tetano
Editor, Senior Moderator
PLoS One
. 2024 Jun 6;19(6):e0302324.
doi: 10.1371/journal.pone.0302324. eCollection 2024. Two-part predictive modeling for COVID-19 cases and deaths in the U.S
Teresa-Thuong Le[SUP] 1 [/SUP], Xiyue Liao[SUP] 2 [/SUP]
Affiliations
COVID-19 prediction has been essential in the aid of prevention and control of the disease. The motivation of this case study is to develop predictive models for COVID-19 cases and deaths based on a cross-sectional data set with a total of 28,955 observations and 18 variables, which is compiled from 5 data sources from Kaggle. A two-part modeling framework, in which the first part is a logistic classifier and the second part includes machine learning or statistical smoothing methods, is introduced to model the highly skewed distribution of COVID-19 cases and deaths. We also aim to understand what factors are most relevant to COVID-19's occurrence and fatality. Evaluation criteria such as root mean squared error (RMSE) and mean absolute error (MAE) are used. We find that the two-part XGBoost model perform best with predicting the entire distribution of COVID-19 cases and deaths. The most important factors relevant to either COVID-19 cases or deaths include population and the rate of primary care physicians.
. 2024 Jun 6;19(6):e0302324.
doi: 10.1371/journal.pone.0302324. eCollection 2024. Two-part predictive modeling for COVID-19 cases and deaths in the U.S
Teresa-Thuong Le[SUP] 1 [/SUP], Xiyue Liao[SUP] 2 [/SUP]
Affiliations
- PMID: 38843223
- DOI: 10.1371/journal.pone.0302324
COVID-19 prediction has been essential in the aid of prevention and control of the disease. The motivation of this case study is to develop predictive models for COVID-19 cases and deaths based on a cross-sectional data set with a total of 28,955 observations and 18 variables, which is compiled from 5 data sources from Kaggle. A two-part modeling framework, in which the first part is a logistic classifier and the second part includes machine learning or statistical smoothing methods, is introduced to model the highly skewed distribution of COVID-19 cases and deaths. We also aim to understand what factors are most relevant to COVID-19's occurrence and fatality. Evaluation criteria such as root mean squared error (RMSE) and mean absolute error (MAE) are used. We find that the two-part XGBoost model perform best with predicting the entire distribution of COVID-19 cases and deaths. The most important factors relevant to either COVID-19 cases or deaths include population and the rate of primary care physicians.