A comparison study of COVID-19 outbreaks in the United States between states with Republican and Democratic Governors

Authors

  • Wen Tang Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
  • Shuqi Wang Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
  • Liyan Xiong Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
  • Mengyu Fang Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
  • Chi-yang Chiu Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
  • Christopher Loffredo Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
  • Ruzong Fan Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA

DOI:

https://doi.org/10.3396/ijic.v17.20940

Keywords:

COVID-19, test positivity rate, longitudinal study, mortality rate, recovery rate, United States

Abstract

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.

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References

World Health Organization (WHO). Coronavirus disease 2019 (COVID-19) situation reports. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/.

Center for Disease Control and Prevention (CDC). Coronavirus disease 2019 (COVID-19). Available from: https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html.

Center for Disease Control and Prevention (CDC). COVID-19 forecasts: cumulative deaths. Available from: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html.

Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet 2020; 20: 533–534. doi: 10.1016/S1473-3099(20)30120-1

The Johns Hopkins University. Csse covid 19 daily reports us. Available from: https://github.com/TWtangtang/COVID-19/tree/master/csse covid 19 data.

Galva JE, Atchison C, Levey S. Public health strategy and the police powers of the state. Public Health Rep 2005; 120 (Suppl 1): 20–7. doi: 10.1177/00333549051200S106

The United States Census Bureau. Available from: https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-total.html.

Milne GJ, Halder N, Kelso JK. The cost effectiveness of pandemic influenza interventions: a pandemic severity based analysis. PLoS One 2013; 8 (4): e61504. doi: 10.1371/journal.pone.0061504

Pasquini-Descomps H, Brender N, Maradan D. Value for moneyin H1N1 influenza: a systematic review of the cost-effectiveness of pandemic interventions. Value Health 2017; 20: 819–827. doi: 10.1016/j.jval.2016.05.005

Smith MJ, Silva DS. Ethics for pandemics beyond influenza: Ebola, drug resistant tuberculosis, and anticipating future ethical challenges in pandemic preparedness and response. Monash Bioeth Rev 2015; 33: 130–147. doi: 10.1007/s40592-015-0038-7

Joo H, Miller GF, Gregory Sunshine G, Gakh M, Pike J, Havers FP. Decline in COVID-19 hospitalization growth rates associated with statewide mask mandates – 10 states, March–October 2020. Morb Mortal Wkly Rep 2021; 70: 212–216. doi: 10.15585/mmwr.mm7006e2

Crane MA, Shermock KM, Omer SB, Romley JA. Change in reported adherence to non-pharmaceutical interventions during the COVID-19 pandemic, April-November 2020. J Am Med Assoc 2020; 325: 883–5. doi: 10.1001/jama.2021.0286

Anderson RM, May RM. Infectious diseases of humans. Oxford: Oxford University Press; 1992.

Bjornstad ON. Epidemics: models and data using R. Nature Switzerland: Springer; 2018.

Earn DJD. A light introduction to modelling recurrent epidemics. Lect Notes Math Epidemiol 2008; 1945: 3–18. doi: 10.1007/978-3-540-78911-6_1

Hethcote HW. The mathematics of infectious diseases. SIAM Rev 2000; 42: 599–653. doi: 10.1137/S0036144500371907

Keeling MJ, Rohani P. Modeling infectious diseases in humans and animals. Princeton, NJ: Princeton University Press; 2008.

Kissler SM, Tedijanto C, Goldstein E, Grad YH, Lipsitch M. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science 2020; 368 (6493): 860–868. doi: 10.1126/science.abb5793

Miller JC. Mathematical models of SIR disease spread with combined non-sexual and sexual transmission routes. Infect Dis Model 2017; 2(1): 35–55. doi: 10.1016/j.idm.2016.12.003

Osemwinyen AC, Diakhaby A. Mathematical modelling of the transmission dynamics of Ebola virus. Appl Comput Math 2015; 4(4): 313–320. doi: 10.11648/j.acm.20150404.19

Plank M, Binny RN, Hendy SC, Lustig A, James A, Steyn N. A stochastic model for COVID-19 spread and the effects of alert level 4 in Aotearoa New Zealand. 2020. doi: 10.1101/2020.04.08.20058743

Wang SQ, Tang W, Xiong LY, Fang MY, Zhang BS, Chiu CY, et al. Mathematical modeling of transmission dynamics of COVID-19. Big Data Inf Anal 2021; 6:12–25. doi: 10.3934/bdia.2021002

Neupane D, Rai J, Chaulagain S, Jha N, Sah A, Bhuju DR. Role of academic institutions during the COVID-19 pandemic. Int J Infect Control 2021. doi: 10.3396/ijic.v16i4.024.20

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Published

2021-09-20

How to Cite

Tang, W., Wang, S., Xiong, L., Fang, M., Chiu, C.- yang, Loffredo, C., & Fan, R. (2021). A comparison study of COVID-19 outbreaks in the United States between states with Republican and Democratic Governors. International Journal of Infection Control, 17(1). https://doi.org/10.3396/ijic.v17.20940

Issue

Vol. 17 (2021)

Section

Original Articles

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