Quantitative Risk Assessment for the Introduction of Bovine Leukemia Virus-Infected Cattle Using a Cattle Movement Network Analysis

Descriptive network analysis and the influence of timescale on centrality and cohesion metrics from a system of between-herd dairy cow movements in Ontario, Canada

Previous research has demonstrated that static monthly networks of between-herd dairy cow movements in Ontario, Canada were highly fragmented, reducing potential for large-scale outbreaks. Extrapolating results from static networks can become problematic for diseases with an incubation period that exceeds the timescale of the network. The objectives of this research were to: 1) describe the networks of dairy cow movements in Ontario, and 2) describe the changes that occur among network analysis metrics when conducted at seven different timescales. Networks of dairy cow movements were created using Lactanet Canada milk recording data collected in Ontario between 2009 and 2018. Centrality and cohesion metrics were calculated after aggregating the data at seven timescales: weekly, monthly, semi-annual, annual, biennial, quinquennial, and decennial. There were 50,598 individual cows moved between Lactanet-enrolled farms, representing approximately 75% of provincially registered dairy herds. Most movements occurred over short distances (median = 39.18 km), with fewer long-range movements (maximum = 1150.80 km). The number of arcs increased marginally relative to the number of nodes with longer network timescales. Both mean out-degree, and mean clustering coefficients increased disproportionately with increasing timescale. Conversely, mean network density decreased with increasing timescale. The largest weak and strong components at the monthly timescale were small relative to the full network (267 and 4 nodes), whereas yearly networks had much higher values (2213 and 111 nodes). Higher relative connectivity in networks with longer timescales suggests pathogens with long incubation periods and animals with subclinical infection present increased potential for wide-spread disease transmission among dairy farms in Ontario. Careful consideration of disease-specific dynamics should be made when using static networks to model disease transmission among dairy cow populations.

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance

Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.

Emerging infectious diseases may spread across pig trade networks in Thailand once introduced: a network analysis approach

In Thailand, pork is one of the most consumed meats nationwide. Pig farming is hence an important business in the country. However, 95% of the farms were considered smallholders raising only 50 pigs or less. With limited budgets and resources, the biosecurity level in these farms is relatively low. Pig movements have been previously identified as a risk factor in the spread of infectious diseases. Therefore, the present study aimed to explicitly analyze the pig movement network structure and assess its vulnerability to the spread of emerging diseases in Thailand. We used official electronic records of nationwide pig movements throughout the year 2021 to construct a directed weighted one-mode network. Degree centrality, degree distribution, connected components, network community, and modularity were measured to explore the network architectures and properties. In this network, 484,483 pig movements were captured. In which, 379,948 (78.42%) were moved toward slaughterhouses and hence excluded from further analyses. From the remaining links, we suggested that the pig movement network in Thailand was vulnerable to the spread of emerging infectious diseases. Within the network, we found a strongly connected component (SCC) connecting 1044 subdistricts (38.6% of the nodes), a giant weakly connected component (GWCC) covering 98.2% of the nodes (2654/2704), and inter-regional communities with overall network modularity of 0.68. The disease may rapidly spread throughout the country. A better understanding of the nationwide pig movement networks is helpful in tailoring control interventions to cope with the newly emerged diseases once introduced.

Bovine Leukaemia Virus: Current Epidemiological Circumstance and Future Prospective

Bovine leukaemia virus (BLV) is a deltaretrovirus that is closely related to human T-cell leukaemia virus types 1 and 2 (HTLV-1 and -2). It causes enzootic bovine leukosis (EBL), which is the most important neoplastic disease in cattle. Most BLV-infected cattle are asymptomatic, which potentiates extremely high shedding rates of the virus in many cattle populations. Approximately 30% of them show persistent lymphocytosis that has various clinical outcomes; only a small proportion of animals (less than 5%) exhibit signs of EBL. BLV causes major economic losses in the cattle industry, especially in dairy farms. Direct costs are due to a decrease in animal productivity and in cow longevity; indirect costs are caused by restrictions that are placed on the import of animals and animal products from infected areas. Most European regions have implemented an efficient eradication programme, yet BLV prevalence remains high worldwide. Control of the disease is not feasible because there is no effective vaccine against it. Therefore, detection and early diagnosis of the disease are essential in order to diminish its spreading and the economic losses it causes. This review comprises an overview of bovine leukosis, which highlights the epidemiology of the disease, diagnostic tests that are used and effective control strategies.

Transmission Dynamics of Bovine Viral Diarrhea Virus in Hokkaido, Japan by Phylogenetic and Epidemiological Network Approaches

Bovine viral diarrhea (BVD) caused by BVD virus (BVDV) leads to economic loss worldwide. Cattle that are persistently infected (PI) with BVDV are known to play an important role in viral transmission in association with the animal movement, as they shed the virus during their lifetime. In this research, the "hot spot" for BVD transmission was estimated by combining phylogenetic and epidemiological analyses for PI cattle and cattle that lived together on BVDV affected farms in Tokachi district, Hokkaido prefecture, Japan. Viral isolates were genetically categorized into BVDV-1a, 1b, and 2a, based on the nucleotide sequence of the entire E2 region. In BVDV genotype 1, subgenotype b (BVDV-1b), cluster I was identified as the majority in Tokachi district. Network analysis indicated that 12 of the 15 affected farms had cattle movements from other facilities (PI-network) and farms affected with BVDV-1b cluster I consisted of a large network. It was implied that the number of cattle movements themselves would be a risk of BVD transmission, using the PageRank algorithm. Therefore, these results demonstrate that cattle movements would contribute to disease spread and the combination of virological and epidemiological analysis methods would be beneficial in determining possible virus transmission routes.

Molecular Characterization of the env Gene of Bovine Leukemia Virus in Cattle from Pakistan with NGS-Based Evidence of Virus Heterogeneity

Characterization of the global genetic diversity of the bovine leukemia virus (BLV) is an ongoing international research effort. Up to now BLV sequences have been classified into eleven distinct genotypes. Although BLV genotyping and molecular analysis of field isolates were reported in many countries, there is no report describing BLV genotypes present in cattle from Pakistan. In this study we examined 27 env gene sequences from BLV-infected cattle coming from four farms located in Khyber Pakhtunkwa, Gilgit Baltisan and Punjab provinces. Phylogenetic analyses revealed the classification of Pakistani sequences into genotypes G1 and G6. The alignment with the FLK-BLV sequence revealed the presence of 45 mutations, namely, seven in genotype G1 and 33 in genotype G6. Five mutations were found in both, G1 and G6 genotypes. Twelve amino acid substitutions were found in the analyzed sequences, of which only one P264S was specific for sequences from Pakistan. Furthermore, a certain degree of nucleotide heterogeneity was identified by NGS. These results highlight the need for further study on the importance of genetic variability of BLV, especially in the context of its pathogenicity and potential effect on serological detection.