Infectious Diseases In Animal Populations Research Articles

Data-driven agent-based model building for animal movement through Exploratory Data Analysis

Fields studying animal movement are data-starved due to large monetary and time costs of data collection, and, moreover, not all quantities of interest can be measured. Despite limited data, diverse models have been developed to study animal movement. Many of these models are based on random walks that use Gaussian noise. Examinations of real movement data show that the assumptions made in these models are not always valid on all spatial and time scales, and these shortcomings suggest that new models may be needed. We provide methods for developing and training more realistic models, specifically agent-based models of animal movement, by taking a data-driven approach. We took an exploratory data analysis (EDA) approach that allowed us to develop a model that best reproduced the movement patterns we observed in a real dataset of animal locations. In our EDA, we examined distributions of positions, calculated the autocorrelation of the movement data, used Fourier analysis, calculated mean-squared displacements, and tested for correlations. We introduce the fused-lasso regression-analysis method for identifying large and sudden changes in position through a non-parametric fit that is sensitive to discontinuities. We introduce the copula and kernel-density estimates as methods for approximating coordinate-system-independent movement correlations from the marginal location differences represented in our data set; use of the copula allows us to create correlated, non-Gaussian noise. With the insights gained from our EDA, we created a Langevin model that describes the movements of an individual animal, features non-Gaussian noise and incorporates multiple movement patterns. We extended this model to an agent-based model that describes the movements of several groups of deer. We compared our Langevin model to three models built on different assumptions that result in substantial differences in the area covered by an animal. Using our agent-based model, we simulated three groups of deer with parameters sampled from our data to illustrate the amount of area covered by deer in our model and the ability of groups to overlap in space. This overlapping behavior is an important feature of models of processes driven by interactions between animals, such as infectious diseases; our model could be useful for studying the spread of infectious diseases in animal populations, such as chronic wasting disease. While we have applied our data-driven methods to animal movement, these methods are widely applicable to developing and training models using a wide range of data sources.

Using social network analysis to inform disease control interventions

Contact patterns between individuals are an important determinant for the spread of infectious diseases in populations. Social network analysis (SNA) describes contact patterns and thus indicates how infectious pathogens may be transmitted. Here we explore network characteristics that may inform the development of disease control programes.This study applies SNA methods to describe a livestock movement network of 180 farms in New Zealand from 2006 to 2010. We found that the number of contacts was overall consistent from year to year, while the choice of trading partners tended to vary. This livestock movement network illustrated how a small number of farms central to the network could play a potentially dominant role for the spread of infection in this population. However, fragmentation of the network could easily be achieved by “removing” a small proportion of farms serving as bridges between otherwise isolated clusters, thus decreasing the probability of large epidemics.This is the first example of a comprehensive analysis of pastoral livestock movements in New Zealand. We conclude that, for our system, recording and exploiting livestock movements can contribute towards risk-based control strategies to prevent and monitor the introduction and the spread of infectious diseases in animal populations.

Investigation of the Occurrence of Porcine Reproductive and Respiratory Virus in Swine Herds Participating in an Area Regional Control and Elimination Project in Ontario, Canada.

The main goal of this study was to investigate the occurrence of porcine reproductive and respiratory syndrome virus (PRRSV)-specific genotypes in swine sites in Ontario (Canada) using molecular, spatial and network data from a porcine reproductive and respiratory syndrome (PRRS) regional control project. For each site, location, animal movement service provider (truck companies), PRRSV status and sequencing data of the open reading frame 5 (ORF5) were obtained. Three-kilometre buffers were created to evaluate neighbourhood characteristics for each site. Social network analysis was conducted on swine sites and trucking companies to assemble the network and define network components. Three different PRRSV genotypes were used as outcomes for statistical analysis based on the region's phylogenetic tree of the ORF5. Multivariable exact logistic regression was conducted to investigate the association between being positive for a specific genotype and two main exposures of interest: (i) having at least one neighbour within threekm also positive for the same genotype outside the production system and (ii) having at least one positive site for the same genotype in the same truck network component outside the production system. Results showed that the importance of area spread and truck network on PRRSV occurrence differed according to genotype. Additionally, the Ontario PRRS database appears suitable for conducting regional disease investigations. Finally, the use of relatively new tools available for network, spatial and molecular analysis could be useful in investigation, control and prevention of endemic infectious diseases in animal populations.

