Hunting for the Next Pandemic Virus

Hunting for the Next Pandemic Virus

Illustration of the search for zoonotic viruses. Source: American Society for Microbiology What if researchers could find the next pandemic virus before it finds humans? This is the basis of virus discovery initiatives, which involve searching for and cataloguing viruses in animal populations to uncover potential zoonotic threats. But where should researchers look for zoonotic pathogens they don’t know exist? More importantly, how can they use the knowledge gained from virus hunting endeavors to prevent pandemics? It’s complicated.

On the one hand, computational tools have boosted the utility of discovery data by identifying novel animal viruses (and their hosts) that pose the greatest zoonotic risk. On the other hand, preventing the next pandemic, which, like every viral pandemic since the start of the 20th century, will likely stem from a virus with animal origins, is an enormous task. According to Dr. Gregory Albery, a disease ecologist at Georgetown University and co-founder of the Viral Emergence Research Initiative (Verena), discovering viruses is only a single gear in a complex system of zoonotic risk mitigation procedures and behaviors.

The Role of Virus Discovery in Zoonotic Pandemic Prevention

According to Dr. Neil Vora, a former epidemic intelligence service officer with the U.S. Centers for Disease Control and Prevention (CDC) and a physician with Conservation International, there are 2 branches of pandemic prevention: primary and secondary. The latter is largely reactionary; surveillance for diseases of concern and associated efforts to contain the spread of that disease take place after a spillover event has occurred.

Conversely, primary prevention centers on preventing spillover from animal to human hosts from happening in the first place. Viral discovery aligns with this strategy. Ideally, by profiling viruses circulating among animals, researchers hope to learn which viruses exist in close proximity to humans and how those viruses may evolve or acquire the ability to infect people. Such insights could help scientists develop strategies to prevent spillover down the road. They could also inform secondary prevention tactics, including the development of vaccines and diagnostics for emerging zoonotic threats.

This branched view of virus discovery as a steppingstone for pandemic preparedness has informed several initiatives over the past decade. One prominent example is PREDICT, a project through the U.S. Agency for International Development (USAID) in partnership with the University of California (UC) Davis One Health Institute. PREDICT, which ran from 2009 to 2020, enabled global surveillance of pathogens that can spillover from animals hosts into people. Researchers identified 958 novel viruses, including a novel ebolavirus and over 100 novel coronaviruses from more than 160,000 animals and people at high-risk animal-human interfaces in over 30 countries. The discoveries shed light on the distribution of viruses with zoonotic potential and provided a foundation to study their virology, pathogenesis and evolution.

New initiatives are also in the works. In October 2021, USAID announced a 5-year, $125 million project (Discovery & Exploration of Emerging Pathogens—Viral Zoonoses, or DEEP VZN) geared toward bolstering global capacity to detect and understand the risks of viral spillover from wildlife to humans that could cause another pandemic. The U.S. National Institute of Allergy and Infectious Disease (NIAID) also recently initiated the Centers for Research in Emerging Infectious Diseases (CREID), which unites multidisciplinary teams of investigators across the globe to study emerging and re-emerging infectious diseases. Although CREID does not specifically focus on virus discovery, the network’s projects include sampling wildlife for viruses with high zoonotic potential in Malaysia and Thailand, and surveilling animal populations in various regions for known and unknown viruses.

How to Hunt for a Virus

When scientists go on a virus hunt, they generally collect samples from animals (e.g., blood and feces) and use molecular biology methods (e.g., PCR and/or high-throughput sequencing) to detect which viruses are present in the sample. But where should researchers look for viruses with zoonotic potential, and what types of viruses should they look for? The spillover risk of a virus depends on factors related to the virus itself, its animal host(s) and the environment, all of which shape discovery strategies.

Target Animal-Human Interfaces in Spillover Hotspots

Spillover is intricately linked with human-associated impacts on, and changes to, the environment. Deforestation, for instance, increases the chances of humans encountering previously isolated animals—and their viruses. It also contributes to climate change, which (along with its myriad other negative effects) promotes spillover by forcing animals out of increasing inhospitable environments into regions populated by people. As such, spillover hot spots are centered in biodiverse tropical regions undergoing land-use changes (e.g., deforestation), particularly in Southeast Asia, West and Central Africa and the Amazon Basin, where climate change has, and will continue to have, pronounced effects.

