Biology subjects

Dougherty, E. R.

Publications and source records attributed to Dougherty, E. R..

3 recordsLinked to original sources

bioRxiv2018

Using movement data to estimate contact rates in a simulated environmentally-transmitted disease system

Agent-based models have become important tools in ecology, particularly in the study of infectious disease dynamics. Simulations of near-continuous movement paths guided by empirical data offer new avenues of investigation into disease transmission. Here, we simulate the spatiotemporal transmission dynamics of anthrax, the acute disease caused by the bacterium Bacillus anthracis, a pathogen transmitted primarily via environmental reservoirs. We explore how calculations of the probabilities of contact between a host and infectious reservoirs are affected by the scale and method of analysis. At both the landscape and individual scales, empirical movement tracks offer previously unattainable estimates of impacts of movement decisions on contact rate metrics. However, the analytical method selected for the calculation of the probability of contact has notable impacts on the resulting estimates, with convex polygons virtually canceling out variation, and unions of local convex hulls (LoCoH methods) and space-time prisms reflecting reasonable variation, but differing in the magnitude of their estimates. The explicit consideration of behavioral states along movement pathways also impacts evaluations of exposure risk, though its effects differ across methods of analysis. Ultimately, simulations demonstrate that the incorporation of movement data into pathogen transmission analyses helps clarify the role of movement processes underlying the observed dynamics of infectious disease.

ecology↗

bioRxiv2017

Going through the motions: incorporating movement analyses into disease research

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviors, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesize the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology, and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions expanding our understanding of host-pathogen dynamics, and improving our predictive capacity for wildlife and even human diseases.

ecology↗

bioRxiv2017

A cross-validation-based approach for delimiting reliable home range estimates

BackgroundWith decreasing costs of GPS telemetry devices, data repositories of animal movement paths are increasing almost exponentially in size. A series of complex statistical tools have been developed in conjunction with this increase in data. Each of these methods offers certain improvements over previously proposed methods, but each has certain assumptions or shortcomings that make its general application difficult. In the case of the recently developed Time Local Convex Hull (T-LoCoH) method, the subjectivity in parameter selection serves as one of the primary impediments to its more widespread use. While there are certain advantages to the flexibility it offers for question-driven research, the lack of an objective approach for parameter selection may prevent some users from exploring the benefits of the method.\n\nResultsHere we present a cross-validation-based approach for selecting parameter values to optimize the T-LoCoH algorithm. We demonstrate the utility of the approach using a case study from the Etosha National Park anthrax system. Utilizing the proposed algorithm, rather than the guidelines in the T-LoCoH documentation, results in significantly different values for derived site fidelity metrics.\n\nConclusionsDue to its basis in principles of cross-validation, the application of this method offers a more objective approach than the relatively subjective guidelines set forth in the T-LoCoH documentation and enables a more accurate basis for the comparison of home ranges among individuals and species, as well as among studies.

ecology↗