Paper
7 September 2023 Impact of age, race and ethnicity on the prevalence of COVID-19 in California
Junyi Li
Author Affiliations +
Proceedings Volume 12789, International Conference on Modern Medicine and Global Health (ICMMGH 2023); 127892B (2023) https://doi.org/10.1117/12.2692857
Event: International Conference on Modern Medicine and Global Health (ICMMGH 2023), 2023, Oxford, United Kingdom
Abstract
Covid-19 is a global public health disease that has significantly impacted the world from various domains. Numerous existing studies have analyzed the association of coronavirus with different industries, with the impact of individual patient characteristics on coronavirus transmission being a topic of significant interest. In this study, linear regression models were used to examine the effects of age, race and population distribution on coronavirus morbidity and mortality using data collected on coronavirus in California, USA, for November. Statistical analysis found that coronavirus transmission was significantly influenced by race, which produces consistency with some studies today. Further research is needed on specific differences in coronavirus development between individual races. In addition, the association between age and coronavirus prevalence could not be determined due to the lack of data and limitations of statistical models. Imbalances in healthcare resources, differences in vaccination rates and living environments are all considerable reasons for the association between racial factors and coronavirus transmission. Accordingly, countermeasures can be considered to help mitigate the efficiency of coronavirus transmission and its negative effects on social development.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Junyi Li "Impact of age, race and ethnicity on the prevalence of COVID-19 in California", Proc. SPIE 12789, International Conference on Modern Medicine and Global Health (ICMMGH 2023), 127892B (7 September 2023); https://doi.org/10.1117/12.2692857
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KEYWORDS
Statistical analysis

COVID 19

Linear regression

Data modeling

Error analysis

Medicine

Diseases and disorders

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