Paige Miller, from Gustavus Adolphus College, and Dr. John Drake in the Odum School of Ecology, examined early warning signals in disease systems.
Paige Miller, Gustavus Adolphus College
John Drake, Odum School of Ecology, University of Georgia
Infectious diseases have ravaged the human population since the beginning of time. Eradication of human infectious diseases has been a public health initiative for more than a century. Emerging and re-emerging infectious diseases, such as Ebola, multi-drug-resistant TB, and pertussis, continually threaten lives of people across the world. Predicting when eradication of a disease is almost achieved or when emergence events are likely to occur could give policy makers specific evidence to stop the disease emergence or continue eradication efforts. Early warning signals (EWS) have been studied in many systems such as fishery collapses, economic market fluctuations, and global climate change. In the case of infectious diseases, however, EWS are difficult to use because of inherent periodicities (seasonality or multi-yearly cycling) and under-reporting issues in the datasets along with violations of normality, independence, and stationarity assumptions. We evaluate wavelet-based methods, which make fewer assumptions and allow us to specify which periodicities to study. Wavelets are a method for representing a time-series in terms of coefficients that are associated with a particular time and a particular frequency. The power ratio, the ratio of low frequency waves to high frequency waves, is calculated using wavelet-based analysis. Here, we determine if the power ratio can be used as a more reliable early warning signal for detecting infectious disease emergence and eradication.