Transmission Patterns of Pinworms in Two Sympatric Congeneric Primate Species

Understanding pathogen transmission is essential to addressing the dynamics of infectious diseases in animal populations. Directly transmitted parasites spread in host populations via 1) contact with infected individuals and 2) contact with contaminated substrates. Although studies exist that support social or ranging effects on transmission, it is less clear how these factors interact. We test the hypothesis that a combination of social, ranging, diet, and intrinsic factors account for Trypanoxyuris minutus (pinworm) infections in sympatric howler species Alouatta palliata and A. pigra. We collected 211 howler fecal samples from 34 adults living in four groups, two of each species, in Tabasco (Mexico), and calculated pinworm prevalence and eggs per gram of feces (EPG). We followed each group for 80 h to determine ranging, diet, frequency of contact, and conspecific proximity. Prevalence of Trypanoxyurisminutus was high, with 82% of all individuals infected. Logistic modeling indicated that pinworm prevalence was positively associated with proximity and the proportion of group members contacted by focal individuals. Although EPG results should be interpreted cautiously owing to variable egg excretion, this index was also positively associated with proximity and the proportion of group members that were contacted, as well as with dietary diversity and use of non-tree foods. Neither intrinsic factors such as species and sex, nor group and population level variables, such as group and home range size, home range overlap, and intensity of range use, were significant predictors of pinworm infection. We conclude that both sociality and feeding behavior are key factors in infection dynamics of Trypanoxyuris minutus in sympatric Alouatta palliata and A. pigra, confirming that contact with infected conspecifics and contaminated substrates are important mechanisms for directly transmitted parasites.

Use of data mining techniques to investigate disease risk classification as a proxy for compromised biosecurity of cattle herds in Wales

BackgroundBiosecurity is at the forefront of the fight against infectious diseases in animal populations. Few research studies have attempted to identify and quantify the effectiveness of biosecurity against disease introduction or presence in cattle farms and, when done, they have relied on the collection of on-farm data. Data on environmental, animal movement, demographic/husbandry systems and density disease determinants can be collated without requiring additional specific on-farm data collection activities, since they have already been collected for some other purposes. The aim of this study was to classify cattle herds according to their risk of disease presence as a proxy for compromised biosecurity in the cattle population of Wales in 2004 for risk-based surveillance purposes.ResultsThree data mining methods have been applied: logistic regression, classification trees and factor analysis. Using the cattle holding population in Wales, a holding was considered positive if at least bovine TB or one of the ten most frequently diagnosed infectious or transmissible non-notifiable diseases in England and Wales, according to the Veterinary Investigation Surveillance Report (VIDA) had been diagnosed in 2004. High-risk holdings can be described as open large cattle herds located in high-density cattle areas with frequent movements off to many locations within Wales. Additional risks are associated with the holding being a dairy enterprise and with a large farming area.ConclusionThis work has demonstrated the potential of mining various livestock-relevant databases to obtain generic criteria for individual cattle herd biosecurity risk classification. Despite the data and analytical constraints the described risk profiles are highly specific and present variable sensitivity depending on the model specifications. Risk profiling of farms provides a tool for designing targeted surveillance activities for endemic or emerging diseases, regardless of the prior amount of information available on biosecurity at farm level. As the delivery of practical evidence-based information and advice is one of the priorities of Defra's new Animal Health and Welfare Strategy (AHWS), data-driven models, derived from existing databases, need to be developed that can then be used to inform activities during outbreaks of endemic diseases and to help design surveillance activities.

Simulation analysis of the effect of herd immunity and age structure on infection of a cattle herd with bluetongue viruses in Queensland, Australia

A state-transition model based on Leslie matrix formulation was used to investigate the effects of herd immunity and age structure on the infection of a simulated cattle herd with bluetongue viruses under Australian climatic conditions. Increasing duration of immunity decreased the prevalence of infection. A duration of immunity of 33 months was consistent with prevalence estimates made from previous serological studies of bluetongue virus. Herd prevalence displayed slowly dampening cyclical variation over time (most pronounced when a short duration of immunity was simulated). Increasing calving and mortality risk rates in the simulated herd increased prevalence, whereas increasing age at first calving decreased prevalence. Manipulation of calving rates had the greatest effect on the predicted prevalence of infection in the herd. Simulation of a number of herd-management scenarios suggested that management systems in which cattle are bred early and where high calving rates are achieved are likely to contribute to high levels of infection with bluetongue viruses. Results confirm the importance of management factors in influencing the prevalence of infectious diseases in animal populations.