Early warning of infectious disease outbreaks on cattle-transport networks

Investigating the use of machine learning algorithms to support risk-based animal welfare inspections of cattle and pig farms

In livestock production, animal-related data are often registered in specialised databases and are usually not interconnected, except for a common identifier. Analysis of combined datasets and the possible inclusion of third-party information can provide a more complete picture or reveal complex relationships. The aim of this study was to develop a risk index to predict farms with an increased likelihood for animal welfare violations, defined as non-compliance during on-farm welfare inspections. A data-driven approach was chosen for this purpose, focusing on the combination of existing Swiss government databases and registers. Individual animal-level data were aggregated at the herd level. Since data collection and availability were best for cattle and pigs, the focus was on these two livestock species. We present machine learning models that can be used as a tool to plan and optimise risk-based on-farm welfare inspections by proposing a consolidated list of priority holdings to be visited. The results of previous on-farm welfare inspections were used to calibrate a binary welfare index, which is the prediction goal. The risk index is based on proxy information, such as the participation in animal welfare programmes with structured housing and outdoor access, herd type and size, or animal movement data. Since transparency of the model is critical both for public acceptance of such a data-driven index and farm control planning, the Random Forest model, for which the decision process can be illustrated, was investigated in depth. Using historical inspection data with an overall low prevalence of violations of approximately 4% for both species, the developed index was able to predict violations with a sensitivity of 81.2 and 79.5% for cattle and pig farms, respectively. The study has shown that combining multiple and heterogeneous data sources improves the quality of the models. Furthermore, privacy-preserving methods are applied to a research environment to explore the available data before restricting the feature space to the most relevant. This study demonstrates that data-driven monitoring of livestock populations is already possible with the existing datasets and the models developed can be a useful tool to plan and conduct risk-based animal welfare inspection.

Geographic expansion of Fasciola hepatica (Linnaeus, 1758) due to changes in land use and cover in Brazil

Fasciolosis is caused by parasites of the genus Fasciola, affecting animals and humans worldwide. In South America, the disease is a result of infection with Fasciola hepatica and although animal infections are more frequently reported, the full extent of the impact on human health due to underdiagnosis remains uncertain. This study analyzed changes in land use and the distribution of F. hepatica in bovine livers in Brazil over 18 years. Data on land use and land cover were collected from the Mapbiomas Project. Data on 414,481,963 slaughtered cattle and condemned livers due to F. hepatica infection were obtained from 4,433 municipalities. Joinpoint analysis was used to study the time series, and the Susceptible-Infected-Recovered (SIR) model was utilized to explore the behavior of F. hepatica infection. In the North, pasture areas significantly increased (P = 0.000001), while forested areas decreased (P = 0.000001). The midwestern and northern regions concentrated the highest number (>290 million) of cattle slaughtered in Brazil. More than 2 million bovine livers were infected by F. hepatica. The infected cattle originated from 194 municipalities in 2002, increasing to 747 in 2020. We consider that the changes in land use and intense cattle transportation may have caused the expansion of F. hepatica. The SIR model analyzed the spread of the disease looking at all six biomes: Caatinga, Amazon Forest, Cerrado, Pantanal, Atlantic Forest, and Pampa. Moreover, this infection not only threatens the health of animals but is also a major concern to biodiversity and vulnerable human communities in South America. Emblematic biomes such as the Amazon basin already face challenges with logging, desertification, and loss of biodiversity. Therefore, strategies for mitigating infection should include controlling illegal pasture areas, establishing health inspections of animal transport, quarantine of newly arrived animals, and livestock zoning, as well as clear One Health policies.

A foot and mouth disease ranking of risk using cattle transportation

Foot-and-mouth disease (FMD) is a highly infectious condition that affects domestic and wild cloven-hoofed animals. This disease has substantial economic consequences. Livestock movement is one of the primary causes of disease dissemination. The centrality properties of the livestock mobilization transportation network provide valuable information for surveillance and control of FMD. However, the same transportation network can be described by different centrality descriptions, making it challenging to prioritize the most vulnerable nodes in the transportation network. This work considers the construction of a single network risk ranking, which helps prioritize disease control measurements. Results show that the proposed ranking constructed on 2016 livestock mobilization data may predict an actual outbreak reported in the Cesar (Colombia) region in 2018, with a performance measured by the area under the receiver operating characteristic curve of 0.91. This result constitutes the first quantitative evidence of the predictive capacity of livestock transportation to target FMD outbreaks. This approach may help decision-makers devise strategies to control and prevent FMD.

Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey

The structure of contact networks affects the likelihood of disease spread at the population scale and the risk of infection at any given node. Though this has been well characterized for both theoretical and empirical networks for the spread of epidemics on completely susceptible networks, the long-term impact of network structure on risk of infection with an endemic pathogen, where nodes can be infected more than once, has been less well characterized. Here, we analyze detailed records of the transportation of cattle among farms in Turkey to characterize the global and local attributes of the directed—weighted shipments network between 2007-2012. We then study the correlations between network properties and the likelihood of infection with, or exposure to, foot-and-mouth disease (FMD) over the same time period using recorded outbreaks. The shipments network shows a complex combination of features (local and global) that have not been previously reported in other networks of shipments; i.e. small-worldness, scale-freeness, modular structure, among others. We find that nodes that were either infected or at high risk of infection with FMD (within one link from an infected farm) had disproportionately higher degree, were more central (eigenvector centrality and coreness), and were more likely to be net recipients of shipments compared to those that were always more than 2 links away from an infected farm. High in-degree (i.e. many shipments received) was the best univariate predictor of infection. Low in-coreness (i.e. peripheral nodes) was the best univariate predictor of nodes always more than 2 links away from an infected farm. These results are robust across the three different serotypes of FMD observed in Turkey and during periods of low-endemic prevalence and high-prevalence outbreaks.

Contact network epidemiology has been extensively used in the context of infectious diseases, primarily focusing on epidemic diseases. In this paper we use detailed recorded data about cattle exchange between farms in Turkey from 2007 to 2012, to build, analyze and characterize the directed-weighted complex network of shipments of cattle. Additionally, using outbreaks data about recorded cases of foot-and-mouth disease (FMD) in Turkey, we assess the correlation between the “farm’s” position in the network (importance) and the risk of being infected with FMD, which has been endemic in Turkey for a long time. We find some network measures that are more likely to identify high-risk and low-risk farms (in-degree and in-coreness, respectively) when proposing strategies for surveillance or containment of an infectious disease.