Projections for first-wave COVID-19 deaths across the US using social-distancing measures derived from mobile phones
Woody, S.; Garcia Tec, M.; Dahan, M.; Gaither, K.; Fox, S.; Meyers, L. A.; Scott, J. G.
Show abstract
We propose a Bayesian model for projecting first-wave COVID-19 deaths in all 50 U.S. states. Our models projections are based on data derived from mobile-phone GPS traces, which allows us to estimate how social-distancing behavior is "flattening the curve" in each state. In a two-week look-ahead test of out-of-sample forecasting accuracy, our model significantly outperforms the widely used model from the Institute for Health Metrics and Evaluation (IHME), achieving 42% lower prediction error: 13.2 deaths per day average error across all U.S. states, versus 22.8 deaths per day average error for the IHME model. Our model also provides an accurate, if slightly conservative, assessment of forecasting accuracy: in the same look-ahead test, 98% of data points fell within the models 95% credible intervals. Our models projections are updated daily at https://covid-19.tacc.utexas.edu/projections/.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- It’s complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US 93%
- Predicting critical state after COVID-19 diagnosis: Model development using a large US electronic health record dataset 93%
- Continuous-Time and Dynamic Suicide Attempt Risk Prediction with Neural Ordinary Differential Equations 92%
Similar papers in this journal
Similar papers in this journal
- Early Detection of COVID-19 Outbreaks Using Human Mobility Data 94%
- A Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered Model to Predict Demand for COVID-19 Inpatient Care in a Large Healthcare System 93%
- A causal inference approach for estimating effects of non-pharmaceutical interventions during Covid-19 pandemic 93%
Similar papers in this journal
- Forecasting local surges in COVID-19 hospitalizations through adaptive decision tree classifiers 92%
- A Tutorial on Discrete Event Simulation Models in R Using a Cost-Effectiveness Analysis Example 92%
- Multilevel and Quasi Monte Carlo methods for the calculation of the Expected Value of Partial Perfect Information 90%
Similar papers in this journal
- Learning from local to global - an efficient distributed algorithm for modeling time-to-event data 93%
- A Bayesian Framework for Estimating the Risk Ratio of Hospitalization for People with Comorbidity Infected by the SARS-CoV-2 Virus 92%
- sureLDA: A Multi-Disease Automated Phenotyping Method for the Electronic Health Record 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.