Public health officials and infectious disease experts could better predict outbreaks of reemerging diseases like measles with the help of a new statistical method.
Researchers from the University of Georgia in Atlanta created the statistical method, which they described in a study published in PLOS Computational Biology.
Most statistical analysis methods focus on a disease's spread after it has already reached a tipping point. The new model predicts future disease reemergence by detecting subtle changes in a disease's spread before that tipping point occurs.
This ability to monitor and track the warning signs of emerging diseases could help health officials better anticipate and address outbreaks to reduce disease burden.