ReviewHealthcare informatics research2018
Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies.
Review in Healthcare informatics research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Social media based surveillance systems for healthcare using machine learning: A systematic review.Journal of biomedical informatics · 2020Pooled it
- Digital epidemiology: Utilizing big data for public health surveillance and disease outbreak prediction.Journal of public health research · 2026Review
- Viral emergence and pandemic preparedness in a One Health framework.Nature reviews. Microbiology · 2026Review
- Advances in Animal Disease Surveillance and Information Systems and Their Role in Disease Control and Prevention: Implications in Ethiopia.Veterinary medicine and science · 2025Review
- Investigating Social Network Peer Effects on HIV Care Engagement Using a Fuzzy-Like Matching Approach: Cross-Sectional Secondary Analysis of the N2 Cohort Study.JMIR public health and surveillance · 2025Article
- Investigating Google Trends to forecast acute febrile illness outbreaks in North India reported through the Integrated Disease Surveillance Program.BMC infectious diseases · 2025Article
- Estimation of free-roaming dog populations using Google Street View: A methodological study.PloS one · 2025Article
- Epidemiological methods in transition: Minimizing biases in classical and digital approaches.PLOS digital health · 2025Review
- Tracking Chlamydia and Syphilis in the Detroit Metro Area by Molecular Analysis of Environmental Samples.Environmental science & technology · 2024Article
- Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review.Current HIV/AIDS reports · 2023Article
- Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models.Nature communications · 2023Article
- Establishment of a Public Mental Health Database for Research Purposes in the Ferrara Province: Development and Preliminary Evaluation Study.JMIR medical informatics · 2023Article
- Predicting pulmonary tuberculosis incidence in China using Baidu search index: an ARIMAX model approach.Environmental health and preventive medicine · 2023Article
- Review
- COVID-19 and digital epidemiology.Zeitschrift fur Gesundheitswissenschaften = Journal of public health · 2022Article
- Validating viral hepatitis B and C diagnosis codes: a retrospective analysis using Ontario's health administrative data.Canadian journal of public health = Revue canadienne de sante publique · 2021Article
- Power of Big Data in ending HIV.AIDS (London, England) · 2021Article
- Digital public health surveillance: a systematic scoping review.NPJ digital medicine · 2021Article
- Coronavirus (Covid-19) pandemic: How may communication strategies influence our behaviours?International journal of disaster risk reduction : IJDRR · 2021Article
- COVID-19 in the Twitterverse, from epidemic to pandemic: information-sharing behavior and Twitter as an information carrier.Scientometrics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method.
methodsWe reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies.
resultsWe identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses.
conclusionsDigital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.
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.