Reference : Performance of early warning signals for disease re-emergence: A case study on COVID-...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others
Computational Sciences
http://hdl.handle.net/10993/50892
Performance of early warning signals for disease re-emergence: A case study on COVID-19 data
English
Proverbio, Daniele mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Control >]
Kemp, Francoise mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Integrative Cell Signalling >]
Magni, Stefano mailto [University of Luxembourg > > >]
Goncalves, Jorge mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Control >]
2022
PLoS Computational Biology
Public Library of Science
18
3
e1009958
Yes (verified by ORBilu)
1553-734X
1553-7358
San Francisco
CA
[en] EWS ; Critical transitions ; COVID-19
[en] 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 charac- teristics 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 emer- gence 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 sat- isfied: 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 moni- toring 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 empiri- cal studies, constituting a further step towards the application of EWS indicators for inform- ing public health policies.
Luxembourg Centre for Systems Biomedicine (LCSB)
Fonds National de la Recherche - FnR
http://hdl.handle.net/10993/50892
10.1371/journal.pcbi.1009958
FnR ; FNR10907093 > Jorge Gon├žalves > CriTICS > Critical Transitions In Complex Systems: From Theory To Applications > 01/11/2016 > 30/04/2023 > 2015

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