Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index
Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index
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DOI：
10.3390/ijerph17072365
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发表时间：
2020-04-01
影响因子：
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通讯作者：
Wu, Szu-Yuan
中科院分区：
文献类型：
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作者：
Qin, Lei;Sun, Qiang;Wu, Szu-Yuan
预测2019年新型冠状病毒病（COVID-19）新发疑似或确诊病例数，对于预防和控制COVID-19疫情至关重要。收集2019年12月31日至2020年2月9日期间干咳、发烧、胸闷、冠状病毒和肺炎的社交媒体搜索索引（SMSI）。新发COVID-19疑似病例数据于2020年1月20日至2020年2月9日收集。我们使用滞后的SMSI序列来预测这一时期新的疑似COVID-19病例数。为了避免过拟合，采用了子集选择、正向选择、套索回归、脊回归和弹性网五种方法来估计系数。我们选择了预测2020年1月20日至2020年2月9日新发疑似病例数的最佳方法。我们进一步验证了2019年12月31日至2020年2月17日新确诊病例的最佳方法。新增疑似病例数与滞后的SMSI序列显著相关。SMSI可比新发疑似病例早6-9天被发现。最优的方法是子集选择法，该方法估计误差最小，预测因子数量适中。子集选择方法与新发确诊病例也有显著相关性。延迟第10天的SMSI结果与新确诊病例显著相关。SMSI可能是COVID-19感染数量的重要预测指标。SMSI可能是一个有效的早期预测器，它将使政府卫生部门能够找到潜在的和高风险的爆发地区。
Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6-9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments' health departments to locate potential and high-risk outbreak areas.