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Early warning signals of infectious disease transitions : a review
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Southall, Emma, Brett, Tobias S., Tildesley, Michael J. and Dyson, Louise (2021) Early warning signals of infectious disease transitions : a review. Journal of the Royal Society Interface, 18 (182). 20210555. doi:10.1098/rsif.2021.0555
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WRAP-early-warning-signals-infectious-disease-transitions-review-Dyson-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1320Kb) | Preview |
Official URL: https://doi.org/10.1098/rsif.2021.0555
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.
| Item Type: | Journal Item | |||||||||
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| Subjects: | R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | |||||||||
| SWORD Depositor: | Library Publications Router | |||||||||
| Library of Congress Subject Headings (LCSH): | Communicable diseases -- Transmission -- Mathematical models, Communicable diseases -- Prevention -- Mathematical models, Time-series analysis | |||||||||
| Journal or Publication Title: | Journal of the Royal Society Interface | |||||||||
| Publisher: | The Royal Society | |||||||||
| ISSN: | 1742-5662 | |||||||||
| Official Date: | 29 September 2021 | |||||||||
| Dates: |
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| Volume: | 18 | |||||||||
| Number: | 182 | |||||||||
| Article Number: | 20210555 | |||||||||
| DOI: | 10.1098/rsif.2021.0555 | |||||||||
| Status: | Not Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 4 October 2021 | |||||||||
| Date of first compliant Open Access: | 4 October 2021 | |||||||||
| RIOXX Funder/Project Grant: |
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