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Google Search Trends Predicting Disease Outbreaks: An Analysis from India
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society of Medical Informatics
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6230529/ https://www.ncbi.nlm.nih.gov/pubmed/30443418 http://dx.doi.org/10.4258/hir.2018.24.4.300 |
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author | Verma, Madhur Kishore, Kamal Kumar, Mukesh Sondh, Aparajita Ravi Aggarwal, Gaurav Kathirvel, Soundappan |
author_facet | Verma, Madhur Kishore, Kamal Kumar, Mukesh Sondh, Aparajita Ravi Aggarwal, Gaurav Kathirvel, Soundappan |
author_sort | Verma, Madhur |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6230529 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Korean Society of Medical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-62305292018-11-15 Google Search Trends Predicting Disease Outbreaks: An Analysis from India Verma, Madhur Kishore, Kamal Kumar, Mukesh Sondh, Aparajita Ravi Aggarwal, Gaurav Kathirvel, Soundappan Healthc Inform Res Original Article 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. Korean Society of Medical Informatics 2018-10 2018-10-31 /pmc/articles/PMC6230529/ /pubmed/30443418 http://dx.doi.org/10.4258/hir.2018.24.4.300 Text en © 2018 The Korean Society of Medical Informatics http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Verma, Madhur Kishore, Kamal Kumar, Mukesh Sondh, Aparajita Ravi Aggarwal, Gaurav Kathirvel, Soundappan Google Search Trends Predicting Disease Outbreaks: An Analysis from India |
title | Google Search Trends Predicting Disease Outbreaks: An Analysis from India |
title_full | Google Search Trends Predicting Disease Outbreaks: An Analysis from India |
title_fullStr | Google Search Trends Predicting Disease Outbreaks: An Analysis from India |
title_full_unstemmed | Google Search Trends Predicting Disease Outbreaks: An Analysis from India |
title_short | Google Search Trends Predicting Disease Outbreaks: An Analysis from India |
title_sort | google search trends predicting disease outbreaks: an analysis from india |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6230529/ https://www.ncbi.nlm.nih.gov/pubmed/30443418 http://dx.doi.org/10.4258/hir.2018.24.4.300 |
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