Abstract
Based on data from three major pig diseases, this study calculated the animal disease epidemic index of 31 provinces and autonomous regions in mainland China. We adopted the Gini coefficient to investigate the interregional differences in animal disease epidemic risk and used the Shapley value decomposition method to illustrate the contribution of influencing factors. The results showed that the Gini coefficient remains above 0.60, indicating significant interregional differences in mainland China. Animal breeding level, ecological environment, and animal disease prevention and control contribute most to the interregional differences in animal epidemic risk. The results imply that reducing sewage discharge, increasing pig production, and changing the breeding style from free-range to large-scale farming are measures that may help improve disease prevention and control. This study has implications for providing theoretical references for preventing and controlling animal epidemics and for improving public health governance.
Highlights
With the rapid development of the livestock and poultry farming industry in mainland China, the density of livestock and poultry breeding has increased greatly, and the trade and circulation of livestock and poultry products have accelerated
The results show that interregional differences in animal feeding and production contributed most to the interregional differences in animal epidemic risk from 2010 to 2014, with the highest contribution in 2011, at 43.04%, and the lowest contribution in 2014, at 39.53%
The epidemic situation in each region was estimated by applying the epidemic index formula, for which the 31 provinces, municipalities, and autonomous regions in mainland China were divided into extremely high, high, medium, and low-risk regions using the animal epidemic index
Summary
With the rapid development of the livestock and poultry farming industry in mainland China, the density of livestock and poultry breeding has increased greatly, and the trade and circulation of livestock and poultry products have accelerated. This is accompanied by the spread of animal epidemics, leading to a series of public health concerns over environmental damage, zoonotic diseases, and public panic [1]. Previous studies have investigated animal epidemic risk prevention and control from various perspectives. Sung et al investigated the determinants of porcine epidemic diarrhea virus dissemination because of spatial and temporal factors in Taiwan [5]. Damien illustrated what the effectiveness of applying big data means for animal health surveillance to improve risk management [6]
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