Correlation between Google Trends on dengue fever and national surveillance report in Indonesia
Open accessAbstract
Background: Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. Many studies reported the use of Google Trends data as a potential data source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia. No study was performed to measure the correlation between country wide official dengue reports and Google Trends data in Indonesia.Objective: This study aims to measure the correlation between Google Trends data on dengue fever and the Indonesian national surveillance report.Methods: This research was a quantitative study using time series data (2012–2016). Two sets of data were analyzed using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the correlation between those data.Results: Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for three defined search terms with R-value range from 0.921 to 0.937 (p ≤ 0.05, overall period) which showed increasing trend in epidemic periods (2015–2016). Time lag correlation also indicated that Google Trends data can potentially be used for an early warning system and novel tool to monitor public reaction before the increase of dengue cases and during the outbreak.Conclusions: Google Trends data have a linear time series pattern and statistically correlated with annual official dengue reports. Identification of information-seeking behavior is needed to support the use of Google Trends for disease surveillance in Indonesia.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In Indonesia, Google Trends data had a linear time series pattern and statistically correlated with the annual official dengue report.
“For example, in Indonesia, Google Trends™ data had a linear time series pattern and it statistically correlated with the annual official dengue report [5].”
From Global Interest in Telehealth During COVID-19 Pandemic: An Analysis of Google Trends™ (2020) · cites this paper as [5]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009) · BMJ · doi:10.1136/bmj.b2393 · Cited by 7,299 · see citing works
Reduced incidence of Prevotella and other fermenters in intestinal microflora of autistic children
Kang, D.-W., Park, J. G., Ilhan, Z. E., Wallstrom, G., LaBaer, J., Adams, J. B., & Krajmalnik-Brown, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068322 · Cited by 976 · see citing works
Google Trends: A Web‐Based Tool for Real‐Time Surveillance of Disease Outbreaks
Carneiro, H. A., & Mylonakis, E. E. (2009) · Clinical Infectious Diseases · doi:10.1086/630200 · Cited by 815 · see citing works
Salathé, M., Bengtsson, L., Bodnar, T. J., Brewer, D. D., Brownstein, J. S., Buckee, C., Campbell, E. M., Cattuto, C., Khandelwal, S., Mabry, P. L., et al. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002616 · Cited by 500 · see citing works
Beck, F., Richard, J., Nguyen‐Thanh, V., Montagni, I., Parizot, I., & Renahy, É. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2934 · Cited by 363 · see citing works
Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings
Cervellin, G., Comelli, I., & Lippi, G. (2017) · Journal of Epidemiology and Global Health · doi:10.1016/j.jegh.2017.06.001 · Cited by 331 · see citing works
Internet-based surveillance systems for monitoring emerging infectious diseases
Milinovich, G. J., Williams, G. M., Clements, A., & Hu, W. (2013) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(13)70244-5 · Cited by 306 · see citing works
Chan, E. H., Sahai, V., Conrad, C., & Brownstein, J. S. (2011) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0001206 · Cited by 280 · see citing works
Prediction of Dengue Incidence Using Search Query Surveillance
Althouse, B. M., Ng, Y. Y., & Cummings, D. A. T. (2011) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0001258 · Cited by 268 · see citing works
Digital epidemiology: what is it, and where is it going?
Salathé, M. (2018) · Life Sciences Society and Policy · doi:10.1186/s40504-017-0065-7 · Cited by 215 · see citing works
Nölke, L., Mensing, M., Krämer, A., & Hornberg, C. (2015) · BMC Public Health · doi:10.1186/s12889-015-1423-0 · Cited by 192 · see citing works
Dynamic Forecasting of Zika Epidemics Using Google Trends
Teng, Y., Bi, D., Xie, G., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0165085 · Cited by 181 · see citing works
Using Google Trends for Influenza Surveillance in South China
Kang, M., Zhong, H., He, J., Rutherford, S., & Yang, F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055205 · Cited by 171 · see citing works
Evaluation of Internet-Based Dengue Query Data: Google Dengue Trends
Gluskin, R. T., Johansson, M. A., Santillana, M., & Brownstein, J. S. (2014) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0002713 · Cited by 154 · see citing works
Alicino, C., Bragazzi, N. L., Faccio, V., Amicizia, D., Panatto, D., Gasparini, R., Icardi, G., & Orsi, A. (2015) · Infectious Diseases of Poverty · doi:10.1186/s40249-015-0090-9 · Cited by 147 · see citing works
Dengue disease surveillance: an updated systematic literature review
Runge‐Ranzinger, S., McCall, P. J., Kroeger, A., & Horstick, O. (2014) · Tropical Medicine & International Health · doi:10.1111/tmi.12333 · Cited by 133 · see citing works
Correlation between National Influenza Surveillance Data and Google Trends in South Korea
Cho, S. J., Sohn, C. H., Jo, M. W., Shin, S.-Y., Lee, J.-H., Ryoo, S. M., Kim, W. Y., & Seo, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081422 · Cited by 127 · see citing works
Cost of dengue outbreaks: literature review and country case studies
Stahl, H.-C., Butenschoen, V. M., Tran, H. T., Gozzer, E., Skewes-Ramm, R., Mahendradhata, Y., Runge‐Ranzinger, S., Kroeger, A., & Farlow, A. W. K. (2013) · BMC Public Health · doi:10.1186/1471-2458-13-1048 · Cited by 87 · see citing works
Odu, B. P., Mitchell, S. L., Isa, H., et al. (2015) · Infectious Diseases of Poverty · doi:10.1186/2049-9957-4-1 · Cited by 79 · see citing works
Google dengue trends: An indicator of epidemic behavior. The Venezuelan Case
Strauss, R. A., Castro, J. S., Reintjes, R., & Torres, J. R. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.05.003 · Cited by 79 · see citing works
Bragazzi, N. L., Barberis, I., Rosselli, R., Gianfredi, V., Nucci, D., Moretti, M., Salvatori, T., Martucci, G., & Martini, M. (2016) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2017.1264742 · Cited by 74 · see citing works
Impediments of reporting dengue cases in India
Das, S., Sarfraz, A., Jaiswal, N. K., et al. (2017) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2017.02.004 · Cited by 48 · see citing works
Multiple imputation for time series data with Amelia package.
