我国猪丹毒疫情的时空特征及其影响因素

doi:10.11843/j.issn.0366-6964.2023.06.031

摘要/Abstract

摘要： 猪丹毒是危害养殖业的一种由丹毒丝菌引起的传染病,了解我国猪丹毒的时空分布格局以及影响因素可以为今后猪丹毒的早期预警和防控措施提供参考。以我国31个省级行政区为研究区域,结合H-P滤波法、空间自相关分析和时空扫描统计对我国2010-2020年各省猪丹毒发病数的时空分布特征进行探讨,同时采用多尺度地理加权回归模型(multiscale geographically weighted regression, MGWR)、地理加权回归模型(geographically weighted regression, GWR)以及经典的最小二乘法(ordinary least squares, OLS)模型探究社会和气候因素在空间层面上对猪丹毒发病数的影响程度。结果表明:时间上,疫情呈现先增后减的趋势,病例数的季节指数在6—9月大于120%,在1—4月小于90%。空间上,每年的发病数存在明显的空间正相关性(Moran’s I值为0.127~0.295)。时空扫描在2010—2014年和 2015—2020年探测出的一级聚集区分别为5和4个,聚集时间为6—8月,且聚集中心有向南移动的趋势。对比多个模型结果显示,MGWR模型拟合效果最佳(R²为0.43~0.84),风速显著影响猪丹毒病例的时期覆盖了整个研究期,气温、公路密度、生猪数量以及乡村人口比例均能在研究期内的部分年份显著地影响猪丹毒病例,并且不同地区的影响因素存在不同的波动方向和强度。研究结果表明,疫情分布在时空上存在明显的聚集性,疫情主要暴发于我国东南地区的夏季,风速、气温、公路密度、生猪数量以及乡村人口比例均会对区域猪丹毒的病例数有一定影响。

关键词: 猪丹毒, 时空特征, 时空扫描, 影响因素, 多尺度地理加权回归模型

Abstract: Swine erysipelas is an infectious disease caused by Erysipelothrix rhusiopathiae which endangers breeding industry. Understanding the spatial and temporal distribution pattern and influencing factors of swine erysipelas in China can provide reference for early warning and prevention and control measures of swine erysipelas in the future. In this study, 31 provincial administrative regions of China were selected as the study area, and the spatial and temporal distribution characteristics of the incidence of erysipelas in China from 2010 to 2020 were analyzed by combining H-P filtering, spatial autocorrelation analysis and spatio-temporal scanning statistics. Meanwhile, MGWR, GWR and OLS model were used to explore the spatial impact of social and climatic factors on the incidence of erysipelas in China. The results show that: In terms of time, the epidemic showed a trend of increasing first and then decreasing, the seasonal index of case numbers was greater than 120% in June-September and less than 90% in January-April. Spatially, there was a significantly spatial positive correlation in the number of cases per year (Moran's I value ranged from 0.127 to 0.295). The first-level agglomerations detected by spatio-temporal scanning during 2010-2014 and 2015-2020 were 5 and 4, respectively. The agglomerations occurred from June to August, and the agglomerations tended to move southward. The results of comparing multiple models show that the MGWR model has the best fitting effect (R² ranged from 0.43 to 0.84). Wind speed, temperature, road density, number of live pigs, and the proportion of rural population can significantly affect swine erysipelas cases to a certain extent, and the influencing factors in different regions have different fluctuation directions and intensities. Our results indicated that the epidemic distribution has obvious aggregation in time and space. The outbreak mainly occurred in the southeastern part of China. Wind speed and rural population ratio are the main factors affecting swine erysipelas cases.

Key words: swine erysipelas, spatio-temporal characteristics, spatio-temporal scanning, influencing factors, multiscale geographically weighted regression model

中图分类号:

S851.31

王静宇, 樊姝琪, 黎成, 尹宁, 庄彬贤, 刘慧铭, 温永仙. 我国猪丹毒疫情的时空特征及其影响因素[J]. 畜牧兽医学报, 2023, 54(6): 2528-2542.

WANG Jingyu, FAN Shuqi, LI Cheng, YIN Ning, ZHUANG Binxian, LIU Huiming, WEN Yongxian. Spatio-temporal Characteristics and Influencing Factors of Swine Erysipelas Epidemic in China[J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54(6): 2528-2542.

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