Estimating the Growth Rate and Doubling Time for Short-Term Prediction and Monitoring Trend During the COVID-19 Pandemic with a SAS Macro
Xu, S.; Clarke, C.; Shetterly, S.; Narwaney, K.
Show abstract
Coronavirus disease (COVID-19) has spread around the world causing tremendous stress to the US health care system. Knowing the trend of the COVID-19 pandemic is critical for the federal and local governments and health care system to prepare plans. Our aim was to develop an approach and create a SAS macro to estimate the growth rate and doubling time in days if growth rate is positive or half time in days if growth rate is negative. We fit a series of growth curves using a rolling approach. This approach was applied to the hospitalization data of Colorado State during March 13th and April 13th. The growth rate was 0.18 (95% CI=(0.11, 0.24)) and the doubling time was 5 days (95% CI= (4, 7)) for the period of March 13th-March 19th; the growth rate reached to the minimum -0.19 (95% CI= (-0.29, -0.10)) and the half time was 4 days (95% CI= (2, 6)) for the period of April 2nd - April 8th. This approach can be used for regional short-term prediction and monitoring the regional trend of the COVID-19 pandemic.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Estimation of the probability of reinfection with COVID-19 coronavirus by the SEIRUS model 92%
- A Multivariate Forecasting Model for the COVID-19 Hospital Census Based on Local Infection Incidence 92%
- Estimating COVID-19 Hospitalizations in the United States with surveillance data using a Bayesian Hierarchical model 90%
Similar papers in this journal
- Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study 92%
- A Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand 92%
- A Recursive Bifurcation Model for Predicting the Peak of COVID-19 Virus Spread in United States and Germany 91%
Similar papers in this journal
- Use Crow-AMSAA Method to predict the cases of the Coronavirus 19 in Michigan and U.S.A 93%
- Modelling the coronavirus disease (COVID-19) outbreak on the Diamond Princess ship using the public surveillance data from January 20 to February 20, 2020 93%
- Impact of School Reopening on Pandemic Spread: A Case Study using an Agent-Based Model for COVID-19 92%
"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.