Early warning signals of infectious disease transitions: a review

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.

Estimating the basic reproduction number from noisy daily data

“…In analyzing European data, some authors, e.g. Proverbio et al (2022) , resorted to the recent literature on early warning signals of disease re-emergence, EWS ( Southall et al, 2021 ) in order to better assess the beginning of a new wave, an important practical issue. For our analysis, we proceeded empirically, and considered the time interval from September 1st 2020 till January 31st 2021, a period when the second wave was surely under way.…”

How early can an upcoming critical transition be detected?

“…population collapse [33], recovery of a ecosystem [32] and the emergence of COVID-19 [34], although it has not been formally tested with a simulated control environment [35].…”

“…Here, a bootstrapping technique is proposed, which preserves the original features of the observed data and can be used to create the null distribution. However, the use of statistical tests and the misinterpretation of p-values have been previously criticised [4,38]; and other approaches have been proposed for estimating the null distribution.…”

“…For infectious disease systems, criticality typically occurs when the basic reproduction number equals 1. Research has shown that infectious disease systems undergo the phenomenon of Critical Slowing Down (CSD) when the basic reproduction number, R 0 , crosses through the critical transition at R 0 = 1 [1][2][3], reviewed in [4].…”

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

“…An approach derived from bifurcation theory on networks was applied on COVID-19 data in [ 19 ]. A review can be found in [ 20 ]. Performing more tests is thus a necessary next step towards the application of EWS in routine surveillance procedures.…”