| 摘要: |
| 对我国发生非洲猪瘟期间（2018年5月—2019年9月）的百度指数与该种疾病爆发的关联性进行研究，采用以省份为区域分组的二元Logistic回归模型，通过对17个地区数据的拟合，分别提前3周、提前2周、提前1周及当周预测了非洲猪瘟早期爆发的区域.研究结果表明：预测的准确率均高于91.2%，可作为对传统监测系统的有力补充. |
| 关键词: 大数据 百度指数 预测 非洲猪瘟 |
| DOI：10.3969/J.ISSN.1000-5137.2022.02.014 |
| 分类号:TP274 |
| 基金项目: |
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| Research on prediction of the breakout of African Swine Fever based on big data technology |
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GAO Xianglan1,2, ZHOU Nan2, CAI Xiang3, MU Shanghai2
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1.School of Information, Shanghai Lida University, Shanghai 201609, China;2.School of Management, Shinawatra University, Pathum Thani 12160, Thailand;3.School of Business, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China
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| Abstract: |
| The relevance between Baidu Index during African Swine Fever occurred from May 2018 to September 2019 in China and the breakout of the disease was studied in this paper. By means of fitting the data of 17 districts, the area where African Swine Fever would outbreak was successfully predicted by the binary Logistic regression model 3, 2, 1 weeks in advance and in that week respectively. The results showed that the accuracy of the prediction was over 91.2% without exception, which could be a valuable supplement to the traditional monitoring system. |
| Key words: big data Baidu index prediction African Swine Fever |
