Individual and temporal variation in pathogen load predicts long-term impacts of an emerging infectious disease.

ABSTRACT: Emerging infectious diseases increasingly threaten wildlife populations. Most studies focus on managing short-term epidemic properties, such as controlling early outbreaks. Predicting long-term endemic characteristics with limited retrospective data is more challenging. We used individual-based modeling informed by individual variation in pathogen load and transmissibility to predict long-term impacts of a lethal, transmissible cancer on Tasmanian devil (Sarcophilus harrisii) populations. For this, we employed approximate Bayesian computation to identify model scenarios that best matched known epidemiological and demographic system properties derived from 10 yr of data after disease emergence, enabling us to forecast future system dynamics. We show that the dramatic devil population declin

SUBMITTER: Wells K 

PROVIDER: S-EPMC6415924 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature