ArticleEJIFCC2026
Using Google Trends to Predict Dengue Outbreaks in Pakistan: A Time-Series and Causality Analysis.
Article in EJIFCC, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Dengue fever is a significant public health issue in Pakistan. Seasonal outbreaks lead to considerable illness and economic strain. Traditional surveillance systems, like the Integrated Disease Surveillance and Response (IDSR) system, depend mainly on passive reporting. They often identify outbreaks only after transmission has already worsened. Digital data sources, such as Google Trends, might provide early signs of increasing disease activity by reflecting changes in public information-seeking behavior. Objective: To determine if Google search activity for dengue-related terms can predict dengue case counts in Pakistan during 2024. Methods: Weekly dengue case count data were collected from the IDSR Weekly Bulletin. Weekly Relative Search Volume (RSV) data for "dengue," "dengue fever," "dengue symptoms," and "dengue treatment" were gathered from Google Trends. Case counts were adjusted to a 0-100 scale. Pearson correlation was used to examine the relationship between search activity and case count data. Time-series causality analysis was carried out using the Toda-Yamamoto method, following tests for stationarity and the specification of the Vector Autoregression (VAR) model. Results: Strong positive correlations were found between dengue-related search interest and case count data (r = 0.756-0.832; p < 0.001). The strongest correlation was for the term "dengue." Toda-Yamamoto causality testing showed that search activity significantly predicted subsequent increases in dengue case counts for "dengue symptoms" (p = 0.004), "dengue treatment" (p = 0.001), and "dengue" (p = 0.004), with an approximate two-week lag. Conclusion: Google Trends search data effectively reflects and can anticipate dengue case count patterns in Pakistan. This suggests that it could serve as a cost-effective, real-time early warning tool to complement the national surveillance system. Combining digital search monitoring with IDSR reporting could lead to earlier detection and a more proactive public health response.
Identifiers
42730060PMC13563537What OpenQuestion holds
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.