With a new case of measles and whooping cough seemingly in the news every week and vaccination rates declining, researchers are trying new approaches to predict future disease epidemics.
A research team from the University of Georgia has developed a new five-year modeling technique to forecast disease reemergence, particularly with preventable childhood infections like measles and pertussis, which demonstrates how subtle changes in the stream of reported cases of a disease can be predictive of future epidemics and the final success of a disease eradication campaign.
“We hope that in the near future, we will be available to monitor and track warning signals for emerging diseases identified by this model,” John Drake, the Distinguished Research Professor of Ecology and director for the Center for the Ecology of Infectious Diseases at the University of Georgia, said in a statement.
Recently, a number of infectious diseases such as measles, mumps, polio, whooping cough and other vaccine-preventable diseases have reemerged. U.S. officials have recently become worried about possible measles outbreaks, with at least 14 confirmed cases in New York and New Jersey in 2019. Other states such as California and Michigan have also had confirmed measles cases this year.
This recent spate of disease has led to researchers and scientists scrambling to find to new approaches for emergency preparedness.
“Research has been done in ecology and climate science about tipping points in climate change,” Drake said. “We realized this is mathematically similar to disease dynamics.”
The researchers zeroed in on “critical slowing down”—the loss of stability that occurs in a system as the tipping point is reached resulting from pathogen evolution, changes in contact rates of infected individuals and declines in vaccination—as a key parameter that serves as a basis for their research. All of the changes occurring during the critical slowing down period may impact the spread of a disease gradually, without significant consequence until the tipping point is reached.
However, the majority of data analysis techniques are aimed at characterizing disease spread after the tipping point is crossed, which can be too late.
“We saw a need to improve the ways of measuring how well-controlled a disease is, which can be difficult to do in a very complex system, especially when we observe a small fraction of the true number of cases that occur,” Eamon O’Dea, a postdoctoral researcher in Drake’s laboratory who focuses on disease ecology, said in a statement.
Using their new approach, the researchers produced predictions that were similar to well-known findings by British epidemiologists Roy Anderson and Robert May, who compared the duration of epidemic cycles in measles, rubella, mumps, smallpox, chickenpox, scarlet fever, diphtheria and pertussis from the 1880s to 1980s.
An example of this is how the epidemiologists discovered that measles in England and Wales slowed down after extensive immunization in 1968. Using their new model, the University of Georgia research team found that infectious diseases slow as an immunization threshold is approached and minuscule variations in infection levels are a sign of problems to come.
“Our goal is to validate this on smaller scales so states and cities can potentially predict disease, which is practical in terms of how to make decisions about vaccines,” O’Dea said. “This could be particularly useful in countries where measles is still a high cause of mortality.”
As part of Project AERO (Anticipating Emerging and Re-emerging Outbreaks), the researchers are creating tools like an interactive dashboard that helps non-scientists plot and analyze trending data and helps researchers and policymakers to use in the field and guide decisions.
The researchers plan to present a prototype to the public within the next year.
“If a computer model of a particular disease was sufficiently detailed and accurate, it would be possible to predict the course of an outbreak using simulation,” Eric Marty, an ecology researcher who specializes in data visualization, said in a statement. “But if you don’t have a good model, as is often the case, then the statistics of critical slowing down might still give us early warning of an outbreak.”
The study was published in PLOS Computational Biology.