Apr 2, 2020

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

BioRxiv : the Preprint Server for Biology
Emma SouthallM. Tildesley

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 predicti...Continue Reading

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Mentioned in this Paper

Calcinus elegans
Cyartonema elegans
Coleonyx elegans
Biochemical Pathway
Small Nuclear RNA
Cestrum elegans
Clarkia unguiculata
Clathrulina elegans
Cardioglossa elegans
Cymbella elegans

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