Reducing Uncertainty in Risk Projections for Statewide Hospital Capacity in COVID-19

Enacting a statewide crisis response to the progression of COVID-19 requires a multiscale data collection,  aggregation, and analysis strategy to circumvent variability associated with hyper-local effects of  individual hospitals on one end and excessively coarse models based on national averages on the other.  Consider the relationship between local health care systems in normal times. Beyond health care privacy  laws and corporate rules, different hospital systems are friendly competitors, not used to sharing data

about real-time functioning. In contrast, the extraordinary nature of the COVID-19 crisis required state wide coordination among Delaware hospitals to rapidly develop “shotgun” data agreements so that  models can be fit with real-time data, balancing need and required effort. Integrating these data into  models generates projections that inform constantly changing risk theory analyses, including the need for  additional hospital facilities and medical supplies. With this proposal, we seek to develop a framework for  data collection, aggregation, integration, and cooperation based on our Delaware case-study that can be  used by hospital systems throughout the US to plan resources as the COVID-19 pandemic evolves as well  as for future pandemics. The highly interdisciplinary project team consists of experts in Public Policy,  Epidemiology, Physics, Computer Science and Industrial Engineering and has strong integration with local  health systems in Delaware.

There are two prongs to this project. The first is to study homeless data for the  State of Delaware; the second is to model demand for beds in the statewide hospital capacity in  response to COVID-19 data from Delaware. Prof. RJ Braun, director of the MS in data science at UD, will  be the overall mentor for the projects, and will coordinate the efforts led by Profs. Metraux and Dobler. 


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