DeepAI AI Chat
Log In Sign Up

Routine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden

by   Len Covello, et al.

Throughout the COVID-19 pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community and as predictors of clinical burden. In the absence of any successful public or academic campaign for comprehensive or random testing, we have developed a proxy method for synthetic random sampling, based on viral RNA testing of patients who present for elective procedures within a hospital system. We present here an approach under multilevel regression and poststratification (MRP) to collecting and analyzing data on viral exposure among patients in a hospital system and performing statistical adjustment that has been made publicly available to estimate true viral incidence and trends in the community. We apply our MRP method to track viral behavior in a mixed urban-suburban-rural setting in Indiana. This method can be easily implemented in a wide variety of hospital settings. Finally, we provide evidence that this model predicts the clinical burden of SARS-CoV-2 earlier and more accurately than currently accepted metrics.


page 1

page 2

page 3

page 4


Beyond Vaccination Rates: A Synthetic Random Proxy Metric of Total SARS-CoV-2 Immunity Seroprevalence in the Community

Explicit knowledge of total community-level immune seroprevalence is cri...

EpiBeds: Data informed modelling of the COVID-19 hospital burden in England

The first year of the COVID-19 pandemic put considerable strain on the n...

AI-based Monitoring and Response System for Hospital Preparedness towards COVID-19 in Southeast Asia

This research paper proposes a COVID-19 monitoring and response system t...

What can we learn about SARS-CoV-2 prevalence from testing and hospital data?

Measuring the prevalence of active SARS-CoV-2 infections is difficult be...

Preventing Hospital Acquired Infections Through a Workflow-Based Cyber-Physical System

Hospital acquired infections (HAI) are infections acquired within the ho...

Measuring performance for end-of-life care

Although not without controversy, readmission is entrenched as a hospita...

Technological Platform for the Prevention and Management of Healthcare Associated Infections and Outbreaks

Hospital acquired infections are infections that occur in patients durin...