As the Earth’s climate continues to warm, researchers predict wild animals will be forced to relocate their habitats — likely to regions with large human populations — dramatically increasing the risk of a viral jump to humans that could lead to the next pandemic. 

This link between climate change and viral transmission is described by an international research team led by scientists at Georgetown University.

In their study, the scientists conducted the first comprehensive assessment of how climate change will restructure the global mammalian virome. The work focuses on geographic range shifts — the journeys that species will undertake as they follow their habitats into new areas. As they encounter other mammals for the first time, the study projects they will share thousands of viruses.

The scientists say these shifts bring greater opportunities for viruses like Ebola or coronaviruses to emerge in new areas, making them harder to track, and into new types of animals, making it easier for viruses to jump across a “stepping stone” species into humans.

“The closest analogy is actually the risks we see in the wildlife trade,” says the study’s lead author Colin Carlson, PhD, an assistant research professor at the Center for Global Health Science and Security at Georgetown University Medical Center. “We worry about markets because bringing unhealthy animals together in unnatural combinations creates opportunities for this stepwise process of emergence — like how SARS jumped from bats to civets, then civets to people. But markets aren’t special anymore; in a changing climate, that kind of process will be the reality in nature just about everywhere.”

Of concern is that animal habitats will move disproportionately in the same places as human settlements, creating new hotspots of spillover risk. Much of this process may already be underway in today’s 1.2 degrees warmer world, and efforts to reduce greenhouse gas emissions may not stop these events from unfolding.

An additional important finding is an impact rising temperatures will have on bats, which account for the majority of novel viral-sharing. Their ability to fly will allow them to travel long distances and share the most viruses. Because of their central role in viral emergence, the greatest impacts are projected in southeast Asia, a global hotspot of bat diversity.

“At every step,” said Carlson, “our simulations have taken us by surprise. We’ve spent years double-checking those results, with different data and different assumptions, but the models always lead us to these conclusions. It’s a really stunning example of just how well we can, actually, predict the future if we try.”

As viruses start to jump between host species at unprecedented rates, the authors say that the impacts on conservation and human health could be stunning.

“This mechanism adds yet another layer to how climate change will threaten human and animal health,” says the study’s co-lead author, Gregory Albery, PhD, a postdoctoral fellow in the Department of Biology in the Georgetown University College of Arts and Sciences.

“It’s unclear exactly how these new viruses might affect the species involved, but it’s likely that many of them will translate to new conservation risks and fuel the emergence of novel outbreaks in humans.”

Altogether, the study suggests that climate change will become the biggest upstream risk factor for disease emergence — exceeding higher-profile issues like deforestation, wildlife trade and industrial agriculture. The authors say the solution is to pair wildlife disease surveillance with real-time studies of environmental change.

“When a Brazilian free-tailed bat makes it all the way to Appalachia, we should be invested in knowing what viruses are tagging along,” says Carlson. “Trying to spot these host jumps in real-time is the only way we’ll be able to prevent this process from leading to more spillovers and more pandemics.”

“We’re closer to predicting and preventing the next pandemic than ever,” says Carlson. “This is a big step towards prediction — now we have to start working on the harder half of the problem.”

“The COVID-19 pandemic, and the previous spread of SARS, Ebola, and Zika, show how a virus jumping from animals to humans can have massive effects. To predict their jump to humans, we need to know about their spread among other animals,” said Sam Scheiner, a program director with the U.S. National Science Foundation (NSF), which funded the research. “This research shows how animal movements and interactions due to a warming climate might increase the number of viruses jumping between species.”

Johns Hopkins biomedical engineers have developed an artificial intelligence (AI) training strategy to capture images of mouse brain cells in action. The researchers say the AI system, in concert with specialized ultra-small microscopes, makes it possible to find precisely where and when cells are activated during movement, learning, and memory. The data gathered with this technology could someday allow scientists to understand how the brain functions and is affected by the disease.  

“When a mouse’s head is restrained for imaging, its brain activity may not truly represent its neurological function,” says Xingde Li, Ph.D., professor of biomedical engineering at the Johns Hopkins University School of Medicine. “To map brain circuits that control daily functions in mammals, we need to see precisely what is happening among individual brain cells and their connections, while the animal is freely moving around, eating and socializing.” 

To gather this extremely detailed data, Li’s team developed ultra-small microscopes that the mice can wear on the top of their head. Measuring a couple of millimeter in diameter, the size of these microscopes limit the imaging technology they can carry onboard. In comparison to benchtop models, the frame rate on the miniature microscopes is low, which makes them susceptible to interference from motion. Disturbances such as the mouse’s breathing or heart rate would affect the accuracy of the data these microscopes can capture. Researchers estimate that Li’s miniature microscope would need to exceed 20 frames per second to eliminate all the disturbances from the motion of a freely moving mouse.

“There are two ways to increase frame rate,” says Li. “You can increase the scanning speed and you can decrease the number of points scanned.”

In previous research, Li’s engineering team quickly found they hit the physical limits of the scanner, reaching six frames per second, which maintained excellent image quality but was far below the required rate. So, the team moved on to the second strategy for increasing frame rate — decreasing the number of points scanned. However, similar to reducing the number of pixels in an image, this strategy would cause the microscope to capture lower-resolution data.

Li hypothesized that an AI program could be trained to recognize and restore the missing points, enhancing the images to a higher resolution. Such AI training protocols are used when it is impossible or time-consuming to create a computer program for a task, such as reliably recognizing a cluster of features as a human face. Instead, computer scientists use the approach of letting computers learn to program themselves through processing large sets of data.

One significant challenge in the proposed AI approach was the lack of similar images of mouse brains to train the AI against. To overcome this gap, the team developed a two-stage training strategy. The researchers began training the AI to identify the building blocks of the brain from images of fixed samples of mouse brain tissue. They next trained the AI to recognize these building blocks in a head-restrained living mouse under their ultra-small microscope. This step trained the AI to recognize brain cells with natural structural variation and a small bit of motion caused by the movement of the mouse’s breathing and heartbeat. 

“The hope was that whenever we collect data from a moving mouse, it will still be similar enough for the AI network to recognize,” says Li.

Then, the researchers tested the AI program to see if it could accurately enhance mouse brain images by incrementally increasing the frame rate. Using a reference image, the researchers reduced the microscope scanning points by factors of 2, 4, 8, 16, and 32 and observed how accurately the AI could enhance the image and restore the image resolution.

The researchers found that the AI could adequately restore the image quality up to 26 frames per second. 

The team then tested how well the AI tool performed in combination with a mini microscope attached to the head of a moving mouse. With the combination of AI and microscope, the researchers were able to precisely see activity spikes of individual brain cells activated by the mouse walking, rotating, and generally exploring its environment.

“We could never have seen this information at such high resolution and frame rate before,” says Li. “This development could make it possible to gather more information on how the brain is dynamically connected to the action on a cellular level.”

The researchers say that with more training, the AI program may be able to accurately interpret images up to 52 or even 104 frames per second.

Other researchers involved in this study include Honghua Guan, Dawei Li, Hyeon-cheol Park, Ang Li, Yungtian Gau, and Dwight Bergles of the Johns Hopkins University School of Medicine; Yuanlei Yue and Hui Lu of George Washington University; and Ming-Jun Li from Corning Inc.