Within these hotspots, virus discovery efforts focus on animal-human interfaces. Researchers collect samples from livestock and domesticated animals that may serve as reservoirs for viruses to jump into humans. They also target wild animals in the wildlife trade (1 of several key routes of animal-human viral transmission) and those that live with or near people. For instance, Bombali virus, a novel ebolavirus discovered via the PREDICT project, was isolated from free-tailed bats roosting in people’s homes in Sierra Leone. Dr. Christine Johnson, director of the EpiCenter for Disease Dynamics at the UC Davis One Health Institute highlighted that the virus has since been detected in other countries, and researchers are currently studying whether it could infect humans (or already has).

Monkeys foraging for food near humans.
Greater proximity between wild animals and humans, via land-use changes and the wildlife trade, among others, creates opportunities for spillover. Seen here: monkeys in Bali, Indonesia.
Source: Iker Martiarena/iStock.

Sample from Animals Likely to Harbor Zoonotic Viruses

The proximity of humans to animals is only 1 driver of a virus’s spillover risk; the physiology, behavior and geographical distribution of its host(s) also play a role. For example, the genetic relatedness between a virus’s animal host and humans may influence whether people possess the cellular machinery to facilitate viral entry and replication. This is 1 of several reasons why zoonotic diseases often emerge from wild mammals. To that end, Johnson and her colleagues recently found that 3 mammalian orders—rodents, bats and primates—hosted nearly 76% of known zoonotic viruses. Bats and rodents are particularly notable for harboring zoonotic pathogens, though the reasons why aren’t entirely clear. It may be tied, in part, to the sheer number of bat and rodent species spread across the globe (roughly 1,400 and 2,500, respectively).

Indeed, animals with high species diversity and broad geographic ranges have a greater chance of cross-species viral transmission. As climate change forces animals into new habitats, viral sharing among diverse mammal species (including humans) is expected to increase. Thus, focusing virus discovery initiatives on select animal (i.e., mammalian) groups is useful for uncovering zoonotic threats. While this is no small task (it is estimated that scientists only know about 1% of mammal viruses), it does allow for more targeted hunting.

Focus on Viruses with High Spillover Potential

Not all viruses are equal in their potential to spread to, and among, humans. For example, the genetic variability, adaptability and broad host range of RNA viruses, like coronaviruses and influenza viruses, make them prime spillover candidates. DNA viruses have an evolutionary rate of <1% that of RNA viruses, making successful infection of, and adaptation to, new hosts (e.g., humans) less likely. Indeed, RNA viruses are the culprits behind recent pandemics, from the H1N1 flu pandemic to COVID-19. Given that it is likely the next pandemic virus will bear similarities to those already known to infect humans, experts believe that looking for viruses with demonstrated spillover potential is an advantageous approach. For this reason, PREDICT mainly used consensus PCR (cPCR) for the targeted discovery of coronaviruses, filoviruses, paramyxoviruses and influenza viruses; each group includes viruses of “known zoonotic concern” with a “high-risk for causing future outbreaks or pandemics.” An emphasis on studying select high priority ‘prototype’ pathogens to mitigate future threats has also gained traction in the NIAID’s Pandemic Preparedness Plan, announced earlier this year.

Making Sense of Discovery Data with Zoonotic Risk Technologies

Still, even with a targeted virus hunting strategy, “identifying the viruses is only the first step,” Albery said. “After that point, you have to assess their risk, which is a whole other kettle of fish.” In other words, finding a virus is great, but knowing the risk it poses to humans is key.

This need has led to the development of computational tools, or zoonotic risk technologies, that use what is known about viruses that do infect humans to predict which animal pathogens may pose a spillover threat. For example, researchers developed an open-source, interactive web tool, called SpillOver, which uses data from PREDICT to perform a comparative risk assessment between known zoonotic viruses and those with uncharacterized spillover potential. In their initial analyses, the team found that the top-ranking viruses were known pathogens, including Lassa virus and Ebola virus, although the list also contained newly detected viruses, specifically coronaviruses. Johnson and her colleagues have also developed a new method that uses machine learning to determine the host range of known zoonotic viruses to predict the host species of novel animal viruses—and where humans fit into the mix.

