ReviewJournal of public health research2026
Digital epidemiology: Utilizing big data for public health surveillance and disease outbreak prediction.
Review in Journal of public health research, 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital epidemiology represents a transformative approach to public health surveillance and disease outbreak prediction by leveraging big data from diverse digital sources, including social media, search engine queries, wearable devices, mobile health applications, environmental sensors, and electronic health records. Unlike traditional epidemiological methods that rely primarily on structured clinical or administrative datasets, digital epidemiology uses real-time, high-volume, and often unstructured data streams to monitor population health dynamics and detect emerging threats with improved temporal and spatial resolution. This study was conducted as a structured literature review guided by PRISMA principles. A comprehensive search of PubMed, Scopus, Web of Science, and IEEE Xplore identified studies published between 1 January 2014 and 1 January 2026. The review synthesizes evidence on methodological foundations, analytical techniques such as machine learning and natural language processing, and applications in outbreak detection, syndromic surveillance, behavioral risk monitoring, and health system responsiveness. Included studies showed that digital signals can improve forecasting accuracy, provide earlier indications of disease activity than conventional systems, and strengthen situational awareness during public health emergencies when integrated with established surveillance frameworks. However, important challenges remain, including data privacy, algorithmic transparency, interoperability, and integration into routine public health systems. This review provides an updated synthesis across multiple digital data streams and emerging artificial intelligence applications while identifying persistent methodological, governance, and equity-related gaps requiring future attention. With continued innovation, stronger regulatory oversight, and interdisciplinary collaboration, digital epidemiology has the potential to support proactive, predictive, and precision-oriented public health strategies for future global health threats.
Indexed as
Identifiers
What 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.