Identifying spatio-temporal patterns of transboundary disease spread: examples using avian influenza H5N1 outbreaks

Characterizing spatio-temporal patterns among epidemics in which the mechanism of spread is uncertain is important for generating disease spread hypotheses, which may in turn inform disease control and prevention strategies. Using a dataset representing three phases of highly pathogenic avian influenza H5N1 outbreaks in village poultry in Romania, 2005–2006, spatio-temporal patterns were characterized. We first fit a set of hierarchical Bayesian models that quantified changes in the spatio-temporal relative risk for each of the 23 affected counties. We then modeled spatial synchrony in each of the three epidemic phases using non-parametric covariance functions and Thin Plate Spline regression models. We found clear differences in the spatio-temporal patterns among the epidemic phases (local versus regional correlated processes), which may indicate differing spread mechanisms (for example wild bird versus human-mediated). Elucidating these patterns allowed us to postulate that a shift in the primary mechanism of disease spread may have taken place between the second and third phases of this epidemic. Information generated by such analyses could assist affected countries in determining the most appropriate control programs to implement, and to allocate appropriate resources to preventing contact between domestic poultry and wild birds versus enforcing bans on poultry movements and quarantine. The methods used in this study could be applied in many different situations to analyze transboundary disease data in which only location and time of occurrence data are reported

Inferring invasive species abundance using removal data from management actions

Evaluation of the progress of management programs for invasive species is crucial for demonstrating impacts to stakeholders and strategic planning of resource allocation. Estimates of abundance before and after management activities can serve as a useful metric of population management programs. However, many methods of estimating population size are too labor intensive and costly to implement, posing restrictive levels of burden on operational programs. Removal models are a reliable method for estimating abundance before and after management using data from the removal activities exclusively, thus requiring no work in addition to management. We developed a Bayesian hierarchical model to estimate abundance from removal data accounting for varying levels of effort, and used simulations to assess the conditions under which reliable population estimates are obtained. We applied this model to estimate site-specific abundance of an invasive species, feral swine (Sus scrofa), using removal data from aerial gunning in 59 site/time-frame combinations (480–19,600 acres) throughout Oklahoma and Texas, USA. Simulations showed that abundance estimates were generally accurate when effective removal rates (removal rate accounting for total effort) were above 0.40. However, when abundances were small

Effects of point-count duration on estimated detection probabilities and occupancy of breeding birds

Increasingly, point-count data are used to estimate occupancy, the probability that a species is present at a given location; occupancy accounts for imperfect detection, the probability that a species is detected given that it is present. To our knowledge, effects of sampling duration on inferences from models of bird occupancy have not been evaluated. Our objective was to determine whether changing count duration from 5 to 8min affected inferences about the occupancy of birds sampled in the Chesapeake Bay Lowlands (eastern United States) and the central and western Great Basin (western United States) in 2012 and 2013. We examined the proportion of species (two doves, one cuckoo, two swifts, five hummingbirds, 11 woodpeckers, and 122 passerines) for which estimates of detection probability were 0.3. For species with single-season detection probabilities 0.3, we compared occupancy estimates derived from 5- and 8-min counts. We also compared estimates for three species sampled annually for 5yr in the central Great Basin. Detection probabilities based on both the 5- and 8-min counts were 0.3 for 40% 3% of the species in an ecosystem. Extending the count duration from 5 to 8min increased the detection probability to 0.3 for 5% +/- 0.5% of the species. We found no difference in occupancy estimates that were based on 5- versus 8-min counts for species sampled over two or five consecutive years. However, for 97% of species sampled over 2yr, precision of occupancy estimates that were based on 8-min counts averaged 12% +/- 2% higher than those based on 5-min counts. We suggest that it may be worthwhile to conduct a pilot season to determine the number of locations and surveys needed to achieve detection probabilities that are sufficiently high to estimate occupancy for species of interest

Hydrological and associated biogeochemical consequences of rapid global warming during the Paleocene-Eocene Thermal Maximum

The Paleocene-Eocene Thermal Maximum (PETM) hyperthermal, ~ 56 million years ago (Ma), is the most dramatic example of abrupt Cenozoic global warming. During the PETM surface temperatures increased between 5 and 9 °C and the onset likely took < 20 kyr. The PETM provides a case study of the impacts of rapid global warming on the Earth system, including both hydrological and associated biogeochemical feedbacks, and proxy data from the PETM can provide constraints on changes in warm climate hydrology simulated by general circulation models (GCMs). In this paper, we provide a critical review of biological and geochemical signatures interpreted as direct or indirect indicators of hydrological change at the PETM, explore the importance of adopting multi-proxy approaches, and present a preliminary model-data comparison. Hydrological records complement those of temperature and indicate that the climatic response at the PETM was complex, with significant regional and temporal variability. This is further illustrated by the biogeochemical consequences of inferred changes in hydrology and, in fact, changes in precipitation and the biogeochemical consequences are often conflated in geochemical signatures. There is also strong evidence in many regions for changes in the episodic and/or intra-annual distribution of precipitation that has not widely been considered when comparing proxy data to GCM output. Crucially, GCM simulations indicate that the response of the hydrological cycle to the PETM was heterogeneous – some regions are associated with increased precipitation – evaporation (P – E), whilst others are characterised by a decrease. Interestingly, the majority of proxy data come from the regions where GCMs predict an increase in PETM precipitation. We propose that comparison of hydrological proxies to GCM output can be an important test of model skill, but this will be enhanced by further data from regions of model-simulated aridity and simulation of extreme precipitation events

