital settings' named entity 'LASV' named entity 'West Africa' named entity 'Machine learning' named entity 'pandemics' named entity 'single-stranded' named entity 'Lassa' named entity 'arenavirus' named entity 'Arenaviridae' named entity 'LASV' named entity 'viral disease' named entity 'rodent' named entity 'focal' named entity 'endemic' named entity 'West Africa' named entity 'LASV' named entity 'rodents' named entity 'zoonotic' named entity 'feces' named entity 'Lassa' named entity 'pathogen' named entity 'LASV' named entity 'Lassa virus' named entity 'pathogen' named entity 'human population' named entity 'LASV' named entity 'binary classification' named entity 'upper bound' named entity 'long-term' named entity 'virus transmission' named entity 'zoonotic' named entity 'arenavirus' named entity 'pathogen' named entity 'seroprevalence' named entity 'animal-borne' named entity 'human population' named entity 'West Africa' named entity 'human population' named entity 'virus' named entity 'food contaminated' named entity 'LASV' named entity 'seroprevalence' named entity 'virus'

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