Prospects for detecting early warning signals in discrete event sequence data: application to epidemiological incidence data

Emma Southall; Michael J. Tildesley; Louise Dyson

In conclusion, there is a tremendous potential for using early warning signals to 555 provide evidence on our progress towards elimination and inform public health policies. 556 We have indicated that by monitoring simple statistics over time it is possible to 557 observe disease emergence and elimination, which with further development offers a 558 promising solution for an automated system that can update time series statistics in 559 real-time as new data becomes available. This would be particularly useful for emerging 560 diseases where EWS could be used to prompt early detection and help aid rapid The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.02.021576 doi: bioRxiv preprint responses. The focus of our paper has provided insight on how statistics behave for 562 different types of infectious disease data, where we considered suitable data which could 563 be incorporated into such monitoring system. We have researched the resemblance of 564 observed time series results between different data types, a necessary exploration for the 565 development of EWS before they can impact decision making. We reported that some 566 indicators traits are inconsistent across all data types and some EWS differ significantly 567 between disease thresholds: elimination and emergence. Knowledge of the type of data 568 which has been collected is imperative to avoid misleading judgements in response to 569 time series trends. Our work has provided analytical evidence to understand why results 570 differ, improving our ability to monitor EWS for infectious disease transitions.