Southall, Emma, Tildesley, Michael J. and Dyson, Louise (2020) Prospects for detecting early warning signals in discrete event sequence data : application to epidemiological incidence data. PLoS Computational Biology, 16 (9). e1007836. doi:10.1371/journal.pcbi.1007836

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Abstract

Early warning signals (EWS) identify systems approaching a critical transition, where the system undergoes a sudden change in state. For example, monitoring changes in variance or autocorrelation offers a computationally inexpensive method which can be used in real-time to assess when an infectious disease transitions to elimination. EWS have a promising potential to not only be used to monitor infectious diseases, but also to inform control policies to aid disease elimination. Previously, potential EWS have been identified for prevalence data, however the prevalence of a disease is often not known directly. In this work we identify EWS for incidence data, the standard data type collected by the Centers for Disease Control and Prevention (CDC) or World Health Organization (WHO). We show, through several examples, that EWS calculated on simulated incidence time series data exhibit vastly different behaviours to those previously studied on prevalence data. In particular, the variance displays a decreasing trend on the approach to disease elimination, contrary to that expected from critical slowing down theory; this could lead to unreliable indicators of elimination when calculated on real-world data. We derive analytical predictions which can be generalised for many epidemiological systems, and we support our theory with simulated studies of disease incidence. Additionally, we explore EWS calculated on the rate of incidence over time, a property which can be extracted directly from incidence data. We find that although incidence might not exhibit typical critical slowing down properties before a critical transition, the rate of incidence does, presenting a promising new data type for the application of statistical indicators.

Item Type:	Journal Article
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine
Divisions:	Faculty of Science > Life Sciences (2010- ) Faculty of Science > Mathematics
Library of Congress Subject Headings (LCSH):	Epidemics, Epidemics -- Mathematical models, Epidemics -- Prevention -- Data processing , Communicable diseases, Communicable diseases -- Detection, Communicable diseases -- Prevention -- Data processing , Emergency communication systems , Epidemics -- Safety measures, Communicable diseases -- Epidemiology -- Data processing
Journal or Publication Title:	PLoS Computational Biology
Publisher:	Public Library of Science
ISSN:	1553-7358
Official Date:	22 September 2020
Dates:	Date Event 22 September 2020 Published 30 July 2020 Accepted
Date of first compliant deposit:	1 October 2020
Volume:	16
Number:	9
Article Number:	e1007836
DOI:	10.1371/journal.pcbi.1007836
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Copyright Holders:	© 2020 Southall et al.
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID EP/L015374/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266
Related URLs:	Related dataset

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