Estimating Preventable COVID19 Infections Related to Elective Outpatient Surgery in Washington State: A Quantitative Model
Zhang, Y.; Cheng, S.-R.
Show abstract
BackgroundAs the number of suspected and confirmed COVID-19 cases in the US continues to rise, the US surgeon general, Centers for Disease Control and Prevention, and several specialty societies have issued recommendations to consider canceling elective surgeries. However, these recommendations have also faced controversy and opposition. MethodsUsing previously published information and publicly available data on COVID-19 infections, we calculated a transmission rate and generated a mathematical model to predict a lower bound for the number of healthcare-acquired COVID-19 infections that could be prevented by canceling or postponing elective outpatient surgeries in Washington state. ResultsOur model predicts that over the course of 30 days, at least 75.9 preventable patient infections and at least 69.3 preventable healthcare worker (HCW) infections would occur in WA state alone if elective outpatient procedures were to continue as usual. ConclusionCanceling elective outpatient surgeries during the COVID-19 pandemic could prevent a large number of patient and healthcare worker infections.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The Effect of Stay-at-Home Orders on COVID-19 Cases and Fatalities in the United States 93%
- Threshold analyses on rates of testing, transmission, and contact for COVID-19 control in a university setting 92%
- Spatiotemporal Analysis of Medical Resource Deficiencies in the U.S. under COVID-19 Pandemic 92%
Similar papers in this journal
- Probabilistic modelling of effects of antibiotics and calendar time on transmission of healthcare-associated infection 93%
- The effect of multiple interventions to balance healthcare demand for controlling COVID-19 outbreaks: a modelling study 93%
- A Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand 92%
Similar papers in this journal
- Estimating the Cumulative Incidence of COVID-19 in the United States Using Four Complementary Approaches 94%
- An expert judgment model to predict early stages of the COVID-19 outbreak in the United States 93%
- Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: results of a Bayesian evidence synthesis model 93%
Similar papers in this journal
- Estimating COVID-19 Hospitalizations in the United States with surveillance data using a Bayesian Hierarchical model 93%
- Evaluation of Nowcasting for Real-Time COVID-19 Tracking — New York City, March–May 2020 91%
- A Multivariate Forecasting Model for the COVID-19 Hospital Census Based on Local Infection Incidence 91%
Similar papers in this journal
- Estimating individual risk of catheter-associated urinary tract infections using explainable artificial intelligence on clinical data 91%
- Outbreak of COVID-19 and Interventions in One of the Largest Jails in the United States: Cook County, IL, 2020 90%
- Impact of personal protective equipment use on health care workers’ physical health during the COVID-19 pandemic: a systematic review and meta-analysis 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.