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Modelling practices, data provisioning, sharing and dissemination needs for pandemic decision-making: a European
Esther van Kleef1,2,3, Wim Van Bortel3, Elena Arsevska4
1Centre for Tropical Medicine & Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Summary
Advanced outbreak analytics informed COVID-19 decisions, but data gaps and collaboration needs persist. Future epidemic intelligence requires better non-traditional data collection and open sharing of models and code.
Area of Science:
- Epidemic intelligence
- Mathematical modeling
- Public health policy
Background:
- Outbreak analytics were crucial for COVID-19 decision-making.
- Limited studies evaluate the evolution of modeling, data use, and science-policy interactions during health emergencies.
Purpose of the Study:
- To assess the evolution of modeling practices and data usage during public health crises.
- To identify data gaps and evaluate science-policy engagement for future epidemic intelligence.
- To inform strategies for enhanced global preparedness.
Main Methods:
- A two-stage semiquantitative survey of modelers within a European epidemic intelligence consortium.
- Descriptive analysis of responses across early, mid-, and late pandemic phases.
- Assessment of policy impact using policy citations in Overton.
Main Results:
- COVID-19 modeling shifted from understanding dynamics to evaluating interventions and vaccines.
- Traditional surveillance data were available, but real-time non-traditional data (behavioral, serological) were lacking.
- Frequent engagement with decision-makers occurred, yet open-access code sharing was limited (<50%).
Conclusions:
- Modeling needs and uses evolve during public health crises.
- Persistent gaps in non-traditional data necessitate rethinking collection and sharing, including from commercial sources.
- Future preparedness must enhance collaborative platforms for data/code sharing and academia-policymaker engagement.
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