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

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, t

SUBMITTER: Southall E 

PROVIDER: S-EPMC7531856 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature