Spatio-temporal variation of African swine fever epidemic and its transmission risk in China

doi:10.3969/j.issn.1004-9479.2022.02.2021047

Abstract

Abstract:

African Swine Fever (ASF) is a strong infectious disease against pigs that does not infect humans and has a fatal impact on the pig industry. It is convenient to prevent and control ASF by studying the spatio-temporal variation characteristics of ASF and carrying out transmission risk analysis. From the perspective of geography, based on the analysis of the spatiotemporal variation and transmission direction of ASF epidemic in China, the maximum entropy model was used to study the transmission risk of ASF epidemic in China.The results showed that the prevalence of ASF in China increased at first and then decreased rapidly and tended to be stable. The largest number of ASF outbreaks occurred in southwest and northeast China, and the most concentrated region was northeast China. The direction of ASF transmission was northeast to east and south, and southwest to northwest.The main factors affecting the incidence of ASF are roads, rivers, population, breeding density and GDP of the primary industry. Based on this, the risk transmission model of ASF epidemic was constructed, and it was predicted that the high risk areas were mainly concentrated in the northeast, southwest and southeast regions.On the basis of the above analysis, the measures for ASF epidemic prevention and control were put forward.Key words: African swine fever; spatio-temporal variation; transmission risk

摘要：

非洲猪瘟（African swine fever，ASF）是一种不感染人的针对猪的强传染病，对养猪业有着致命打击，研究ASF的时空变化特征及其传播风险分析，便于ASF的预防和控制。从地理学的视角，在对我国ASF疫情时空变化及传播方向分析的基础上，利用最大熵模型对ASF疫情传播风险进行研究。结果表明：我国ASF疫情呈现先增长后快速减少并趋于平稳的趋势，暴发疫情最多的是西南、东北地区，最为集聚的区域为东北地区，传播方向呈现东北方向向东部、南部方向，西南方向向西北方向传播的特征；影响ASF发病的主要因素有公路、河流、人口、养殖密度和第一产业GDP，据此构建ASF疫情风险传播模型，预测出高风险区主要聚集在东北、西南和东南地区。在上述分析基础上，提出了ASF疫情防控的措施建议。

关键词: 非洲猪瘟, 时空变化, 传播风险

Hai LIU, Zeyu AN, Yuefei HUANG, Peng JIA, Yunhai ZHU. Spatio-temporal variation of African swine fever epidemic and its transmission risk in China[J]. World Regional Studies, 2022, 31(2): 410-417.

刘海, 安泽禹, 黄跃飞, 贾鹏, 朱运海. 我国非洲猪瘟疫情时空变化及其传播风险[J]. 世界地理研究, 2022, 31(2): 410-417.

Figures/Tables 7

Fig.1 Line chart of ASF epidemic statistics in China every month

Fig.2 ASF epidemic classification map of Chinese provinces

Fig.3 Cluster analysis chart of ASF epidemic situation in China

Fig.4 Analysis chart of ASF epidemic transmission direction in China

Tab.1 OLS parameter estimation result

自变量	回归系数	P
河流	0.286 730	0.000 000
公路	-0.087 386	0.006 589
铁路	-0.000 054	0.988 717
人口密度	-0.000 295	0.000 010
养殖密度	0.002 899	0.000 000
第一产业GDP	-0.000 042	0.001 686

Fig.5 Risk forecast distribution of ASF epidemic in China

Fig.6 Accuracy of ASF epidemic distribution prediction in China

References 32

1	陈腾, 张守峰, 周鑫韬, 等.我国首次非洲猪瘟疫情的发现和流行分析.中国兽医学报, 2018,38(9):1831-1832.
	CHEN T, ZHANG S F, ZHOU X T, et al. Discovery and epidemic analysis of the first outbreak of African swine fever in China. Chinese Journal of Veterinary Science,2018,38(9):1831-1832.
2	DENIS K, ILYA T, SODNOM T, et al. African Swine Fever Virus, Siberia, Russia, 2017. Emerging Infectious Diseases, 2018, 24(4):796-798.
3	MONTGOMERY ER. On A Form of Swine Fever Occurring in British East Africa (Kenya Colony) Journal of Comparative Pathology & Therapeutics, 1921, 34(3):159-191.
4	GALLARD C, SOLER A, NIETO R, et al. Comparative evaluation of novel African swine fever virus (ASF) antibody detection techniques derived from specific ASF viral genotypes with the OIE internationally prescribed serological tests.Veterinary Microbiology,2013, 162(1):32-43.
5	SÁNCHEZ-VIZCAÍNO J M, Mur L, MUR L, et al. An Update on the Epidemiology and Pathology of? African Swine Fever. Journal of Comparative Pathology, 2015, 152(1):9-21.
6	GOGIN A, GERASIMOV V, MALOGOLOVKIN A, et al. African swine fever in the North Caucasus region and the Russian Federation in years 2007–2012.Virus Research,2013, 173(1):198-203.
7	The United States Animal Health Association. African swine fever//Foreign Animal Diseases. 7th Edition. boca raton,florida: Boca Publication Group, Inc, 2008: 111-116.
8	FERNANDEZ P J, WHITE W R. Atlas of Transboundary Animal Diseases. Paris French: World Organization for Animal Health, Inc,2010: 19-29.
9	ALKHAMIS M A, CARMINA G, CRISTINA J, et al. Phylodynamics and evolutionary epidemiology of African swine fever p72-CVR genes in Eurasia and Africa. Plos One, 2018, 13(2):1-18.
10	GALLARDO C, NIETO R, SOLER A, et al. Assessment of African Swine Fever Diagnostic Techniques as a Response to the Epidemic Outbreaks in Eastern European Union Countries: How To Improve Surveillance and Control Programs. Journal of Clinical Microbiology, 2015, 53(8):2555-2565.
11	秦川, 张连峰. SARS揭示新发传染病对人类健康的威胁. 中国实验动物学报, 2005, 13(3):129-131.
	QIN C, ZHANG L F. SARS revealed the threat of emerging infectious diseases to human health. Acta Laboratorium Animalis Scientia Sinica, 2005, 13(3):129-131.
12	ROWORTH M. Geographical and Environmental Epidemiology: Methods for Small-Area Studies. Journal of Epidemiology & Community Health, 1993, 47(5):382-178.
13	武继磊, 王劲峰, 郑晓瑛,等.空间数据分析技术在公共卫生领域的应用.地理科学进展,2003,22(3):219-228.
	WU J, WANG J, ZHENG X, et al. A review on application of spatial data analysis technology in public health. Progress In Geography, 2003, 22(3):219-228.
14	FRANK C, FIX A D, CÉSAR A. PEÑA,et al. Mapping lyme disease incidence for diagnostic and preventive decisions, Maryland. Emerging Infectious Diseases, 2002, 8(4):427-429.
15	张发,李璐,宣慧玉.传染病传播模型综述.系统工程理论与实践,2011,31(9):1736-1744.
	ZHANG F, LI L, XUAN H.Survey of transmission models of infectious diseases. Systems Engineering-Theory & Practice,2011,31(9):1736-1744.
16	ELITH J, GRAHAM C H, ANDERSON R P, et al. Novel methods improve prediction of species' distributions from occurrence data. Ecography, 2006, 29(2):129-151.
17	麻亚鸿,李丹丹,于晶,等.中国蓑藓属与木灵藓属分布式样与气候因子的关系.生物多样性,2013,21(2):177-191.
	MA Y, LI D, YU J, et al. Geographical distribution patterns of Macromitrium and Orthotrichum in China and their relationship with climatic factors. Biodiversity Science,2013,21(2): 177-191.
18	DIONE M, OUMA E, OPIO F, et al. Qualitative analysis of the risks and practices associated with the spread of African swine fever within the smallholder pig value chains in Uganda. Preventive Veterinary Medicine, 2016, 135:102-112.
19	NANTIMA N, OCAIDO M, OUMA E,et al.Risk factors associated with occurrence of African swine fever outbreaks in smallholder pig farms in four districts along the Uganda-Kenya border.Tropical Animal Health and Production,2015,47(3):589-595.
20	杨公立,张志诚,赵继军,等.非洲猪瘟流行病学及其风险因素分析.猪业科学,2014,31(5):24-27.
	YANG G, ZHANG Z, ZHAO J, et al. Epidemiology and risk factors of African swine fever. Swine Industry Science,2014,31(5):24-27.
21	ZHOU X, LI N, LUO Y, et al.Emergence of African Swine Fever in China, 2018.Transboundary & Emerging Diseases, 2018,65(6):1482-1484.
22	LIANG R, LU Y, QU X, et al. Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data. Transbound Emerg Dis. 2020,67(2):935-946.
23	卢易,王烁,易敬涵,等.基于GIS的中国非洲猪瘟疫情风险分析.中国兽医学报,2019,39(1):8-13+20.
	LU Y, WANG S, YI J H, et al. Risk analysis of African swine fever in China based on GIS. Chinese Journal of Veterinary Science,2019,39(1):8-13+20.
24	宋洁,温璐,王凤歌,等.乌兰布和沙漠生态系统服务价值时空动态研究.生态学报,2021,41(6):1-11.
	SONG J, WEN L, WANG F G,et al.Spatiotemporal dynamics of ecosystem service value in Ulan Buh Desert.ACTA ECOLOGICA SINICA,2021,41(6):1-11
25	SCOTT L M, JANIKAS M V. Handbook of Applied Spatial Analysis.Springer Berlin Heidelberg, 2010.
26	GUI J, LIU T, SUN Z,et al.Fast supervised discrete hashing.IEEE Trans Pattern Anal Mach Intell,2018,40 (2):490-496.
27	AHMAD I, DAR M A, FENTA A, et al. Spatial configuration of groundwater potential zones using OLS regression method. Journal of African Earth Sciences, 2021, 177(4):1-9.
28	JAYNES E T. Information Theory and Statistical Mechanics.Physical Review,1957, 106(4):620-630.
29	RIVAS A L, SMITH S D, SULLIVAN P J, et al. Identification of geographic factors associated with early spread of foot-and-mouth disease.American Journal of Veterinary Research, 2003, 64(12):1519-1527.
30	GULENKIN V M, KORENNOY F I, KARAULOV A K, et al. Cartographical analysis of African swine fever outbreaks in the territory of the Russian Federation and computer modeling of the basic reproduction ratio.Preventive Veterinary Medicine,2011, 102(3):167-174.
31	KORENNOYA F I, GULENKINA J M, MALONEB C B . et al. Spatio-temporal modeling of the African swine fever epidemic in the Russian Federation, 2007-2012.Spatial and Spatio-temporal Epidemiology, 2014,11(10): 135-141.
32	高文强, 王小菲, 江泽平, 等.气候变化下栓皮栎潜在地理分布格局及其主导气候因子. 生态学报, 2016, 36(14):4475-4484.
	GAO W, WANG X, JIANG Z, et al. Impact of climate change on the potential geographical distribution pattern and dominant climatic factors of Quercus variabilis. Acta Ecologica Sinica, 2016, 36(14):4475-4484.