Key takeaways
- Why the US still lacked a national zero-prevalence estimate for COVID-19 a year into the pandemic, and why real-time interoperable surveillance data matters more than raw case counts
- How vaccine rollouts in Texas, Illinois, Chicago, and LA County struggled to reach the hardest-hit minority communities even when the data showed where the need was
- Why tracking new variants depends on active genomic surveillance, and how the UK's practice of sequencing 10 percent of positive samples helped it catch the B117 strain early
- How nursing teams are managing burnout through rotation, retraining clinicians for ICU duty, and expanding virtual education and telehealth-based self-triage
- Why Congress funded roughly 3 billion dollars to consolidate the CDC's more than 100 separate surveillance systems, and what still has to happen to make that money effective
- The four steps panelists say public health data needs: identify and prioritize the right data, standardize it, automate reporting to cut clinician burden, and open two-way access across policy, public health, and clinical settings
Overview
A pandemic response is only as good as its data, and this panel makes the gaps plain. H2O.ai's Prashant Natrajan moderates Dipesh Chandra (Bon Secours Mercy Health), Joel White (Horizon Government Affairs), nurse informaticist Danielle Siarri, and biostatistician Nick Jewell (UC Berkeley, London School of Hygiene and Tropical Medicine) on what worked in the COVID-19 response and what didn't. They cover the lack of real-time interoperable surveillance data, uneven vaccine access in minority communities, variant tracking, and clinician burnout, then outline what a better public health data infrastructure needs to look like.