# Data-Driven Disease Surveillance

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/data-driven-disease-surveillance/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the use of digital data sources such as search engine queries, social media content, and internet usage patterns for disease surveillance, tracking epidemics, and early detection of infectious diseases. It also explores the application of tools like Google Trends and Geographic Information Systems in public health informatics for monitoring and analyzing epidemiological patterns. |
| Domain | Health Sciences |
| Field | Medicine |
| OpenAlex ID | t11819 |
| Works | 92 |

## Topic papers all

Showing 15 of 92.

- [Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016](https://scholariq.org/papers/global-regional-and-national-comparative-risk-assessment-of-84-behavioural-2/)
- [Table S1 - Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak](https://scholariq.org/papers/table-s1-pandemics-in-the-age-of-twitter-content-analysis-of-tweets-during-the/)
- [Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet](https://scholariq.org/papers/infodemiology-and-infoveillance-framework-for-an-emerging-set-of-public-health/)
- [Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations](https://scholariq.org/papers/machine-learning-and-prediction-in-medicine-beyond-the-peak-of-inflated/)
- [Practitioner’s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls](https://scholariq.org/papers/practitioner-s-guide-to-latent-class-analysis-methodological-considerations-and/)
- [The Use of Google Trends in Health Care Research: A Systematic Review](https://scholariq.org/papers/the-use-of-google-trends-in-health-care-research-a-systematic-review/)
- [Health literacy interventions and outcomes: an updated systematic review.](https://scholariq.org/papers/health-literacy-interventions-and-outcomes-an-updated-systematic-review/)
- [Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong](https://scholariq.org/papers/clustering-and-superspreading-potential-of-sars-cov-2-infections-in-hong-kong/)
- [Annual report to the nation on the status of cancer, 1975–2004, featuring cancer in American Indians and Alaska Natives](https://scholariq.org/papers/annual-report-to-the-nation-on-the-status-of-cancer-1975-2004-featuring-cancer/)
- [Spatiotemporal pattern of COVID-19 spread in Brazil](https://scholariq.org/papers/spatiotemporal-pattern-of-covid-19-spread-in-brazil/)
- [Development of a Database of Health Insurance Claims: Standardization of Disease Classifications and Anonymous Record Linkage](https://scholariq.org/papers/development-of-a-database-of-health-insurance-claims-standardization-of-disease/)
- [Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States](https://scholariq.org/papers/evaluation-of-individual-and-ensemble-probabilistic-forecasts-of-covid-19/)
- [World leaders’ usage of Twitter in response to the COVID-19 pandemic: a content analysis](https://scholariq.org/papers/world-leaders-usage-of-twitter-in-response-to-the-covid-19-pandemic-a-content/)
- [Association of the COVID-19 pandemic with Internet Search Volumes: A Google TrendsTM Analysis](https://scholariq.org/papers/association-of-the-covid-19-pandemic-with-internet-search-volumes-a-google/)
- [Tweet for Behavior Change: Using Social Media for the Dissemination of Public Health Messages](https://scholariq.org/papers/tweet-for-behavior-change-using-social-media-for-the-dissemination-of-public/)

## Topic primary papers

Showing 15 of 29.

- [Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet](https://scholariq.org/papers/infodemiology-and-infoveillance-framework-for-an-emerging-set-of-public-health/)
- [Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations](https://scholariq.org/papers/machine-learning-and-prediction-in-medicine-beyond-the-peak-of-inflated/)
- [The Use of Google Trends in Health Care Research: A Systematic Review](https://scholariq.org/papers/the-use-of-google-trends-in-health-care-research-a-systematic-review/)
- [Association of the COVID-19 pandemic with Internet Search Volumes: A Google TrendsTM Analysis](https://scholariq.org/papers/association-of-the-covid-19-pandemic-with-internet-search-volumes-a-google/)
- [Using Google Street View to Audit the Built Environment: Inter-rater Reliability Results](https://scholariq.org/papers/using-google-street-view-to-audit-the-built-environment-inter-rater-reliability/)
- [Google Flu Trends: Correlation With Emergency Department Influenza Rates and Crowding Metrics](https://scholariq.org/papers/google-flu-trends-correlation-with-emergency-department-influenza-rates-and/)
- [Chapter 4: Using Twitter as a Data Source: An Overview of Ethical, Legal, and Methodological Challenges](https://scholariq.org/papers/chapter-4-using-twitter-as-a-data-source-an-overview-of-ethical-legal-and/)
- [Assessment of the Impact of Media Coverage on COVID-19–Related Google Trends Data: Infodemiology Study](https://scholariq.org/papers/assessment-of-the-impact-of-media-coverage-on-covid-19-related-google-trends/)
- [An unsupervised machine learning model for discovering latent infectious diseases using social media data](https://scholariq.org/papers/an-unsupervised-machine-learning-model-for-discovering-latent-infectious/)
- [Evaluating Sampling Methods for Content Analysis of Twitter Data](https://scholariq.org/papers/evaluating-sampling-methods-for-content-analysis-of-twitter-data/)
- [Evaluation of school absenteeism data for early outbreak detection, New York City](https://scholariq.org/papers/evaluation-of-school-absenteeism-data-for-early-outbreak-detection-new-york-city/)
- [Spatial Analysis of Tuberculosis Cases in Migrants and Permanent Residents, Beijing, 2000–2006](https://scholariq.org/papers/spatial-analysis-of-tuberculosis-cases-in-migrants-and-permanent-residents/)
- [Using Baidu Search Index to Predict Dengue Outbreak in China](https://scholariq.org/papers/using-baidu-search-index-to-predict-dengue-outbreak-in-china/)
- [Big Data Knowledge in Global Health Education](https://scholariq.org/papers/big-data-knowledge-in-global-health-education/)
- [An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US](https://scholariq.org/papers/an-intelligent-early-warning-system-of-analyzing-twitter-data-using-machine/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.