COVID 19_EA - sporedata/researchdesigneR GitHub Wiki

• The COVID-19 Evidence Accelerator (EA) is a collaborative initiative launched at the request of the U.S. FDA in April 2020 to address the emerging COVID-19 pandemic by sharing and leveraging real-world evidence (RWE).

• Managed by the Reagan-Udall Foundation and Friends of Cancer Research, the COVID-19 EA involves 230 participating organizations across the healthcare ecosystem, including healthcare systems, insurers, tech companies, pharmaceuticals, laboratories, academics, and federal agencies.

• The COVID-19 EA uses a platform-as-a-service model, enabling diverse data sources to be managed and shared effectively. The platform's APIs facilitate integration and interoperability, supporting the rapid analysis and synthesis of real-world data (RWD) from various sources such as electronic health records (EHRs), claims, patient-generated data, and mobile health data.

• The COVID-19 EA's aim is to facilitate data sharing and analysis to understand and mitigate the impacts of COVID-19.

• Researchers using the COVID-19 EA must ensure compliance with regulatory standards such as GDPR and CCPA. IRB approval or equivalent is required before accessing the data. All analysis occurs within a secure computational environment to protect data privacy and security. Researchers must also accept the COVID-19 EA's terms of use, which establish the conditions for data sharing and usage.

• Quality assurance protocols are integral to the COVID-19 EA's data management, ensuring that data ingested and shared are reliable and suitable for research.

The COVID-19 EA focuses on three main workstreams:

1. Vaccine Evidence Accelerator: Investigates vaccine performance.
2. Diagnostic Evidence Accelerator: Addresses diagnostic and serological questions, such as the real-world performance of COVID-19 tests.
3. Therapeutic Evidence Accelerator: Expedites the identification of effective therapies for COVID-19 symptoms.

COVID-19 EA activities include:

1. Lab Meetings: Regular gatherings where participants share preliminary findings on research and methods.
2. Parallel Analysis: Multiple analytic partners apply master protocols to their own data, performing their analyses and sharing aggregated results.

1. Survey Data: Patient-reported outcomes.
2. Genetic Data: Information from COVID-19 genomic studies.
3. Vaccine Data: Information on vaccine performance and outcomes.
4. Clinical Data: EHR data.
5. Diagnostic Data: Results from COVID-19 testing.
6. Therapeutic Data: Data on treatments and their effectiveness.

• Data Integration: Variability in data formats and standards can pose challenges.
• Privacy Concerns: Ensuring the privacy and security of health data is paramount, necessitating rigorous adherence to regulations like GDPR and CCPA.
• Data Completeness: Evolving data sources may affect the comprehensiveness of available datasets.
• Access Restrictions: Strict compliance with regulatory standards and IRB approval is required.

• COVID-19 Evidence Accelerator
• Friends of Cancer Research - COVID-19 Evidence Accelerator
• Sharing Health Data: The Why, the Will, and the Way Forward
• Accelerating Evidence Generation by Convening Diverse Stakeholders Across the Real-World Data Ecosystem
• COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

Academic institutions, pharmaceutical companies, and other research entities can access COVID-19 EA datasets by adhering to institutional protocols and agreements. Collaboration with the COVID-19 EA involves a review process, provision of IRB approval, and acceptance of terms of use.

Researchers interested in accessing the COVID-19 EA platform must initiate contact through direct outreach to the Reagan-Udall Foundation or Friends of Cancer Research. After obtaining IRB approval and completing the necessary review process, they can access data within the EA's secure computational environment.

• COVID-19 Evidence Accelerator

[1] Grossmann C, Chua PS, Ahmed M, Greene SM. Sharing Health Data: The Why, the Will, and the Way Forward. 2023.

[2] Grossmann C, Chua PS, Ahmed M, Greene SM. CASE STUDY: COVID-19 Evidence Accelerator (EA) - Sharing Health Data. National Academies Press (US). 2022.