Future of Pandemic Prevention and Response CCC Workshop Report
- URL: http://arxiv.org/abs/2403.00096v1
- Date: Thu, 29 Feb 2024 19:54:22 GMT
- Title: Future of Pandemic Prevention and Response CCC Workshop Report
- Authors: David Danks, Rada Mihalcea, Katie Siek, Mona Singh, Brian Dixon, and
Haley Griffin
- Abstract summary: 2-day workshop brought together researchers and practitioners in healthcare, computer science, and social sciences.
attendees discussed how the COVID-19 pandemic amplified gaps in our health and computing systems.
Three major computing themes emerged from the workshop: models, data, and infrastructure.
- Score: 20.088513810701762
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This report summarizes the discussions and conclusions of a 2-day
multidisciplinary workshop that brought together researchers and practitioners
in healthcare, computer science, and social sciences to explore what lessons
were learned and what actions, primarily in research, could be taken. One
consistent observation was that there is significant merit in thinking not only
about pandemic situations, but also about peacetime advances, as many
healthcare networks and communities are now in a perpetual state of crisis.
Attendees discussed how the COVID-19 pandemic amplified gaps in our health and
computing systems, and how current and future computing technologies could fill
these gaps and improve the trajectory of the next pandemic.
Three major computing themes emerged from the workshop: models, data, and
infrastructure. Computational models are extremely important during pandemics,
from anticipating supply needs of hospitals, to determining the care capacity
of hospital and social service providers, to projecting the spread of the
disease. Accurate, reliable models can save lives, and inform community leaders
on policy decisions. Health system users require accurate, reliable data to
achieve success when applying models. This requires data and measurement
standardization across health care organizations, modernizing the data
infrastructure, and methods for ensuring data remains private while shared for
model development, validation, and application. Finally, many health care
systems lack the data, compute, and communication infrastructures required to
build models on their data, use those models in ordinary operations, or even to
reliably access their data. Robust and timely computing research has the
potential to better support healthcare works to save lives in times of crisis
(e.g., pandemics) and today during relative peacetime.
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