Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America.
Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America.
PLoS Comput Biol. 2018 Jun 8;14(6):e1006204. doi: 10.1371/journal.pcbi.1006204. eCollection 2018 Jun.
Epidemic transitions are an important feature of infectious disease systems. As the transmissibility of a pathogen increases, the dynamics of disease spread shifts from limited stuttering chains of transmission to potentially large scale outbreaks. One proposed method to anticipate this transition are early-warning signals (EWS), summary statistics which undergo characteristic changes as the transition is approached. Although theoretically predicted, their mathematical basis does not take into account the nature of epidemiological data, which are typically aggregated into periodic case reports and subject to reporting error. The viability of EWS for epidemic transitions therefore remains uncertain. Here we demonstrate that most EWS can predict emergence even when calculated from imperfect data. We quantify performance using the area under the curve (AUC) statistic, a measure of how well an EWS distinguishes between numerical simulations of an emerging disease and one which is stationary. Values of the AUC statistic are compared across a range of different reporting scenarios. We find that different EWS respond to imperfect data differently. The mean, variance and first differenced variance all perform well unless reporting error is highly overdispersed. The autocorrelation, autocovariance and decay time perform well provided that the aggregation period of the data is larger than the serial interval and reporting error is not highly overdispersed. The coefficient of variation, skewness and kurtosis are found to be unreliable indicators of emergence. Overall, we find that seven of ten EWS considered perform well for most realistic reporting scenarios. We conclude that imperfect epidemiological data is not a barrier to using EWS for many potentially emerging diseases.
传染病系统的一个重要特征是流行转变。随着病原体的传染性增加，疾病传播的动态从有限的传播链转变为潜在的大规模爆发。一种预测这种转变的方法是预警信号（EWS），它是在接近转变时经历特征变化的汇总统计数据。尽管理论上已经预测到，但它们的数学基础并没有考虑到流行病学数据的性质，这些数据通常被汇总为定期的病例报告，并受到报告误差的影响。因此，EWS 用于流行转变的可行性仍然不确定。在这里，我们证明即使从不完善的数据中计算，大多数 EWS 也可以预测疾病的爆发。我们使用 AUC 统计量来量化性能，AUC 统计量是衡量 EWS 区分新兴疾病和稳定疾病的数值模拟的能力的指标。在一系列不同的报告场景中比较 AUC 统计量的值。我们发现不同的 EWS 对不完善的数据有不同的反应。均值、方差和一阶差分方差在报告误差不是高度过分散的情况下表现良好。自相关、自协方差和衰减时间在数据的聚合期大于序列间隔且报告误差不是高度过分散的情况下表现良好。变异系数、偏度和峰度被发现是疾病爆发的不可靠指标。总体而言，我们发现考虑的十种 EWS 中有七种在大多数现实的报告场景中表现良好。我们得出的结论是，不完善的流行病学数据并不是使用 EWS 对许多潜在新兴疾病进行预测的障碍。
