ReviewInfection prevention in practice2024
Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks.
Review in Infection prevention in practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed.
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Climate Change and Autochthonous Vector-Borne Disease Transmission in Europe: Dengue as a Sentinel Signal for Surveillance and Preparedness.Tropical medicine and infectious disease · 2026Article
- Experts' Opinions on the Sustainable Use of Digital Health Tools for Effective Future Pandemic Preparedness and Response: Questionnaire Study.JMIR public health and surveillance · 2026Article
- Review
- An ARIMAX model integrating Baidu index for improved hand, foot, and mouth disease incidence prediction.Scientific reports · 2026Article
- Digital epidemiology: Utilizing big data for public health surveillance and disease outbreak prediction.Journal of public health research · 2026Review
- Early warning of hepatitis B epidemics in Henan Province, China, from 2014 to 2023 based on Baidu Index and Bayesian Structural Time Series model.Archives of public health = Archives belges de sante publique · 2026Article
- Cross-lingual transfer with multilingual language models for influenza-like-illness detection in social media texts.npj digital public health · 2026Article
- Pathogenic Significance of Trypanosomatids: Progress in Drug Resistance, Control Strategies, and Artificial Intelligence.Interdisciplinary perspectives on infectious diseases · 2026Review
- Challenges and recommendations for embracing digital tools for one health surveillance in LMICs: Viewpoints from a special session of 17th ISVEE.One health (Amsterdam, Netherlands) · 2025Article
- Infectious Disease Surveillance in the Era of Big Data and AI: Opportunities and Pitfalls.Cureus · 2025Review
- Spatio-Temporal Modelling and Forecasting of the Prolonged Measles Outbreak in Romania: Insights and Challenges.Healthcare (Basel, Switzerland) · 2025Article
- Early Warning of Infectious Disease Outbreaks Using Social Media and Digital Data: A Scoping Review.International journal of environmental research and public health · 2025Article
- The role of artificial intelligence in detecting avian influenza virus outbreaks: A review.Open veterinary journal · 2025Review
- Optimizing epidemic prevention in nursing homes using clinical surveillance of respiratory infections.Infection prevention in practice · 2025Article
- Foodborne Event Detection Based on Social Media Mining: A Systematic Review.Foods (Basel, Switzerland) · 2025Article
- From pandemics to preparedness: harnessing AI, CRISPR, and synthetic biology to counter biosecurity threats.Frontiers in public health · 2025Review
- Unified framework for the ingestion of early epidemic data for downstream data analytics.Wellcome open research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital epidemiology is the process of investigating the dynamics of disease-related patterns, both social and clinical, as well as the causes of these trends in epidemiology. Digital epidemiology, utilising big data from a variety of digital sources, has emerged as a viable method for early detection and monitoring of viral outbreaks. The present review gives an overview of digital epidemiology, emphasising its importance in the timely detection of infectious disease outbreaks. Researchers may discover and track outbreaks in real time using digital data sources such as search engine queries, social media trends, and digital health records. However, data quality, concerns about privacy, and data interoperability must be addressed to maximise the effectiveness of digital epidemiology. As the global landscape of infectious diseases evolves, integrating digital epidemiology becomes critical to improving pandemic preparedness and response efforts. Integrating digital epidemiology into routine monitoring systems has the potential to improve global health outcomes and save lives in the event of viral outbreaks.
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.