Dynamical footprints enable detection of disease emergence
Published By: PLoS ONE | Published Date: May, 20 , 2020Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data.
Author(s): Tobias S Brett, Pejman Rohani | Posted on: May 28, 2020 | Views() | Download (81)