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

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 e

SUBMITTER: Proverbio D 

PROVIDER: S-EPMC9000113 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature