Assessment of foot-and-mouth disease risk areas in mainland China based spatial multi-criteria decision analysis

Ecological niche modeling for surveillance of foot-and-mouth disease in South Asia

Control of transboundary diseases at a regional level is commended over the country level due to its inherent complexities. World Organization for Animal Health (WOAH) has established different zones worldwide to control such contagious diseases as foot-and-mouth disease (FMD). Controlling FMD is difficult because of the complicated connection between FMD risk factors, and the deficits of surveillance activities in countries. We used an ecological niche model (ENM) that accounts for the under-reporting of outbreaks to determine FMD risk and risk factors in South Asian countries India, Bangladesh, and Sri Lanka. Centered on known outbreak information, we predicted high-risk areas using similar regional ecological features. Using a multi-algorithm machine-learning ensemble that includes random forest, support vector, and gradient boosting, 15 predictive variables (i.e., livestock densities, land cover, and climate), 660 FMD outbreaks from 13 years (2009-2022) in the region including the outbreaks from India, Bangladesh, and Sri Lanka we identified that Sri Lanka and Bangladesh appeared to have low to medium outbreak risk in the range of 0.04 to 0.55. India was used to fit the model. The machine learning models demonstrated high predictive performance (accuracy >0.87) through cross-validation. Production systems, isothermality, cattle density (per Km2), and mean diurnal range was identified as the most important predictors of FMD outbreaks. These models help to determine FMD low-risk areas to minimize FMD surveillance activities and high-risk areas to focus on performing additional confirmatory testing, and improve surveillance in a regional context.

Development and application of a WebGIS-based prediction system for multi-criteria decision analysis of porcine pasteurellosis

Porcine pasteurellosis is an infectious disease caused by Pasteurella multocida (P. multocida), which seriously endangers the healthy development of pig breeding industry. Early detection of disease transmission in animals is a crucial early warning for humans. Therefore, predicting risk areas for disease is essential for public health authorities to adopt preventive measures and control strategies against diseases. In this study, we developed a predictive model based on multi-criteria decision analysis (MCDA) and assessed risk areas for porcine pasteurellosis in the Chinese mainland. By using principal component analysis, the weights of seven spatial risk factors were determined. Fuzzy membership function was used to standardize all risk factors, and weight linear combination was used to create a risk map. The sensitivity of the risk map was analyzed by calculating the mean of absolute change rates of risk factors, as well as calculating an uncertainty map. The results showed that risk areas for porcine pasteurellosis were predicted to be locate in the south-central of the Chinese mainland, including Sichuan, Chongqing, Guangdong, and Guangxi. The maximum standard deviation of the uncertain map was less than 0.01and the ROC results showed that the prediction model has moderate predictive performance with the area under the curve (AUC) value of 0.80 (95% CI 0.75-0.84). Based on the above process, MCDA was combined with WebGIS technology to construct a system for predicting risk areas of porcine pasteurellosis. Risk factor data was directly linked to the developed model, providing decision support for disease prevention and control through monthly updates.

Spatio-temporal analysis and risk modeling of foot-and-mouth disease outbreaks in China

FMD is an acute contagious disease that poses a significant threat to the health and safety of cloven-hoofed animals in Asia, Europe, and Africa. The impact of FMD exhibits geographical disparities within different regions of China. The present investigation undertook an exhaustive analysis of documented occurrences of bovine FMD in China, spanning the temporal range from 2011 to 2020. The overarching objective was to elucidate the temporal and spatial dynamics underpinning these outbreaks. Acknowledging the pivotal role of global factors in FMD outbreaks, advanced machine learning techniques were harnessed to formulate an optimal prediction model by integrating comprehensive meteorological data pertinent to global FMD. Random Forest algorithm was employed with top three contributing factors including Isothermality(bio3), Annual average temperature(bio1) and Minimum temperature in the coldest month(bio6), all relevant to temperature. By encompassing both local and global factors, our study provides a comprehensive framework for understanding and predicting FMD outbreaks. Furthermore, we conducted a phylogenetic analysis to trace the origin of Foot-and-mouth disease virus (FMDV), pinpointing India as the country posing the greatest potential hazard by leveraging the spatio-temporal attributes of the collected data. Based on this finding, a quantitative risk model was developed for the legal importation of live cattle from India to China. The model estimated an average probability of 0.002254% for FMDV-infected cattle imported from India to China. TA sensitivity analysis identified two critical nodes within the model: he possibility of false negative clinical examination in infected cattle at destination (P5) and he possibility of false negative clinical examination in infected cattle at source(P3). This comprehensive approach offers a thorough evaluation of FMD landscape within China, considering both domestic and global perspectives, thereby augmenting the efficacy of early warning mechanisms.

A review of foot-and-mouth disease in Ethiopia: epidemiological aspects, economic implications, and control strategies

Foot-and-mouth disease (FMD) is a contagious viral disease that affects the livelihoods and productivity of livestock farmers in endemic regions. It can infect various domestic and wild animals with cloven hooves and is caused by a virus belonging to the genus Aphthovirus and family Picornaviridae, which has seven different serotypes: A, O, C, SAT1, SAT2, SAT3, and Asia-1. This paper aims to provide a comprehensive overview of the molecular epidemiology, economic impact, diagnosis, and control measures of FMD in Ethiopia in comparison with the global situation. The genetic and antigenic diversity of FMD viruses requires a thorough understanding for developing and applying effective control strategies in endemic areas. FMD has direct and indirect economic consequences on animal production. In Ethiopia, FMD outbreaks have led to millions of USD losses due to the restriction or rejection of livestock products in the international market. Therefore, in endemic areas, disease control depends on vaccinations to prevent animals from developing clinical disease. However, in Ethiopia, due to the presence of diverse antigenic serotypes of FMD viruses, regular and extensive molecular investigation of new field isolates is necessary to perform vaccine-matching studies to evaluate the protective potential of the vaccine strain in the country.

Characterization of T-cell subsets in response to foot-and-mouth disease bivalent inactivated vaccine in Chinese Holstein cows

IMPORTANCE

Vaccination plays a crucial role in the prevention and control of FMD; however, outbreaks persist occurring worldwide. Assessing the immune response to FMD vaccines is essential for effective prevention of FMD. In this study, a seven-color flow cytometry protocol was developed to systematically evaluate the T-cell response of Chinese Holstein cows vaccinated with FMD bivalent inactivated vaccine. Our findings showed that while most T-cell subsets (%) decreased post-vaccination, a significant increase was observed in CD4+CD8+ DP T cells, which was consistent with the levels of specific foot-and-mouth disease virus (FMDV) antibodies. These findings suggested that CD4+CD8+ DP T cells could serve as a potential biomarker for the evaluation of cellular and humoral responses to FMDV vaccination. Additionally, we should be aware of the potential decline in cellular immunity among cattle during FMD vaccination, as this may increase the risk of other pathogen-related issues.