Michael G. Buhnerkempe

The impact of movements and animal density on continental scale cattle disease outbreaks in the United States.

Globalization has increased the potential for the introduction and spread of novel pathogens over large spatial scales necessitating continental-scale disease models to guide emergency preparedness. Livestock disease spread models, such as those for the 2001 foot-and-mouth disease (FMD) epidemic in the United Kingdom, represent some of the best case studies of large-scale disease spread. However, generalization of these models to explore disease outcomes in other systems, such as the United States’s cattle industry, has been hampered by differences in system size and complexity and the absence of suitable livestock movement data. Here, a unique database of US cattle shipments allows estimation of synthetic movement networks that inform a near-continental scale disease model of a potential FMD-like (i.e., rapidly spreading) epidemic in US cattle. The largest epidemics may affect over one-third of the US and 120,000 cattle premises, but cattle movement restrictions from infected counties, as opposed to national movement moratoriums, are found to effectively contain outbreaks. Slow detection or weak compliance may necessitate more severe state-level bans for similar control. Such results highlight the role of large-scale disease models in emergency preparedness, particularly for systems lacking comprehensive movement and outbreak data, and the need to rapidly implement multi-scale contingency plans during a potential US outbreak.

Eight challenges in modelling disease ecology in multi-host, multi-agent systems

Many disease systems exhibit complexities not captured by current theoretical and empirical work. In particular, systems with multiple host species and multiple infectious agents (i.e., multi-host, multi-agent systems) require novel methods to extend the wealth of knowledge acquired studying primarily single-host, single-agent systems. We outline eight challenges in multi-host, multi-agent systems that could substantively increase our knowledge of the drivers and broader ecosystem effects of infectious disease dynamics.

A Bayesian Approach for Modeling Cattle Movements in the United States: Scaling up a Partially Observed Network

Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on Bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.

A national-scale picture of U.S. cattle movements obtained from Interstate Certificate of Veterinary Inspection data

We present the first comprehensive description of how shipments of cattle connect the geographic extent and production diversity of the United States cattle industry. We built a network of cattle movement from a state-stratified 10% systematic sample of calendar year 2009 Interstate Certificates of Veterinary Inspection (ICVI) data. ICVIs are required to certify the apparent health of cattle moving across state borders and allow us to examine cattle movements at the county scale. The majority of the ICVI sample consisted of small shipments (<20 head) moved for feeding and beef production. Geographically, the central plains states had the most connections, correlated to feeding infrastructure. The entire nation was closely connected when interstate movements were summarized at the state level. At the county-level, the U.S. is still well connected geographically, but significant heterogeneities in the location and identity of counties central to the network emerge. Overall, the network of interstate movements is described by a hub structure, with a few counties sending or receiving extremely large numbers of shipments and many counties sending and receiving few shipments. The county-level network also has a very low proportion of reciprocal movements, indicating that high-order network properties may be better at describing a countys importance than simple summaries of the number of shipments or animals sent and received. We suggest that summarizing cattle movements at the state level homogenizes the network and a county level approach is most appropriate for examining processes influenced by cattle shipments, such as economic analyses and disease outbreaks.

Survival and Breeding Transitions for a Reintroduced Bison Population: a Multistate Approach

Abstract The iconic plains bison (Bison bison) have been reintroduced to many places in their former range, but there are few scientific data evaluating the success of these reintroductions or guiding the continued management of these populations. Relying on mark–recapture data, we used a multistate model to estimate bison survival and breeding transition probabilities while controlling for the recapture process. We tested hypotheses in these demographic parameters associated with age, sex, reproductive state, and environmental variables. We also estimated biological process variation in survival and breeding transition probabilities by factoring out sampling variation. The recapture rate of females and calves was high (0.78 ± 0.15 [SE]) and much lower for males (0.41 ± 0.23), especially older males (0.17 ± 0.15). We found that overall bison survival was high (>0.8) and that males (0.80 ± 0.13) survived at lower rates than females (0.94 ± 0.04), but as females aged survival declined (0.89 ± 0.05 for F ≥15 yr old). Lactating and non-lactating females survived at similar rates. We found that females can conceive early (approx. 1.5 yr of age) and had a high probability (approx. 0.8) of breeding in consecutive years, until age 13.5 years, when females that were non-lactating tended to stay in that state. Our results suggest senescence in reproduction and survival for females. We found little support for the effect of climatic covariates on demographic rates, perhaps because the parks current population management goals were predicated from drought-year conditions. This reintroduction has been successful, but continued culling actions will need to be employed and an adaptive management approach is warranted. Our demographic approach can be applied to other heavily managed large-ungulate systems with few or no natural predators.

Identification of migratory bird flyways in North America using community detection on biological networks

Migratory behavior of waterfowl populations in North America has traditionally been broadly characterized by four north-south flyways, and these flyways have been central to the management of waterfowl populations for more than 80 yr. However, previous flyway characterizations are not easily updated with current bird movement data and fail to provide assessments of the importance of specific geographical regions to the identification of flyways. Here, we developed a network model of migratory movement for four waterfowl species, Mallard (Anas platyrhnchos), Northern Pintail (A. acuta), American Green-winged Teal (A. carolinensis), and Canada Goose (Branta canadensis), in North America, using bird band and recovery data. We then identified migratory flyways using a community detection algorithm and characterized the importance of smaller geographic regions in identifying flyways using a novel metric, the consolidation factor. We identified four main flyways for Mallards, Northern Pintails, and American Green-winged Teal, with the flyway identification in Canada Geese exhibiting higher complexity. For Mallards, flyways were relatively consistent through time. However, consolidation factors revealed that for Mallards and Green-winged Teal, the presumptive Mississippi flyway was potentially a zone of high mixing between other flyways. Our results demonstrate that the network approach provides a robust method for flyway identification that is widely applicable given the relatively minimal data requirements and is easily updated with future movement data to reflect changes in flyway definitions and management goals.