A benefit-cost analysis decision framework for mitigation of disease transmission at the wildlife–livestock interface

The economics of managing disease transmission at the wildlife–livestock interface have received heightened attention as agricultural and natural resource agencies struggle to tackle growing risks to animal health. In the fiscal landscape of increased scrutiny and shrinking budgets, resource managers seek to maximize the benefits and minimize the costs of disease mitigation efforts. To address this issue, a benefit-cost analysis decision framework was developed to help users make informed choices about whether and how to target disease management efforts in wildlife and livestock populations. Within the context of this framework, we examined the conclusions of a benefit-cost analysis conducted for vampire bat (Desmodus rotundus) rabies control in Mexico. The benefit-cost analysis decision framework provides a method that can be used to identify, assemble, and measure the components vital to the biological and economic efficiency of animal disease mitigation efforts. The framework can be applied to commercially-raised and free-ranging species at various levels of management – from detailed intervention strategies to broad programmatic actions. The ability of benefit cost analysis to illustrate the benefits of disease management projects per dollar spent allows for the determination of economic efficiency of alternative management actions. We believe this framework will be useful to the broader natural resource management community to maximize returns on financial and other resources invested in wildlife and livestock disease management programs

Continental-scale dynamics of avian influenza in U.S. waterfowl are driven by demography, migration and temperature

Emerging diseases of wildlife origin are increasingly spilling over into humans and domestic animals. Surveillance and risk assessments for transmission between these populations are informed by a mechanistic understanding of the pathogens in wildlife reservoirs. For avian influenza viruses (AIV), much observational and experimental work in wildlife has been conducted at local scales, yet fully understanding their spread and distribution requires assessing the mechanisms acting at both local, (e.g., intrinsic epidemic dynamics), and continental scales, (e.g., long‐distance migration). Here, we combined a large, continental‐scale dataset on low pathogenic, Type A AIV in the United States with a novel network‐based application of bird banding/recovery data to investigate the migration‐based drivers of AIV and their relative importance compared to well‐characterised local drivers (e.g. demography, environmental persistence). We compared among regression models reflecting hypothesized ecological processes and evaluated their ability to predict AIV in space and time using within and out‐of‐sample validation. We found that predictors of AIV were associated with multiple mechanisms at local and continental scales. Hypotheses characterising local epidemic dynamics were strongly supported, with age, the age‐specific aggregation of migratory birds in an area and temperature being the best predictors of infection. Hypotheses defining larger, network‐based features of the migration processes, such as clustering or between‐cluster mixing explained less variation but were also supported. Therefore, our results support a role for local processes in driving the continental distribution of AIV

Environmental and Demographic Determinants of Avian Influenza Viruses in Waterfowl across the Contiguous United States

Outbreaks of avian influenza in North American poultry have been linked to wild waterfowl. A first step towards understanding where and when avian influenza viruses might emerge from North American waterfowl is to identify environmental and demographic determinants of infection in their populations. Laboratory studies indicate water temperature as one determinant of environmental viral persistence and we explored this hypothesis at the landscape scale. We also hypothesized that the interval apparent prevalence in ducks within a local watershed during the overwintering season would influence infection probabilities during the following breeding season within the same local watershed. Using avian influenza virus surveillance data collected from 19,965 wild waterfowl across the contiguous United States between October 2006 and September 2009 We fit Logistic regression models relating the infection status of individual birds sampled on their breeding grounds to demographic characteristics, temperature, and interval apparent prevalence during the preceding overwintering season at the local watershed scale. We found strong support for sex, age, and species differences in the probability an individual duck tested positive for avian influenza virus. In addition, we found that for every seven days the local minimum temperature fell below zero, the chance an individual would test positive for avian influenza virus increased by 5.9 percent. We also found a twelve percent increase in the chance an individual would test positive during the breeding season for every ten percent increase in the interval apparent prevalence during the prior overwintering season. These results suggest that viral deposition in water and sub-freezing temperatures during the overwintering season may act as determinants of individual level infection risk during the subsequent breeding season. Our findings have implications for future surveillance activities in waterfowl and domestic poultry populations. Further study is needed to identify how these drivers might interact with other host-specific infection determinants, such as species phylogeny, immunological status, and behavioral characteristics