When, where and why do diseases jump from animals to humans? A new project will monitor how changing seasons, land use and human behavior affect viral outbreaks in wild rodent populations, in order to identify hotspots with a high probability of spreading to humans. The project is led by Barbara Hahn, a disease ecologist at the Cary Institute of Ecosystem Studies, in an international team of scientists from the Smithsonian Institution, the Royal Veterinary College, the University of Oxford and the University of Glasgow.
Until now, it has been difficult to study how changes in environmental conditions affect virus transmission in the wild. With $2.9 million in funding from the National Science Foundation and an additional $1.9M in support from the UK’s Biotechnology and Biological Sciences Research Council, the new project overcomes this challenge by combining traditional disease ecology approaches with the recently proven tool of metaviromics.
Unlike other genetic sequencing techniques, which are quite targeted, metaviromics will allow the team to scan for a wide range of viruses in mice and see how they’re changing over time and space, Hahn said. The research will focus on RNA-based viruses; such as coronaviruses, hantaviruses and enteroviruses -; Many of which pose a higher risk to humans.
This study will be an important test of the use of metaviromics to identify potential new epidemics. “The methods developed here have very broad applicability across many potential pathogens.”
Samuel Scheiner, Program Director, Division of Environmental Biology, National Science Foundation
Viruses are more likely to jump from animals to humans when the animal host has a high viral load and frequent contact with humans, explains James Hassell, a wildlife veterinarian and epidemiologist in the Global Health Program at the Smithsonian’s National Zoo and Conservation Biology Institute. “And the likelihood of the virus reaching high levels or potentially infecting people depends on the season, how dense the animal population is, and how often they come into contact with people.”
The team will explore these dynamics between wild mice and the viruses they carry in both the UK and eastern Uganda. The project focuses on rodents because they are responsible for more zoonotic diseases than any other mammalian order. Many rodent species live in close proximity to humans and are highly responsive to environmental changes.
Field work is a large component of the project; This involves trapping mice, tagging them, and collecting stool samples for viral RNA extraction. Radio frequency identification tags will help researchers track the health and viral load of individual mice and estimate their movement, range and social contact.
To identify key drivers of virus transmission in seasonal environments, the team will track wood mice (Apodemus sylvaticus) in Witham Woods, UK for three years.
The eastern Uganda site, where forests are being converted to agriculture, will help reveal the effects of land use change on rodent populations and viral dynamics. Rodents will be observed across a variety of landscapes, from forests to fields and villages. The team will estimate the frequency of rodent exposure through questionnaires, household rodent infestation measurements, and human movement and land use activity data.
“These types of human-animal contact data are difficult to obtain, but can give us a powerful new view of pathogen transmission at a complex interface spanning different environments and seasons,” said Christina Faust of the University of Glasgow, who led the work. in Uganda in collaboration with country partners.
Artificial intelligence will be used to analyze how these factors and others interact to influence virus abundance and spillover risk. “Machine learning is a really powerful way to look at data from a different perspective,” Hahn notes, “and it reveals patterns that you wouldn’t be able to see otherwise.”
Jayna Raghwani from the Royal Veterinary College added: “Combined with mathematical modelling, metaviromics data brings us closer to both measuring and predicting when viruses actually emerge in the wild.”
Results will inform disease surveillance efforts in changing environments, help identify emerging diseases before they become epidemics, and guide strategies to reduce spillover risk.
“This project is fundamental to being able to make better, more effective predictions about viral emergence at scales where management can actually reduce disease emergence,” Hahn said.