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谷歌搜索趋势预测疾病爆发：来自印度的分析

Google Search Trends Predicting Disease Outbreaks: An Analysis from India.

作者信息

Verma Madhur, Kishore Kamal, Kumar Mukesh, Sondh Aparajita Ravi, Aggarwal Gaurav, Kathirvel Soundappan

机构信息

Department of Community Medicine, Kalpana Chawla Government Medical College and Hospital, Karnal, India.

Department of Biostatistics, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India.

出版信息

Healthc Inform Res. 2018 Oct;24(4):300-308. doi: 10.4258/hir.2018.24.4.300. Epub 2018 Oct 31.

DOI:10.4258/hir.2018.24.4.300
PMID:30443418
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6230529/
Abstract

OBJECTIVES

Prompt detection is a cornerstone in the control and prevention of infectious diseases. The Integrated Disease Surveillance Project of India identifies outbreaks, but it does not exactly predict outbreaks. This study was conducted to assess temporal correlation between Google Trends and Integrated Disease Surveillance Programme (IDSP) data and to determine the feasibility of using Google Trends for the prediction of outbreaks or epidemics.

METHODS

The Google search queries related to malaria, dengue fever, chikungunya, and enteric fever for Chandigarh union territory and Haryana state of India in 2016 were extracted and compared with presumptive form data of the IDSP. Spearman correlation and scatter plots were used to depict the statistical relationship between the two datasets. Time trend plots were constructed to assess the correlation between Google search trends and disease notification under the IDSP.

RESULTS

Temporal correlation was observed between the IDSP reporting and Google search trends. Time series analysis of the Google Trends showed strong correlation with the IDSP data with a lag of -2 to -3 weeks for chikungunya and dengue fever in Chandigarh ( > 0.80) and Haryana ( > 0.70). Malaria and enteric fever showed a lag period of -2 to -3 weeks with moderate correlation.

CONCLUSIONS

Similar results were obtained when applying the results of previous studies to specific diseases, and it is considered that many other diseases should be studied at the national and sub-national levels.

摘要

目的

及时发现是传染病防控的基石。印度综合疾病监测项目可识别疫情，但无法准确预测疫情。本研究旨在评估谷歌趋势与综合疾病监测计划（IDSP）数据之间的时间相关性，并确定使用谷歌趋势预测疫情或流行病的可行性。

方法

提取2016年印度昌迪加尔联合属地和哈里亚纳邦与疟疾、登革热、基孔肯雅热和伤寒相关的谷歌搜索查询，并与IDSP的推定表格数据进行比较。使用斯皮尔曼相关性和散点图来描述两个数据集之间的统计关系。构建时间趋势图以评估谷歌搜索趋势与IDSP下疾病通报之间的相关性。

结果

在IDSP报告与谷歌搜索趋势之间观察到时间相关性。谷歌趋势的时间序列分析显示，昌迪加尔（>0.80）和哈里亚纳邦（>0.70）的基孔肯雅热和登革热与IDSP数据具有很强的相关性，滞后2至3周。疟疾和伤寒的滞后时间为2至3周，相关性中等。

结论

将先前研究的结果应用于特定疾病时获得了类似结果，并且认为应在国家和次国家层面研究许多其他疾病。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab8d/6230529/37d6f92a5177/hir-24-300-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab8d/6230529/43650534ba39/hir-24-300-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab8d/6230529/871abcac265b/hir-24-300-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab8d/6230529/37d6f92a5177/hir-24-300-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab8d/6230529/43650534ba39/hir-24-300-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab8d/6230529/871abcac265b/hir-24-300-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab8d/6230529/37d6f92a5177/hir-24-300-g003.jpg

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