Epidemiological connectivity between humans and animals across an urban landscape.

Urbanization is predicted to be a key driver of disease emergence through human exposure to novel, animal-borne pathogens. However, while we suspect that urban landscapes are primed to expose people to novel animal-borne diseases, evidence for the mechanisms by which this occurs is lacking. To address this, we studied how bacterial genes are shared between wild animals, livestock, and humans (n = 1,428) across Nairobi, Kenya-one of the world's most rapidly developing cities. Applying a multilayer network framework, we show that low biodiversity (of both natural habitat and vertebrate wildlife communities), coupled with livestock management practices and more densely populated urban environments, promotes sharing of Escherichia coli-borne bacterial mobile genetic elements between animals and humans. These results provide empirical support for hypotheses linking resource provision, the biological simplification of urban landscapes, and human and livestock demography to urban dynamics of cross-species pathogen transmission at a landscape scale. Urban areas where high densities of people and livestock live in close association with synanthropes (species such as rodents that are more competent reservoirs for zoonotic pathogens) should be prioritized for disease surveillance and control.

Merging Metagenomics and Spatial Epidemiology To Understand the Distribution of Antimicrobial Resistance Genes from Enterobacteriaceae in Wild Owls.

Antimicrobial resistance (AMR) is a well-documented phenomenon in bacteria from many natural ecosystems, including wild animals. However, the specific determinants and spatial distribution of resistant bacteria and antimicrobial resistance genes (ARGs) in the environment remain incompletely understood. In particular, information regarding the importance of anthropogenic sources of AMR relative to that of other biological and ecological influences is lacking. We conducted a cross-sectional study of AMR in great horned owls (Bubo virginianus) and barred owls (Strix varia) admitted to a rehabilitation center in the midwestern United States. A combination of selective culture enrichment and shotgun metagenomic sequencing was used to identify ARGs from Enterobacteriaceae Overall, the prevalence of AMR was comparable to that in past studies of resistant Enterobacteriaceae in raptors, with acquired ARGs being identified in 23% of samples. Multimodel regression analyses identified seasonality and owl age to be important predictors of the likelihood of the presence of ARGs, with birds sampled during warmer months being more likely to harbor ARGs than those sampled during cooler months and with birds in their hatch year being more likely to harbor ß-lactam ARGs than adults. Beyond host-specific determinants, ARG-positive owls were also more likely to be recovered from areas of high agricultural land cover. Spatial clustering analyses identified a significant high-risk cluster of tetracycline resistance gene-positive owls in the southern sampling range, but this could not be explained by any predictor variables. Taken together, these results highlight the complex distribution of AMR in natural environments and suggest that both biological and anthropogenic factors play important roles in determining the emergence and persistence of AMR in wildlife.IMPORTANCE Antimicrobial resistance (AMR) is a multifaceted problem that poses a worldwide threat to human and animal health. Recent reports suggest that wildlife may play an important role in the emergence, dissemination, and persistence of AMR. As such, there have been calls for better integration of wildlife into current research on AMR, including the use of wild animals as biosentinels of AMR contamination in the environment. A One Health approach can be used to gain a better understanding of all AMR sources and pathways, particularly those at the human-animal-environment interface. Our study focuses on this interface in order to assess the effect of human-impacted landscapes on AMR in a wild animal. This work highlights the value of wildlife rehabilitation centers for environmental AMR surveillance and demonstrates how metagenomic sequencing within a spatial epidemiology framework can be used to address questions surrounding AMR complexity in natural ecosystems.

Landscape influence on spatial patterns of meningeal worm and liver fluke infection in white-tailed deer.

Parasites that primarily infect white-tailed deer (Odocoileus virginianus), such as liver flukes (Fascioloides magna) and meningeal worm (Parelaphostrongylus tenuis), can cause morbidity and mortality when incidentally infecting moose (Alces alces). Ecological factors are expected to influence spatial variation in infection risk by affecting the survival of free-living life stages outside the host and the abundance of intermediate gastropod hosts. Here, we investigate how ecology influenced the fine-scale distribution of these parasites in deer in Voyageurs National Park, Minnesota. Deer pellet groups (N = 295) were sampled for the presence of P. tenuis larvae and F. magna eggs. We found that deer were significantly more likely to be infected with P. tenuis in habitats with less upland deciduous forest and more upland mixed conifer forest and shrub, a pattern that mirrored microhabitat differences in gastropod abundances. Deer were also more likely to be infected with F. magna in areas with more marshland, specifically rooted-floating aquatic marshes (RFAMs). The environment played a larger role than deer density in determining spatial patterns of infection for both parasites, highlighting the importance of considering ecological factors on all stages of a parasite's life cycle in order to understand its occurrence within the definitive host.

Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).

Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in understanding transmission dynamics, even for environmentally transmitted microbes like E. coli. This study is the first to use microbial genetics to construct and analyse transmission networks in a wildlife population and highlights the potential utility of an approach integrating microbial genetics with network analysis.

Epidemiological investigation of bovine tuberculosis outbreaks in Uruguay (2011-2013).

Bovine tuberculosis (bTB) is a chronic disease of cattle caused by infection with the Mycobacterium bovis. While bTB prevalence in Uruguay has been low (<11 outbreaks/year) for the past 50 years as a consequence of a national control program, annual incidence increased in 2011 through 2013-15, 26 and 16 infected herds each year, raising concerns from livestock stakeholders and the government. The goal of this study was to assess the spatial dynamics of bTB in Uruguay from 2011 to 2013 and the association between bTB and potential demographic and movement risk factors at the herd level using data provided by the Uruguayan Ministry of Livestock, Agriculture, and Fisheries. Clustering of incident outbreaks was assessed using the Cuzick-Edwards' test and the Bernoulli model of the spatial scan statistic, and a conditional multivariable logistic regression model was used to assess risk factors associated with bTB in a subset of Uruguayan dairy farms. Significant (P<0.05) global clustering was detected in 2012, while high-risk local clusters were detected in southwestern (2011, 2012, 2013), northwestern (2012), and southeastern (2012) Uruguay. Increased risk of bTB in different regions of Uruguay suggests a potential role of animal movements in disease dissemination. Larger herds, higher numbers of animals purchased, and incoming steers to the farm were associated with increased odds of breaking with bTB, in agreement with previous studies but also suggesting other additional sources of risk. These results will contribute to enhanced effectiveness of bTB control programs in Uruguay with the ultimate objective of preventing or mitigating the impact of the disease in the human and animal populations of the country.

Factors Affecting Herd Status for Bovine Tuberculosis in Dairy Cattle in Northern Thailand.

The objective of this case-control study was to identify farm-level risk factors associated with bovine tuberculosis (bTB) in dairy cows in northern Thailand. Spatial analysis was performed to identify geographical clustering of case-farms located in Chiang Mai and Chiang Rai provinces in northern Thailand. To identify management factors affecting bTB status, a matched case-control study was conducted with 20 case-farms and 38 control-farms. Case-farms were dairy farms with at least single intradermal tuberculin test- (SIT-) reactor(s) in the farms during 2011 to 2015. Control-farms were dairy farms with no SIT-reactors in the same period and located within 5 km from case-farms. Questionnaires were administered for data collection with questions based on epidemiological plausibility and characteristics of the local livestock industry. Data were analyzed using multiple logistic regressions. A significant geographic cluster was identified only in Chiang Mai province (p < 0.05). The risk factor associated with presence of SIT-reactors in dairy herds located in this region was purchasing dairy cows from dealers (OR = 5.85, 95% CI = 1.66-20.58, and p = 0.006). From this study, it was concluded that geographic clustering was identified for dairy farms with SIT-reactors in these provinces, and the cattle movements through cattle dealers increased the risks for SIT-reactor farm status.

Parameter Values for Epidemiological Models of Foot-and-Mouth Disease in Swine.

In the event of a foot-and-mouth disease (FMD) incursion, response strategies are required to control, contain, and eradicate the pathogen as efficiently as possible. Infectious disease simulation models are widely used tools that mimic disease dispersion in a population and that can be useful in the design and support of prevention and mitigation activities. However, there are often gaps in evidence-based research to supply models with quantities that are necessary to accurately reflect the system of interest. The objective of this study was to quantify values associated with the duration of the stages of FMD infection (latent period, subclinical period, incubation period, and duration of infection), probability of transmission (within-herd and between-herd via spatial spread), and diagnosis of a vesicular disease within a herd using a meta-analysis of the peer-reviewed literature and expert opinion. The latent period ranged from 1 to 7 days and incubation period ranged from 1 to 9 days; both were influenced by strain. In contrast, the subclinical period ranged from 0 to 6 days and was influenced by sampling method only. The duration of infection ranged from 1 to 10 days. The probability of spatial spread between an infected and fully susceptible swine farm was estimated as greatest within 5 km of the infected farm, highlighting the importance of possible long-range transmission through the movement of infected animals. Finally, while most swine practitioners are confident in their ability to detect a vesicular disease in an average sized swine herd, a small proportion expect that up to half of the herd would need to show clinical signs before detection via passive surveillance would occur. The results of this study will be useful in within- and between-herd simulation models to develop efficient response strategies in the event an FMD in swine populations of disease-free countries or regions.

Network analysis of cattle movements in Uruguay: Quantifying heterogeneity for risk-based disease surveillance and control.

Movement of livestock between premises is one of the foremost factors contributing to the spread of infectious diseases of livestock. In part to address this issue, the origin and destination for all cattle movements in Uruguay are registered by law. This information has great potential to be used in assessing the risk of disease spread in the Uruguayan cattle population. Here, we analyze cattle movements from 2008 to 2013 using network analysis in order to understand the flows of animals in the Uruguayan cattle industry and to identify targets for surveillance and control measures. Cattle movements were represented as seasonal and annual networks in which farms represented nodes and nodes were linked based on the frequency and quantity of cattle moved. At the farm level, the distribution of the number of unique farms each farm is connected to through outgoing and incoming movements, as well as the number of animals moved, was highly right-skewed; the majority of farms had few to no contacts, whereas the 10% most highly connected farms accounted for 72-83% of animals moved annually. This extreme level of heterogeneity in movement patterns indicates that some farms may be disproportionately important for pathogen spread. Different production types exhibited characteristic patterns of farm-level connectivity, with some types, such a dairies, showing consistently higher levels of centrality. In addition, the observed networks were characterized by lower levels of connectivity and higher levels of heterogeneity than random networks of the same size and density, both of which have major implications for disease dynamics and control strategies. This represents the first in-depth analysis of farm-level livestock movements within South America, and highlights the importance of collecting livestock movement data in order to understand the vulnerability of livestock trade networks to invasion by infectious diseases.