Utility of mosquito surveillance data for spatial prioritization of vector control against dengue viruses in three Brazilian cities.

BackgroundVector control remains the primary defense against dengue fever. Its success relies on the assumption that vector density is related to disease transmission. Two operational issues include the amount by which mosquito density should be reduced to minimize transmission and the spatio-temporal allotment of resources needed to reduce mosquito density in a cost-effective manner. Recently, a novel technology, MI-Dengue, was implemented city-wide in several Brazilian cities to provide real-time mosquito surveillance data for spatial prioritization of vector control resources. We sought to understand the role of city-wide mosquito density data in predicting disease incidence in order to provide guidance for prioritization of vector control work.MethodsWe used hierarchical Bayesian regression modeling to examine the role of city-wide vector surveillance data in predicting human cases of dengue fever in space and time. We used four years of weekly surveillance data from Vitoria city, Brazil, to identify the best model structure. We tested effects of vector density, lagged case data and spatial connectivity. We investigated the generality of the best model using an additional year of data from Vitoria and two years of data from other Brazilian cities: Governador Valadares and Sete Lagoas.ResultsWe found that city-wide, neighborhood-level averages of household vector density were a poor predictor of dengue-fever cases in the absence of accounting for interactions with human cases. Effects of city-wide spatial patterns were stronger than within-neighborhood or nearest-neighborhood effects. Readily available proxies of spatial relationships between human cases, such as economic status, population density or between-neighborhood roadway distance, did not explain spatial patterns in cases better than unweighted global effects.ConclusionsFor spatial prioritization of vector controls, city-wide spatial effects should be given more weight than within-neighborhood or nearest-neighborhood connections, in order to minimize city-wide cases of dengue fever. More research is needed to determine which data could best inform city-wide connectivity. Once these data become available, MI-dengue may be even more effective if vector control is spatially prioritized by considering city-wide connectivity between cases together with information on the location of mosquito density and infected mosquitos.

Inferring infection hazard in wildlife populations by linking data across individual and population scales

Abstract Our ability to infer unobservable disease‐dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time‐averaged value and are based on population‐level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within‐host processes to FOI is needed. Specifically, within‐host antibody kinetics in wildlife hosts can be short‐lived and produce patterns that are repeatable across individuals, suggesting individual‐level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population‐level FOI signal can be recovered from individual‐level antibody kinetics, despite substantial individual‐level variation. In addition to improving inference, the cross‐scale quantitative antibody approach we describe can reveal insights into drivers of individual‐based variation in disease response, and the role of poorly understood processes such as secondary infections, in population‐level dynamics of disease.

Assessment of paper interstate certificates of veterinary inspection used to support disease tracing in cattle

OBJECTIVE To evaluate the differences among each states Interstate Certificate of Veterinary Inspection (ICVI) form and the legibility of data on paper ICVIs used to support disease tracing in cattle. DESIGN Descriptive retrospective cross-sectional study. SAMPLE Examples of ICVIs from 50 states and 7,630 randomly sampled completed paper ICVIs for cattle from 48 states. PROCEDURES Differences among paper ICVI forms from all 50 states were determined. Sixteen data elements were selected for further evaluation of their value in tracing cattle. Completed paper ICVIs for interstate cattle exports in 2009 were collected from 48 states. Each of the 16 data elements was recorded as legible, absent, or illegible on forms completed by accredited veterinarians, and results were summarized by state. Mean values for legibility at the state level were used to estimate legibility of data at the national level. RESULTS ICVIs were inconsistent among states in regard to data elements requested and availability of legible records. A mean ± SD of 70.0 ± 22.1% of ICVIs in each state had legible origin address information. Legible destination address information was less common, with 55.0 ± 21.4% of records complete. Incomplete address information was most often a result of the field having been left blank. Official animal identification was present on 33.1% of ICVIs. CONCLUSIONS AND CLINICAL RELEVANCE The inconsistency among state ICVI forms and quality of information provided on paper ICVIs could lead to delays and the need for additional resources to trace cattle, which could result in continued spread of disease. Standardized ICVIs among states and more thorough recording of information by accredited veterinarians or expanded usage of electronic ICVIs could enhance traceability of cattle during an outbreak.

Epidemiological models to control the spread of information in marine mammals

Socially transmitted wildlife behaviours that create human–wildlife conflict are an emerging problem for conservation efforts, but also provide a unique opportunity to apply principles of infectious disease control to wildlife management. As an example, California sea lions (Zalophus californianus) have learned to exploit concentrations of migratory adult salmonids below the fish ladders at Bonneville Dam, impeding endangered salmonid recovery. Proliferation of this foraging behaviour in the sea lion population has resulted in a controversial culling programme of individual sea lions at the dam, but the impact of such culling remains unclear. To evaluate the effectiveness of current and alternative culling strategies, we used network-based diffusion analysis on a long-term dataset to demonstrate that social transmission is implicated in the increase in dam-foraging behaviour and then studied different culling strategies within an epidemiological model of the behavioural transmission data. We show that current levels of lethal control have substantially reduced the rate of social transmission, but failed to effectively reduce overall sea lion recruitment. Earlier implementation of culling could have substantially reduced the extent of behavioural transmission and, ultimately, resulted in fewer animals being culled. Epidemiological analyses offer a promising tool to understand and control socially transmissible behaviours.