ABOUT

Rare adverse events following immunization (AEFIs) occurring in <1 per 10,000 vaccine recipients may not be detected in prelicensure clinical trials. The success of mass COVID-19 immunization programs depends upon establishing and maintaining confidence in the safety of COVID-19 vaccines through augmented surveillance and research.

The purpose of INSIS is to address knowledge gaps in our understanding of the causes of adverse events of special interest following vaccination, risk factors for developing AEFIs, and determine the best way to immunize individuals with prior AEFIs or risk factors for developing AEFIs. While the initial focus will be on AEFIs following COVID-19 vaccination, INSIS’s mandate can be broadened to other vaccines in the future.

INSIS aims to inform safety evaluation and risk-benefit assessment of new vaccines for emerging pathogens in low-, middle- and high-income countries through:

• Establishing a multi-national, interdisciplinary platform for AEFI investigation with a formal governance structure, data management core, and adversomics pipeline;

• Partnering with experts in low- and middle-income countries to expand capacity for case finding and evaluation of patients with AEFIs;

• Implementing harmonized case definitions and protocols for data and sample collection of rare AEFIs to characterize the clinical risk factors and molecular signatures/biomarkers associated with rare AEFIs such as TTS, myocarditis, and pericarditis following COVID-19 vaccination.

In addition, INSIS seeks to expand capacity for AEFI investigation in low- and middle-income countries through partnerships with existing vaccine research networks and local providers.

INSIS was formed in the fall of 2020. INSIS has a steering committee and working groups for multi-OMICs and data management and regular meetings of the working groups to discuss network strategy, priorities, and emerging vaccine signals following administration of COVID-19 vaccines, potential pathogens, and provide approaches for their investigation, systemic data, and sample collection.