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The coronavirus disease 2019 (COVID-19) has caused devastating public health, economic, political, and societal crises. We performed a comparison study of COVID-19 outbreaks in states with Republican governors versus states with Democratic governors in the United States between April 2020 and February 2021. This research study shows that 1) states with Democratic governors had tested more people for COVID-19 and have higher testing rates than those with Republican governors; 2) states with Democratic governors had more confirmed cases for COVID-19 from April 12 until the end of July 2020, as well as from early December 2020 to February 22 2021, and had higher test positivity rates from April 12 until late June 2020, and the states with Republican governors had more confirmed cases from August to early December 2020 and had higher test positivity rates since late June 2020; 3) states with Democratic governors had more deaths for COVID-19 and higher mortality rates than those with Republican governors; 4) more people recovered in states with Democratic governors until early July 2020, while the recovery rate of states with Republican governors is similar to that of states with Democratic governors in May 2020 and higher than that of states with Democratic governors in April 2020 and between June 2020 to February 22 2021. We conclude that our data suggest that states with Republican governors controlled COVID-19 better as they had lower mortality rates and similar or higher recovery rates. States with Democratic governors first had higher test positivity rates until late June 2020 but had lower test positivity rates after July 2020. As of February 2021, the pandemic was still spreading as the daily numbers of confirmed cases and deaths were still high, although the test positivity and mortality rates started to stabilize in spring 2021. This study provides a direct description for the status and performance of handling COVID-19 in the states with Republican governors versus states with Democratic governors, and provides insights for future research, policy making, resource distribution, and administration. 

Abstract To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell‐type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell‐type‐specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular expression fractions for bulk‐profiled samples by using reference single‐cell (sc) RNA‐sequencing (RNA‐seq) reference data. In benchmark evaluations of estimating cellular expression fractions, the mixed‐effect models provide similar results as an elegant machine learning algorithm named cell‐type identification by estimating relative subsets of RNA transcripts (CIBERSORTx), which is a well‐known and reliable procedure to reconstruct cell‐type abundances and cell‐type‐specific gene expression profiles. In real data analysis, the mixed‐effect models outperform or perform similarly as CIBERSORTx. The mixed models perform better than the fixed models in both benchmark evaluations and data analysis. In simulation studies, we show that if the heterogeneity exists in scRNA‐seq data, it is better to use mixed models with heterogeneous mean and variance–covariance. As a byproduct, the mixed models provide fractions of covariance between subject‐specific gene expression and cell types to measure their correlations. The proposed mixed models provide a complementary tool to dissect bulk tissues using scRNA‐seq data.