Several University of Georgia researchers came together to create a statistical model that may allow public health and infectious disease forecasters to better predict outbreaks, especially in vaccine-preventable diseases such as measles and pertussis.
In recent years, the re-emergence of measles, mumps, polio, whooping cough and other vaccine-preventable diseases has shined a bigger light on emergency preparedness.
The scientists compared disease dynamics to tipping points in ecology and climate science due to their mathematical similarities. The team focused on "critical slowing down," or the loss of stability that occurs in a system as a tipping point is reached. This slowing down can result from pathogen evolution, changes in contact rates of infected individuals, and declines in vaccination. These changes can affect the spread of a disease, but they often take place gradually and without much effect until a tipping point is crossed.
The research team found that their predictions were similar to the 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. One of their findings was that measles in England and Wales slowed down after extensive immunization in 1968. The model also shows that infectious diseases slow as an immunization threshold is approached. The slight variations in infection levels could be useful early warning signals for disease re-emergence that results from a decline in vaccine uptake.
With this knowledge, the team is developing a program, AERO (Anticipating Emerging and Re-emerging Outbreaks), that will help non-scientists plot and analyze data to understand the current trends for a certain infectious disease. They will present this idea within the next year.