Sampling strategies and pre-pandemic surveillance gaps for bat coronaviruses (preprint)

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.

The World Health Organization’s Disease Outbreak News: a retrospective database (preprint)

The World Health Organization (WHO) notifies the global community about disease outbreaks through the Disease Outbreak News (DON). These online reports tell important stories about both outbreaks themselves and the high-level decision making that governs information sharing during public health emergencies. However, they have been used only minimally in global health scholarship to date. Here, we collate all 2,789 of these reports from their first use through the start of the Covid-19 pandemic (January 1996 to December 2019), and develop an annotated database of the subjective and often inconsistent information they contain. We find that these reports are dominated by a mix of persistent worldwide threats (particularly influenza and cholera) and persistent epidemics (like Ebola virus disease in Africa or MERS-CoV in the Middle East), but also document important periods in history like the anthrax bioterrorist attacks at the turn of the century, the spread of chikungunya and Zika virus to the Americas, or even recent lapses in progress towards polio elimination. We present three simple vignettes that show how researchers can use these data to answer both qualitative and quantitative questions about global outbreak dynamics and public health response. However, we also find that the retrospective value of these reports is visibly limited by inconsistent reporting (e.g., of disease names, case totals, mortality, and actions taken to curtail spread). We conclude that sharing a transparent rubric for which outbreaks are considered reportable, and adopting more standardized formats for sharing epidemiological metadata, might help make the DON more useful to researchers and policymakers.

Present and future distribution of bat hosts of sarbecoviruses: implications for conservation and public health (preprint)

Global changes in response to human encroachment into natural habitats and carbon emissions are driving the biodiversity extinction crisis and increasing disease emergence risk. Host distributions are one critical component to identify areas at risk of spillover, and bats act as reservoirs of diverse viruses. We developed a reproducible ecological niche modelling pipeline for bat hosts of SARS-like viruses (subgenus Sarbecovirus), given that since SARS-CoV-2 emergence several closely-related viruses have been discovered and sarbecovirus-host interactions have gained attention. We assess sampling biases and model bats' current distributions based on climate and landscape relationships and project future scenarios. The most important predictors of species distribution were temperature seasonality and cave availability. We identified concentrated host hotspots in Myanmar and projected range contractions for most species by 2100. Our projections indicate hotspots will shift east in Southeast Asia in >2 {degrees}C hotter locations in a fossil-fueled development future. Hotspot shifts have implications for conservation and public health, as loss of population connectivity can lead to local extinctions, and remaining hotspots may concentrate near human populations.

Zoonotic Risk Technology Enters the Viral Emergence Toolkit (preprint)

In light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programs will identify hundreds of novel viruses that might someday pose a threat to humans. Our capacity to identify which viruses are capable of zoonotic emergence depends on the existence of a technology—a machine learning model or other informatic system—that leverages available data on known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions: What are the prerequisites, in terms of open data, equity, and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it, and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges?