Spatiotemporal analysis of reported classical swine fever outbreaks in China (2005-2018) and the influence of weather

Spatial Epidemiology and Its Role in Prevention and Control of Swine Viral Disease

Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics.

Embedding research and enquiry in Australian DVM curriculum

Research and enquiry (R&E) is an integral part of veterinary training. It is a foundation of evidence-based practice. In the University of Sydney Doctor of Veterinary Medicine degree R&E culminates in a cap-stone experience in Year 3: a 'professionally focused project', a student-driven and academic supported individual research project. The project provides an authentic experience within a veterinary discipline. Students work with an academic advisor who provides guidance for developing and achieving meaningful educational and professional goals. Successful advising depends upon a shared understanding of, and commitment to, the advising process by students, advisors and the university. The R&E mission can be broadly defined as - veterinarians recognise that evidence-based approach to practice, which is based on the scientific method, leads to the generation of new knowledge that underpins the veterinary medical profession.

Application of machine learning models for risk estimation and risk prediction of classical swine fever in Assam, India

The present study is aimed to develop an early warning system of Classical swine fever (CSF) disease by applying machine learning models and to study the climate-disease relationship with respect to the spatial occurrence and outbreaks of the disease in the north-eastern state of Assam, India. The disease incidence data from the year 2005 to 2021 was used. The linear discriminant analysis (LDA) revealed that significant environmental and remote sensing risk factors like air temperature, enhanced vegetation index, land surface temperature, potential evaporation rate and wind speed were significantly contributing to CSF incidences in Assam. Furthermore, the climate-based disease modelling was applied to relevant ecological and environmental risk factors determined using LDA and risk maps were generated. The western and eastern regions of the state were predicted to be at high risk of CSF with presence of significant hotspots. For the districts that are significantly clustered, the Basic reproduction number (R0) was calculated after the predicted results were superimposed onto the risk maps. The R0 value ranged from 1.04 to 2.07, implying that the eastern and western regions of Assam are more susceptible to CSF. Machine learning models were implemented using R statistical software version 3.1.3. The random forest, classification tree analysis and gradient boosting machine were found to be the best-fitted models for the study group. The models' performance was measured using the Receiving Operating Characteristic (ROC) curve, Cohen's Kappa, True Skill Statistics, Area Under ROC Curve, ACCURACY, ERROR RATE, F1 SCORE, and Logistic Loss. As a part of the suggested study, these models will help us to understand the disease transmission dynamics, risk factors and spatio-temporal pattern of spread and evaluate the efficacy of control measures to battle the economic losses caused by CSF outbreaks.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-023-00847-6.

Airborne transmission of common swine viruses

The transmission of viral aerosols poses a vulnerable aspect in the biosecurity measures aimed at preventing and controlling swine virus in pig production. Consequently, comprehending and mitigating the spread of aerosols holds paramount significance for the overall well-being of pig populations. This paper offers a comprehensive review of transmission characteristics, influential factors and preventive strategies of common swine viral aerosols. Firstly, certain viruses such as foot-and-mouth disease virus (FMDV), porcine reproductive and respiratory syndrome virus (PRRSV), influenza A viruses (IAV), porcine epidemic diarrhea virus (PEDV) and pseudorabies virus (PRV) have the potential to be transmitted over long distances (exceeding 150 m) through aerosols, thereby posing a substantial risk primarily to inter-farm transmission. Additionally, other viruses like classical swine fever virus (CSFV) and African swine fever virus (ASFV) can be transmitted over short distances (ranging from 0 to 150 m) through aerosols, posing a threat primarily to intra-farm transmission. Secondly, various significant factors, including aerosol particle sizes, viral strains, the host sensitivity to viruses, weather conditions, geographical conditions, as well as environmental conditions, exert a considerable influence on the transmission of viral aerosols. Researches on these factors serve as a foundation for the development of strategies to combat viral aerosol transmission in pig farms. Finally, we propose several preventive and control strategies that can be implemented in pig farms, primarily encompassing the implementation of early warning models, viral aerosol detection, and air pretreatment. This comprehensive review aims to provide a valuable reference for the formulation of efficient measures targeted at mitigating the transmission of viral aerosols among swine populations.