Zhang, Z. (2016) · PubMed · doi:10.3978/j.issn.2305-5839.2015.12.60 · Cited by 47 · see citing works
Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks
Seo, D., & Shin, S.-Y. (2017) · Healthcare Informatics Research · doi:10.4258/hir.2017.23.4.343 · Cited by 43 · see citing works
Investigating Relationships Between Health-Related Problems and Online Health Information Seeking
Oh, Y. S., & Song, N. (2016) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000000234 · Cited by 34 · see citing works
Ellery, P. J., Vaughn, W., Ellery, J., Bott, J. P., Ritchey, K. A., & Byers, L. N. (2008) · Journal of Communications In Healthcare · doi:10.1179/cih.2008.1.4.441 · Cited by 29 · see citing works
Adawi, M., Bragazzi, N. L., Watad, ., Sharif, Κ., Amital, H., & Mahroum, N. (2017) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.9136 · Cited by 20 · see citing works
Codd, A. S., Al‐Taei, S., & Tabi, Z. (2016) · Annals of Translational Medicine · doi:10.21037/atm.2016.10.30 · Cited by 6 · see citing works
Google dengue trends: An indicator of epidemic behavior. The Venezuelan case
Strauss, R. A., Castro, J. S., Reintjes, R., et al. (2016) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2016.11.298 · Cited by 6 · see citing works
Castro, J. S., Torres, J., Oletta, J. F., et al. (2016) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2016.11.329 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [5]Sitepu FY, Suprayogi A, Pramono D Evaluasi dan Implementasi Sistem Surveilans Demam Berdarah Dengue (DBD) di Kota Singkawang, Kalimantan Barat, 2010. J Litbang Pengendali Penyakit Bersumber Binatang Banjarnegara [Internet]. 2012; 8: 5– 10. Available from: http://ejournal.litbang.depkes.go.id/index.php/blb/article/view/3259
- [35]Indriani C, Fuad A, Kusnanto H Spatial-temporal pattern comparison between chikungunya outbreak and dengue hemmorhagic fever incidence at Kota Yogyakarta 2008. Ber Kedokt Masy. 2011; 27: 41– 50.
Cited by
Sorted by citations
Association of the COVID-19 pandemic with internet search volumes: a Google Trends™ analysis
Effenberger, M., Kronbichler, A., Shin, J. I., Mayer, G., Tilg, H., & Perco, P. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.04.033 · Cited by 270 · see citing works
Higgins, T. S., Wu, A. W., Sharma, D., Illing, E. A., Rubel, K., Ting, J. Y., & Snot Force Alliance (2020) · JMIR Public Health and Surveillance · doi:10.2196/19702 · Cited by 129 · see citing works
Global Interest in Telehealth During COVID-19 Pandemic: An Analysis of Google Trends™
Arshad Ali, S., Bin Arif, T., Maab, H., Baloch, M., Manazir, S., Jawed, F., & Ochani, R. K. (2020) · Cureus · doi:10.7759/cureus.10487 · Cited by 37 · see citing works
Digital Health Interventions in Dengue Surveillance to Detect and Predict Outbreak: A Scoping Review
Salim, M. F., Satoto, T. B. T., ., D., & Daniel, D. (2024) · The Open Public Health Journal · doi:10.2174/0118749445283264240116070726 · Cited by 13 · see citing works
- Authors
- Husnayain, A., Fuad, A., & Lazuardi, L.
- Affiliations
- Universitas Gadjah Mada (ID)
- Corresponding author
- Lazuardi, Lutfan
- Venue
- Global Health Action
- Publisher
- Informa UK Limited
- Published
- 2019-01-01
- Volume
- 12
- Issue
- 1
- Pages
- 1552652
- ISSN
- 1654-9716 (ISSN-L)
- 1654-9880
- PMC
- PMC6327938
- OpenAlex
- W2910885921
- Cited by
- 107
- References
- 30
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 6 · 2023: 13 · 2022: 11 · 2021: 21 · 2020: 35 · 2019: 3
- FWCI
- 13.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Global Health Action (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada (UPPM/221/M/05/04/05.17)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mosquito-borne diseases and control
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
- Keywords
- dengue, Google Trends, digital disease surveillance, nowcasting, early warning systems, lag correlation, information seeking, Indonesia
- MeSH terms
- Dengue, Disease Outbreaks, Forecasting, Humans, Indonesia, Population Surveillance, Internet, Cell Phone, Epidemics, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
