Several University of Georgia researchers teamed up to create a statistical method that may allow public health; and infectious disease forecasters to better predict disease reemergence; especially for preventable childhood infections such as measles and pertussis. But as described in the journal PLOS Computational Biology, their five-year project resulted in a model that shows how subtle changes; in the stream of reported cases of a disease may be predictive of both an approaching epidemic; and of 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,” said John Drake, Distinguished Research Professor of Ecology; but director for the Center for the Ecology of Infectious Diseases who researches the dynamics of biological epidemics. His current projects include studies of Ebola virus in West Africa and Middle East respiratory syndrome-related corona-virus in the horn of Africa.
But in recent years, the reemergence of measles, mumps, polio, whooping cough and other vaccine-preventable diseases; has sparked a refocus on emergency preparedness. “Research was doing in ecology and climate science about tipping points; in climate change,” he said. “We realized this is mathematically similar to disease dynamics.”
Drake and colleagues focused on “critical slowing down,” or the loss of stability that occurs in a system; as a tipping point is reach. This slowing down can result from pathogen evolution, changes in contact rates of infected individuals; and declines in vaccination. All these changes may affect the spread of a disease, but they often take place gradually; and without much consequence until a tipping point is cross. Most data analysis methods are design to characterize disease spread; after the tipping point has already been cross.
“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,” said Eamon O’Dea; a postdoctoral researcher in Drake’s laboratory who focuses on disease ecology
The research team found that their predictions were consistent with well-known; findings of 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.
For instance, Anderson and May found that measles in England and Wales; slowed down after extensive immunization in 1968. But similarly, the model shows that infectious diseases slow as an immunization threshold is approached. Slight variations in infection levels could be useful early warning signals for disease reemergence; but that results from a decline in vaccine uptake, they wrote.
“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. This could be particularly useful in countries where measles is still a high cause of mortality,” said Eamon O’Dea
To illustrate how the infectious disease model behaves, the team created a visualization; that looks like a series of bowls with balls rolling in them. In the model, vaccine coverage affects the shallowness of the bowl and the speed of the ball rolling in it. “Very often, the conceptual side of science is not emphasized as much as it should be; and we were pleased to find the right visuals to help others understand the science,” said Eric Marty, an ecology researcher who specializes in data visualization.