Google Search Trends Predicting Disease Outbreaks: An Analysis from India
2
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
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 (r > 0.80) and Haryana (r > 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.
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 (r > 0.80) and Haryana (r > 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.
참고문헌 (22)
- "Integrated Disease Surveillance Programme" (2018)[URL]
- Milinovich GJ, "Internet-based surveillance systems for monitoring emerging infectious diseases", Lancet Infect Dis, 14(2), pp.160~168 (2014)
- Hoffman DL, "Has the Internet become indispensable?", Commun ACM, 47(7), pp.37~42 (2004)
- "The Indian Telecom Services Performance Indicators" (2017)[URL]
- Hellawell GO, "Urology and the Internet : an evaluation of internet use by urology patients and of information available on urological topics", BJU Int, 86(3), pp.191~194 (2000)
- Tonsaker T, "Health information on the Internet: goldmine or minefield?", Can Fam Physician, 60(5), pp.407~408 (2014)
- Hesse BW, "Surveys of physicians and electronic health information", N Engl J Med, 362(9), pp.859~860 (2010)
- Diaz JA, "Patients' use of the Internet for medical information", J Gen Intern Med, 17(3), pp.180~185 (2002)
- Moretti FA, "Access to health information on the Internet: a public health issue?", Rev Assoc Med Bras, 58(6), pp.650~658 (1992)
- Choi H, "Predicting the present with Google Trends", Econ Rec, 88, pp.2~9 (2012)
- Bragazzi NL, "A Google Trends-based approach for monitoring NSSI", Psychol Res Behav Manag, 7, pp.1~8 (2013)
- Kang M, "Using Google Trends for influenza surveillance in South China", PLoS One, 8(1), pp.e55205~ (2013)
- "Search Engine Market Share India" (2017)[URL]
- Cervellin G, "Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings", J Epidemiol Glob Health, 7(3), pp.185~189 (2017)
- Longini IM Jr, "Containing pandemic influenza at the source", Science, 309(5737), pp.1083~1087 (2005)
- Ferguson NM, "Strategies for containing an emerging influenza pandemic in Southeast Asia", Nature, 437(7056), pp.209~214 (2005)
- Ginsberg J, "Detecting influenza epidemics using search engine query data", Nature, 457(7232), pp.1012~1014 (2009)
- Polgreen PM, "Using internet searches for influenza surveillance", Clin Infect Dis, 47(11), pp.1443~1448 (2008)
- Samaras L, "Syndromic surveillance models using web data : the case of scarlet fever in the UK", Inform Health Soc Care, 37(2), pp.106~124 (2012)
- Desai R, "Norovirus disease surveillance using Google Internet query share data", Clin Infect Dis, 55(8), pp.e75~e78 (2012)
- Ortiz JR, "Monitoring influenza activity in the United States: a comparison of traditional surveillance systems with Google Flu Trends", PLoS One, 6(4), pp.e18687~ (2011)
- Hulth A, "Web query-based surveillance in Sweden during the influenza A(H1N1)2009 pandemic, April 2009 to February 2010", Euro Surveill, 16(18), pp.19856~ (2011)