Abstract:To improve accuracy in predicting the epidemic trends of infectious diseases by using network big data from a search engine, we conducted a retrospective epidemiological investigation of the monthly and weekly new H7N9 subtype avian influenza cases in Guangdong Province from 2013 to 2018. We established a support vector machine (SVM) prediction model and multiple linear regression prediction model to perform fitting degree analysis based on the Baidu index of the keywords "H7N9" and the clinical symptoms of H7N9 subtype avian influenza. We found that the number of new cases and the trend in the Baidu index of the keyword "H7N9" could be divided into four groups. The predicted values of the second and third wave epidemics described the trends in the actual number of cases. The keywords of clinical symptoms were positively correlated with the actual number of cases. The predicted value of the fourth wave epidemic was closer to the actual incidence and had higher prediction accuracy than SVM regression. The results showed that the wave characteristics of public search behavior and epidemics changed the public search frequency for keywords for the clinical symptoms of infectious diseases, thus greatly improving the ability of network big data from a search engine to predict the H7N9 subtype avian influenza epidemic trend.
黄泽颖. 基于百度指数的传染病预测精准性探索——以广东省H7N9亚型禽流感为例[J]. 中国人兽共患病学报, 2020, 36(11): 962-968.
HUANG Ze-ying. Exploration of the Accuracy of epidemic prediction based on the Baidu index——taking H7N9 subtype avian influenza in Guangdong Province as an Example. Chinese Journal of Zoonoses, 2020, 36(11): 962-968.
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