These tools offer several benefits. Albery noted that viral discovery and identification must be followed up with laboratory experiments to understand the infection dynamics of viruses of interest (e.g., human cell entry receptor and usage, viral replication and pathogenesis, among other characteristics). Zoonotic risk technologies can help researchers narrow their experimental focus (and resources) to high-risk viruses.

Image of Commerson’s leaf-nosed bats. Bats are key reservoirs of zoonotic viruses. These Commerson’s leaf-nosed bats belong to the Hipposideridae family, which is known to harbor many betacoronaviruses.
Source: Charles J. Sharp/Wikimedia.
With that in mind, zoonotic risk technology can also shape virus hunting pipelines from the get-go. Albery and his colleagues recently used machine learning models to identify bat species likely to harbor undiscovered betacoronaviruses (a family of viruses with high spillover risk that includes MERS-CoV, SARS-CoV-1 and SARS-CoV-2), based on characteristics of known carriers. The team identified 400 bat species worldwide that could be undetected hosts of betacoronaviruses.

“What our tools allow us to do is narrow down the bats that might be hosting betacoronaviruses, target our sampling to those species and pull out the viruses that we think might actually, someday, be a real risk to human health,” said Dr. Colin Carlson, senior author of the study and an assistant research professor at the Center for Global Health Science and Security at Georgetown University, during the Verena Forum on Zoonotic Risk Technology digital workshop in January 2021. Carlson, who co-founded Verena with Albery, noted that this subset of viruses can then be pegged for downstream analyses—perhaps allowing for the targeted development of diagnostics and vaccines for problematic viruses before they infect humans.

Hunting for Viruses Is Not Enough to Prevent Zoonotic Pandemics

Nevertheless, Carlson cautioned that “knowing about a virus does not inherently make us more prepared.” Indeed, MERS-CoV and SARS-CoV-1 hinted at the potential threat of SARS-like coronaviruses, yet knowing about the threat did not stop COVID-19. Moreover, just because one looks for the next pandemic pathogen doesn’t mean they will find it. It is virtually impossible to detect every single virus in the animal world. Some will inevitably slip through the cracks. Vora highlighted that, with our current knowledge and technologies, it is difficult to determine which newly discovered animal viruses could cause human illness, or a pandemic for that matter. A complex mix of factors rooted in immunology, ecology and epidemiology determines whether a virus successfully infects a human host and spreads. Albery agreed: discovery, even when bolstered by emerging computational tools, “is not really going to cut it” for driving coordinated, effective action toward curbing zoonotic pandemics.

“We have to be clear what is for today—actions here and now to save lives—versus what is for generating knowledge,” Vora said. He pointed to actions that minimize the chances of spillover, regardless of the specific viral threat. These include reducing deforestation, regulating commercial wildlife markets and trade, improving infection control when raising farm animals and enhancing the health of communities living in emerging disease hotspots.

Aerial view of deforestation in the Amazon rainforest.
Deforestation in the Amazon rainforest. Less forest creates more opportunities for animal-human interactions.
Source: Paralaxis/iStock.

For Johnson, there is no question that virus discovery is important, but the framework in which it is implemented is critical. She used PREDICT as an example, stating that the project wasn’t only about discovering novel viruses, it also “sought to unify virus surveillance across the animal and human health sectors and identify wildlife-human interfaces, especially in areas with landscape change, deforestation and other aspects of the environment that could drive some of the connectivity between animals and people and increase the level of risk.” PREDICT aimed to strengthen detection and surveillance capacities in countries where, historically, such capabilities were limited. The project also combined viral discovery efforts “with an approach that also detected known viruses in those virus families that were already of concern.”

Accordingly, all experts stressed that, in addition to primary prevention efforts that reduce spillover risk, there is a need to support secondary prevention strategies that deal with spillover when it (inevitably) happens. This includes surveilling animals and people to keep tabs on known and unknown zoonotic pathogens as they appear in a population and bolstering health care infrastructure to respond to them when they do. “If [we] choose not to invest in any 1 of these items, we are going to have a weak link, and we are going to remain susceptible,” Vora warned. “None of them are perfect in and of themselves.”

Learn more about how researchers use computer modeling to predict spillover events:

Author: Madeline Barron, Ph.D.

Madeline Barron, Ph.D.
Madeline Barron, Ph.D., is the Science Communications Specialist at ASM. She obtained her Ph.D. from the University of Michigan in the Department of Microbiology and Immunology.