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 declines observed thus far are likely attributable to transient dynamics (initial dynamics after disease emergence). Only 21% of matching scenarios led to devil extinction within 100 yr following devil facial tumor disease (DFTD) introduction, whereas DFTD faded out in 57% of simulations. In the remaining 22% of simulations, disease and host coexisted for at least 100 yr, usually with long-period oscillations. Our findings show that pathogen extirpation or hostpathogen coexistence are much more likely than the DFTD-induced devil extinction, with crucial management ramifications. Accounting for individual-level disease progression and the long-term outcome of devil-DFTD interactions at the population-level, our findings suggest that immediate management interventions are unlikely to be necessary to ensure the persistence of Tasmanian devil populations. This is because strong population declines of devils after disease emergence do not necessarily translate into long-term population declines at equilibria. Our modeling approach is widely applicable to other host-pathogen systems to predict disease impact beyond transient dynamics.

Evolution and lineage dynamics of a transmissible cancer in Tasmanian devils

“…DFT1 presents a continued and serious threat to the Tasmanian devil, and predicting the future impact of the disease remains challenging [ 5 , 41 – 43 ]. The spatial and temporal dynamics of DFT1 between 2003 and 2018, described here, reveal not only the trajectories of parallel and competing DFT1 sublineages but also trace the patterns of movement of the diseased devils themselves.…”

Disease control across urban–rural gradients

“…However, with an area-wide spread of COVID-19 in our study area and a concentration of cases in urban communities during the first six months of the epidemic, some general patterns found in model output and empirical data appear to be compatible (K. Wells 2020, personal observations). Given more detailed data of spatio-temporal disease spread and better estimates of epidemiological key parameters, future studies may narrow down the currently intractable large parameter space through statistical approximation methods in order to identify when and how management efforts may results in disease extirpation versus long-term persistence [ 33 ]. Future studies may also account for the various processes that synergistically determine control efficacy and whether a certain level of control can be achieved or not.…”

Activity and social interactions in a wide-ranging specialist scavenger, the Tasmanian devil (Sarcophilus harrisii), revealed by animal-borne video collars

“…Our study thus contributes new information towards building a holistic understanding of the frequency and context of social contacts between devils that are relevant to transmission of DFTD. This body of information is crucial for interpreting social contact networks relevant to transmission of devil facial tumour disease, to predict long-term epidemic outcome [48] and informing management options [49].…”