Observational studyJournal of medical Internet research2020
Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study.
Observational study in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- Seasonal pattern, global search trend, and public interest in arthritis from 2004 to 2022: an infodemiology study.Immunologic research · 2025Article
- Early prediction of the outbreak risk of dengue fever in Ba Ria-Vung Tau province, Vietnam: An analysis based on Google trends and statistical models.Infectious Disease Modelling · 2025Article
- Utilizing Google Trends data to enhance forecasts and monitor long COVID prevalence.Communications medicine · 2025Article
- The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study.JMIR public health and surveillance · 2022Observational
- Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis.JMIR dermatology · 2022Article
- Searching for HIV and AIDS Health Information in South Africa, 2004-2019: Analysis of Google and Wikipedia Search Trends.JMIR formative research · 2022Article
- Epidemiologic evolution of common cutaneous infestations and arthropod bites: A Google Trends analysis.JAAD international · 2021Article
- Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity.BMC medical research methodology · 2021Article
- Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study.Journal of medical Internet research · 2021Article
- Global Public Interest and Seasonal Variations in Alzheimer's Disease: Evidence From Google Trends.Frontiers in medicine · 2021Article
- Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends.JMIR public health and surveillance · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe popularity of dengue can be inferred from Google Trends that summarizes Google searches of related topics. Both the disease and its Google Trends have a similar source of causation in the dengue virus, leading us to hypothesize that dengue incidence and Google Trends results have a long-run equilibrium.
objectiveThis research aimed to investigate the properties of this long-run equilibrium in the hope of using the information derived from Google Trends for the early detection of upcoming dengue outbreaks.
methodsThis research used the cointegration method to assess a long-run equilibrium between dengue incidence and Google Trends results. The long-run equilibrium was characterized by their linear combination that generated a stationary process. The Dickey-Fuller test was adopted to check the stationarity of the processes. An error correction model (ECM) was then adopted to measure deviations from the long-run equilibrium to examine the short-term and long-term effects. The resulting models were used to determine the Granger causality between the two processes. Additional information about the two processes was obtained by examining the impulse response function and variance decomposition.
resultsThe Dickey-Fuller test supported an implicit null hypothesis that the dengue incidence and Google Trends results are nonstationary processes (P=.01). A further test showed that the processes were cointegrated (P=.01), indicating that their particular linear combination is a stationary process. These results permitted us to construct ECMs. The model showed the direction of causality of the two processes, indicating that Google Trends results will Granger-cause dengue incidence (not in the reverse order).
conclusionsVarious hypothesis testing results in this research concluded that Google Trends results can be used as an initial indicator of upcoming dengue outbreaks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.