Corresponding Author: Paige Miller, paige.miller@uga.edu
Although vaccinations are widely utilized, many childhood infections, such as measles and whooping cough, continue to pose health threats for human populations. An alternative method for reducing the negative impacts of infectious diseases involves predicting disease emergence using early warning signals (EWS), which are statistical tools and models for forecasting shifts in emerging epidemics. While EWS are successfully used in non-seasonal models of infectious diseases, their effectiveness for seasonal disease models was uncertain. A team of researchers from the Odum School of Ecology recently assessed whether EWS can also anticipate diseases emergence in seasonal models. Using a seasonally-forced Susceptible-Infected-Recovered (SIR) model, the team simulated the dynamics of an immunizing pathogen with seasonal transmission. Their results indicated that early warning signals were reliable when the disease transmission was subject to seasonal forcing. Studies such as this continue the advancement of disease modeling, helping to predict disease emergence. This in turn has major health and economic benefits by increasing and improving preparedness for populations across the globe.
Miller, P. B., O’Dea, E. B., Rohani, P., & Drake, J. M. (2017). Forecasting infectious disease emergence subject to seasonal forcing. Theoretical Biology and Medical Modelling, 14(1). doi:10.1186/s12976-017-0063-8