ArticlePLOS digital health2026
Public health informatics tools for dengue risk management: A systematic review.
Article in PLOS digital health, 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.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Public health informatics (PHI) tools, including Geographic Information Systems (GIS), Electronic Health Records (EHRs), and Health Information Exchange systems, are increasingly applied to dengue fever surveillance, prevention, and control. Despite their growing adoption, a synthesis of empirical evidence examining their real-world application across endemic settings has not previously been conducted. This systematic review aimed to examine how PHI tools have been applied to dengue risk management, and to evaluate the certainty of evidence supporting their use. A structured literature search was conducted across PubMed, EBSCO/MEDLINE, and Web of Science. Nineteen peer-reviewed empirical studies published between 2010 and 2024 were included following eligibility screening against pre-defined inclusion and exclusion criteria. Study quality was assessed using the Newcastle-Ottawa Scale adapted for cross-sectional studies. Certainty of evidence was evaluated using the GRADE framework across five domains: risk of bias, inconsistency, indirectness, imprecision, and publication bias. Findings were synthesised narratively and organised into three functional categories: mapping and visualisation (n = 12), epidemiological insights (n = 5), and enhanced surveillance (n = 2). GIS was the most frequently used tool, consistently identifying dengue hotspots and supporting spatial dengue risk mapping across diverse geographic settings. EHR-linked health information systems supported epidemiological profiling and, in a limited number of studies, improved outbreak detection. Certainty of evidence was rated as very low across all three categories, reflecting the low evidence associated to observational study designs, methodological heterogeneity, and the uniform reporting of positive findings across all included studies. PHI tools show consistent descriptive utility in dengue surveillance across diverse settings. However, given the very low certainty of evidence, conclusions should be interpreted with caution. Gaps remain in high-burden regions including Sub-Saharan Africa and the Middle East. Standardised evaluation frameworks, broader geographic representation, and integration with emerging digital health technologies are needed to strengthen the evidence base. Systematic review registration: PROSPERO; registration number CRD42024572021.
Identifiers
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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.