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Performance of early warning signals for disease re-emergence: A case study on COVID-19 data.

Published version
Peer-reviewed

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Abstract

Developing measures for rapid and early detection of disease re-emergence is important to perform science-based risk assessment of epidemic threats. In the past few years, several early warning signals (EWS) from complex systems theory have been introduced to detect impending critical transitions and extend the set of indicators. However, it is still debated whether they are generically applicable or potentially sensitive to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data has, so far, been limited. Hence, verifying EWS performance remains a challenge. In this study, we tackle this question by analyzing the performance of common EWS, such as increasing variance and autocorrelation, in detecting the emergence of COVID-19 outbreaks in various countries. Our work illustrates that these EWS might be successful in detecting disease emergence when some basic assumptions are satisfied: a slow forcing through the transitions and not-fat-tailed noise. In uncertain cases, we observe that noise properties or commensurable time scales may obscure the expected early warning signals. Overall, our results suggest that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to certain features of the underlying dynamics. Our findings thus pave a connection between theoretical and empirical studies, constituting a further step towards the application of EWS indicators for informing public health policies.

Description

Keywords

Research Article, Medicine and health sciences, Physical sciences, Computer and information sciences, Engineering and technology, Research and analysis methods, People and places

Journal Title

PLoS Comput Biol

Conference Name

Journal ISSN

1553-734X
1553-7358

Volume Title

18

Publisher

Public Library of Science (PLoS)
Sponsorship
Fonds National de la Recherche Luxembourg (PRIDE DTU CriTiCS, ref 10907093)
Fonds National de la Recherche Luxembourg (PRIDE17/12244779/PARK-QC)