Forecasting infectious disease emergence subject to seasonal forcing

• Published in: Theoretical biology and medical modelling Vol. 14; no. 1; p. 17
• Main Authors: Miller, Paige B., O’Dea, Eamon B., Rohani, Pejman, Drake, John M.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 06.09.2017; BioMed Central
• Subjects: Area Under Curve; Behavior modification; Children; Communicable diseases; Communicable Diseases - epidemiology; Computer Simulation; Disease Susceptibility; Disease transmission; Emergence; Environmental aspects; Forecasting; Housing; Humans; Immunization; Infectious diseases; Mathematical models; Measles; Models, Biological; Pathogens; Phase transitions; Public health; Reliability; Seasonal variations (Diseases); Seasons; Statistical analysis; Statistics; Stochastic Processes; Studies; Time series; Trends; Vaccination; Warning; Wavelet analysis; Weather forecasting; Whooping cough; United States; Disease forecasting; Critical transition; Seasonality
• ISSN: 1742-4682; 1742-4682
• DOI: 10.1186/s12976-017-0063-8

Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models. We simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic. Early warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly. Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.