Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study
Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study
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DOI：
10.2196/18828
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发表时间：
2020-04-01
影响因子：
8.5
通讯作者：
Kalhori, Sharareh R. Niakan
中科院分区：
文献类型：
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作者：
Ayyoubzadeh, Seyed Mohammad;Ayyoubzadeh, Seyed Mehdi;Kalhori, Sharareh R. Niakan
背景：最近全球爆发的冠状病毒病（COVID-19）正在影响世界许多国家。伊朗是受影响最严重的10个国家之一。搜索引擎提供有用的人口数据，这些数据可能对分析流行病有用。利用电子资源数据的数据挖掘方法可以更好地了解COVID-19疫情，从而管理各国和全球的卫生危机。目的：预测新冠肺炎在伊朗的发病率。方法：数据来源于谷歌Trends网站。采用线性回归和长短期记忆（LSTM）模型估计COVID-19阳性病例数。所有模型均采用10倍交叉验证进行评估，并使用均方根误差（RMSE）作为性能指标。结果：线性回归模型预测发病率的RMSE为7.562 （SD为6.492）。除前一天发生率外，最有效的因素包括洗手、洗手液和消毒剂的搜索频率。LSTM模型的RMSE为27.187 （SD为20.705）。结论：数据挖掘算法可用于预测疫情趋势。这一预测可为决策者和卫生保健管理者提供相应的卫生保健资源规划和分配。
Background: The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations, and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources'data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide.Objective: This study aimed to predict the incidence of COVID-19 in Iran.Methods: Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation, and root mean square error (RMSE) was used as the performance metric.Results: The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing, hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705).Conclusions: Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly.