Challenges in reported COVID-19 data: best practices and recommendations for future epidemics

Area of Science:

• Epidemiology
• Public Health
• Data Science

Background:

• The COVID-19 pandemic highlighted the need for composite data sources during large-scale infectious disease events.
• Challenges arise from combining disparate data, with various organizations attempting standardization with mixed results.

Purpose of the Study:

• To discuss best practices for researchers creating composite data compilations.
• To identify and address key challenges in integrating data from multiple, often discrepant, sources for a comprehensive spatiotemporal view of outbreaks.

Main Methods:

• Discussion of best practices for data compilation.
• Categorization of challenges into data dissemination, data elements, and epidemiological factors.
• Highlighting guidelines to address identified challenges.

Main Results:

• Identified three main categories of challenges: data dissemination (discrepant estimates, varying structures), data elements (non-standard formats, differing resolutions), and epidemiological factors (missing data, lags, corrections, definition changes).
• Emphasized the need for researchers to remain engaged in data assessment, integration, validation, and interpretation, cautioning against over-reliance on automated systems.

Conclusions:

• Efforts to reform the global health data ecosystem must consider these challenges.
• Development and incorporation of standards and best practices are crucial for more robust, transparent, and interoperable data.
• Epidemiological expertise is vital for resolving data challenges in surveillance data integration.