Despite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host–virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.
The growing threat of vector-borne diseases, highlighted by recent epidemics, has prompted increased focus on the fundamental biology of vector-virus interactions. To this end, experiments are often the most reliable way to measure vector competence (the potential for arthropod vectors to transmit certain pathogens). Data from these experiments are critical to understand outbreak risk, but – despite having been collected and reported for a large range of vector-pathogen combinations – terminology is inconsistent, records are scattered across studies, and the accompanying publications often share data with insufficient detail for reuse or synthesis. Here, we present a minimum data and metadata standard for reporting the results of vector competence experiments. Our reporting checklist strikes a balance between completeness and labor-intensiveness, with the goal of making these important experimental data easier to find and reuse in the future, without much added effort for the scientists generating the data. To illustrate the standard, we provide an example that reproduces results from a study of Aedes aegypti vector competence for Zika virus.
The emergence of SARS-CoV-2, and the challenge of pinpointing its ecological and evolutionary context, has highlighted the importance of evidence-based strategies for monitoring viral dynamics in bat reservoir hosts. Here, we compiled the results of 93,877 samples collected from bats across 111 studies between 1996 and 2018, and used these to develop an unprecedented open database, with over 2,400 estimates of coronavirus infection prevalence or seroprevalence at the finest methodological, spatiotemporal, and phylogenetic level of detail possible from public records. These data revealed a high degree of heterogeneity in viral prevalence, reflecting both real spatiotemporal variation in viral dynamics and the effect of variation in sampling design. Phylogenetically controlled meta-analysis revealed that the most significant determinant of successful viral detection was repeat sampling (i.e., returning to the same site multiple times); however, fewer than one in five studies longitudinally collected and reported data. Viral detection was also more successful in some seasons and from certain tissues, but was not improved by the use of euthanasia, indicating that viral detection may not be improved by terminal sampling. Finally, we found that prior to the pandemic, sampling effort was highly concentrated in ways that reflected concerns about zoonotic risk, leaving several broad geographic regions (e.g., South Asia, Latin America and the Caribbean, and most of Sub-Saharan Africa) and bat subfamilies (e.g., Stenodermatinae and Pteropodinae) measurably undersampled. These gaps constitute a notable vulnerability for global health security and will likely be a future barrier to contextualizing the origin of novel zoonotic coronaviruses
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