Abstract
Early warning signals (EWSs) are a group of statistical time-series signals which could be used to anticipate a critical transition before it is reached. EWSs are model-independent methods that have grown in popularity to support evidence of disease emergence and disease elimination. Theoretical work has demonstrated their capability of detecting disease transitions in simple epidemic models, where elimination is reached through vaccination, to more complex vector transmission, age-structured and metapopulation models. However, the exact time evolution of EWSs depends on the transition; here we review the literature to provide guidance on what trends to expect and when. Recent advances include methods which detect when an EWS becomes significant; the earlier an upcoming disease transition is detected, the more valuable an EWS will be in practice. We suggest that future work should firstly validate detection methods with synthetic and historical datasets, before addressing their performance with real-time data which is accruing. A major challenge to overcome for the use of EWSs with disease transitions is to maintain the accuracy of EWSs in data-poor settings. We demonstrate how EWSs behave on reported cases for pertussis in the USA, to highlight some limitations when detecting disease transitions with real-world data.
Highlights
Infectious diseases contribute to nearly one-third of the worldwide disease burden [1]
In summary for Vermont, variance is strongly increasing for most choices of window sizes and aggregation, coefficient of variation is weakly decreasing, and autocorrelation lag-1 has a mixed response which perhaps can be explained by the stochastic nature of pertussis in Vermont; the former two indicate characteristics of disease emergence from critical slowing down’ (CSD)
Early warning signals (EWSs) offer a real-time signal of an impending disease transition; to use EWSs reliably in a controlmanagement framework, all limitations need to be identified and communicated clearly with public health officials
Summary
Infectious diseases contribute to nearly one-third of the worldwide disease burden [1]. A variety of statistical methodologies have been proposed for detecting anomalies in data in the context of detecting infectious disease outbreaks [4,5] These surveillance-based approaches identify patterns of disease outbreaks as they arise in public health data to inform the implementation of control, methods which provide sufficient time to allow interventions to take place. This article will summarize the findings on the suitability of EWSs being used in epidemiology, and in particular will discuss future avenues such as the application of these methods to benefit programme managers for disease control
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