Miller Paige B, O'Dea Eamon B, Rohani Pejman, Drake John M
University of Georgia, Odum School of Ecology, 140 E. Green Street, Athens, USA.
Center for the Ecology of Infectious Diseases, University of Georgia, Athens, USA.
Theor Biol Med Model. 2017 Sep 6;14(1):17. 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.
尽管疫苗接种覆盖率很高，但许多儿童期感染对人群构成的威胁却日益增大。准确的疾病预测对公共卫生具有巨大价值。在非季节性传染病模型中，利用预警信号（EWS）预测疾病出现是可行的。在此，我们评估了EWS在季节性模型中是否也能预测疾病出现。
我们模拟了一种免疫传染性病原体接近疾病地方性流行临界点时的动态变化。为探究季节性对早期预警统计可靠性的影响，我们改变了平均传播率周围波动的幅度。我们提出并分析了基于小波谱的两种新预警信号。我们根据临界点之前趋势的强度来测量预警信号的可靠性，然后计算曲线下面积（AUC）统计量。
当疾病传播受到季节性影响时，预警信号是可靠的。基于小波的预警信号与其他传统预警信号一样可靠。我们发现，在分析前去除季节性趋势并不能一致地改善早期预警统计。
预警信号可预测受季节性影响的传染病关键转变的发生。基于小波的早期预警统计也可用于预测传染病。
