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Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making
Fernanda C Dórea1, Crawford W Revie2
1Department of Disease Control and Epidemiology, National Veterinary Institute, Uppsala, Sweden.
Frontiers in Veterinary Science
|March 29, 2021
Summary
The era of big data introduces variety, not just volume, to epidemiology. Integrating diverse health data sources is key for advanced population health surveillance beyond basic signal detection.
Area of Science:
- Public Health and Epidemiology
- Data Science and Health Informatics
Background:
- The "era of big data" has significantly impacted health, particularly epidemiology.
- The primary change is the increased variety of data sources, not just volume.
- New data sources, often not health-related, are increasingly used for epidemiological inference.
Purpose of the Study:
- To review opportunities for connecting diverse data sources in population health.
- To explore methods for generating information and supporting decision-making from varied data.
- To enable data-driven surveillance to achieve goals beyond simple signal detection.
Main Methods:
- Review of opportunities for data connection and integration.
- Analysis of challenges in combining evidence from multiple, heterogeneous data sources.
- Discussion on data interoperability and integration technicalities, distinct from access/privacy issues.
Main Results:
- Highlighting the potential of diverse data for epidemiological contextualization.
- Identifying strategies to overcome technical challenges in data integration.
- Demonstrating how varied data can enhance population health surveillance capabilities.
Conclusions:
- The variety of big data offers significant opportunities for advancing epidemiological research.
- Effective data integration and interoperability are crucial for leveraging these diverse sources.
- Expanded surveillance goals can be met by moving beyond simple signal detection through advanced data utilization.
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