Population structure, intergroup interaction, and human contact govern infectious disease impacts in mountain gorilla populations.

Infectious zoonotic diseases are a threat to wildlife conservation and global health. They are especially a concern for wild apes, which are vulnerable to many human infectious diseases. As ecotourism, deforestation, and great ape field research increase, the threat of human-sourced infections to wild populations becomes more substantial and could result in devastating population declines. The endangered mountain gorillas (Gorilla beringei beringei) of the Virunga Massif in east-central Africa suffer periodic disease outbreaks and are exposed to infections from human-sourced pathogens. It is important to understand the possible risks of disease introduction and spread in this population and how human contact may facilitate disease transmission. Here we present and evaluate an individual-based, stochastic, discrete-time disease transmission model to predict epidemic outcomes and better understand health risks to the Virunga mountain gorilla population. To model disease transmission we have derived estimates for gorilla contact, interaction, and migration rates. The model shows that the social structure of gorilla populations plays a profound role in governing disease impacts with subdivided populations experiencing less than 25% of the outbreak levels of a single homogeneous population. It predicts that gorilla group dispersal and limited group interactions are strong factors in preventing widespread population-level outbreaks of infectious disease after such diseases have been introduced into the population. However, even a moderate amount of human contact increases disease spread and can lead to population-level outbreaks.

Preparedness and disaster response training for veterinary students: literature review and description of the North Carolina State University Credentialed Veterinary Responder Program.

The nation's veterinary colleges lack the curricula necessary to meet veterinary demands for animal/public health and emergency preparedness. To this end, the authors report a literature review summarizing training programs within human/veterinary medicine. In addition, the authors describe new competency-based Veterinary Credential Responder training at North Carolina State University College of Veterinary Medicine (NCSU CVM). From an evaluation of 257 PubMed-derived articles relating to veterinary/medical disaster training, 14 fulfilled all inclusion requirements (nine were veterinary oriented; five came from human medical programs). Few offered ideas on the core competencies required to produce disaster-planning and response professionals. The lack of published literature in this area points to a need for more formal discussion and research on core competencies. Non-veterinary articles emphasized learning objectives, commonly listing an incident command system, the National Incident Management System, teamwork, communications, and critical event management/problem solving. These learning objectives were accomplished either through short-course formats or via their integration into a larger curriculum. Formal disaster training in veterinary medicine mostly occurs within existing public health courses. Much of the literature focuses on changing academia to meet current and future needs in public/animal health disaster-preparedness and careers. The NCSU CVM program, in collaboration with North Carolina Department of Agriculture and Consumer Service, Emergency Programs and University of North Carolina at Chapel Hill School of Public Health, operates as a stand-alone third-year two-week core-curriculum training program that combines lecture, online, experiential, and group exercises to meet entry-level federal credentialing requirements. The authors report here its content, outcomes, and future development plans.

Second-order modeling of variability and uncertainty in microbial hazard characterization.

This study describes an analytical framework that permits quantitative consideration of variability and uncertainty in microbial hazard characterization. Second-order modeling that used two-dimensional Monte Carlo simulation and stratification into homogeneous population subgroups was applied to integrate uncertainty and variability. Specifically, the bootstrap method was used to simulate sampling error due to the limited sample size in microbial dose-response modeling. A data set from human feeding trials with Campylobacter jejuni was fitted to the log-logistic dose-response model, and results from the analysis of FoodNet surveillance data provided further information on variability and uncertainty in Campylobacter susceptibility due to the effect of age. Results of our analyses indicate that uncertainty associated with dose-response modeling has a dominating influence on the analytical outcome. In contrast, inclusion of the age factor has a limited impact. While the advocacy of more closely modeling variability in hazard characterization is warranted, the characterization of key sources of uncertainties and their consistent propagation throughout a microbial risk assessment actually appear of greater importance.

Hood of the truck statistics for food animal practitioners.

This article offers some tips on working with statistics and develops four relatively simple procedures to deal with most kinds of data with which veterinarians work. The criterion for a procedure to be a "Hood of the Truck Statistics" (HOT Stats) technique is that it must be simple enough to be done with pencil, paper, and a calculator. The goal of HOT Stats is to have the tools available to run quick analyses in only a few minutes so that decisions can be made in a timely fashion. The discipline allows us to move away from the all-too-common guess work about effects and differences we perceive following a change in treatment or management. The techniques allow us to move toward making more defensible, credible, and more quantifiably "risk-aware" real-time recommendations to our clients.