The symptoms come on quickly—acute fever, followed by debilitating joint pain that can last for months. Though rarely fatal, the chikungunya virus, a mosquito-borne illness, can be particularly severe for high-risk individuals, including newborns and older adults.
While the virus is common in tropical and subtropical regions, including Asia, Africa and South America, public health officials have been tracking reported infections in Europe and, in September, a confirmed case in Long Island, New York.
Outbreaks of chikungunya have prompted the Centers for Disease Control to issue health notices to travelers bound for Bangladesh; Cuba; Guangdong Province, China; Kenya; Madagascar; Somalia; and Sri Lanka.
In Guangdong Province, an “unprecedented” outbreak recently prompted government officials in China to mandate quarantines for anyone suspected of being infected by the virus, spraying individuals with mosquito repellent and spraying impacted buildings and other areas with insecticide.
In a new study, published in Science Advances, researchers at the University of Notre Dame analyzed more than 80 outbreaks of chikungunya virus to improve prediction of future outbreaks and inform vaccine trial development.
“Chikungunya outbreaks are unpredictable in both size and severity,” said Alex Perkins, the Ann and Daniel Monahan Collegiate Professor of infectious disease epidemiology in the Department of Biological Sciences, and co-author of the study. “You can have one outbreak that infects just a few people, and another in a similar setting that infects tens of thousands. That unpredictability is what makes public health planning—and vaccine development—so difficult.”
For the study, Alexander Meyer, a postdoctoral researcher in Perkins’ lab and lead author of the study, and a team of researchers reconstructed and analyzed 86 chikungunya outbreaks, creating the largest comparative dataset of its kind.
“Instead of looking at outbreaks in isolation, looking at many, all of which varied in size and severity, allowed us to search for patterns among them,” Meyer said.
Chikungunya was first identified in the 1950s. Outbreaks have become increasingly frequent and widespread, but they’re also sporadic and difficult to predict, posing a challenge to public health officials when it comes to planning for and preventing infections.
Changes in outbreaks of chikungunya, transmitted by bites from infected mosquitoes—Aedes aegypti or Aedes albopictus are the primary vectors—and other mosquito-borne illnesses are often considered in relation to climate change, as warmer, more humid conditions can promote mosquito activity.
But Perkins said this study showed that climate isn’t necessarily the most important factor when trying to predict the severity of an outbreak of disease caused by a virus like chikungunya.
“Climate factors like temperature and rainfall can tell us where outbreaks are possible, but this study shows that they don’t help very much in predicting how severe they will be,” he said. “Local conditions matter—things like housing quality, mosquito density and how communities respond. Some variation is simply due to chance. That randomness is part of the story, too.”
Currently, only two vaccines for chikungunya have received regulatory approval—but they are not widely available in regions where the virus is most common.
That is why having such a large, comprehensive dataset is so helpful when it comes to vaccine development, Perkins said.
To test for efficacy, vaccine makers need accurate predictions of where an outbreak might occur before it happens, to conduct trials and monitor whether candidate vaccines are effective.
The study demonstrates how a more comprehensive analysis of past outbreaks can help public health officials prepare for future outbreaks, thereby protecting vulnerable populations and aiding vaccine development.
More information: Alexander D. Meyer et al, Predictability of infectious disease outbreak severity: Chikungunya as a case study, Science Advances (2025). DOI: 10.1126/sciadv.adt5419

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Study of 86 chikungunya outbreaks reveals unpredictability in size and severity
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