Clustering Analysis of US COVID-19 Rates, Vaccine Participation, and Socioeconomic Factors
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914898602098688 |
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| author | Maleki, Morteza |
| author_facet | Maleki, Morteza |
| contents | The COVID-19 pandemic has presented unprecedented challenges worldwide, with its impact varying significantly across different geographic and socioeconomic contexts. This study employs a clustering analysis to examine the diversity of responses to the pandemic within the United States, aiming to provide nuanced insights into the effectiveness of various strategies. We utilize an unsupervised machine learning approach, specifically K-Means clustering, to analyze county-level data that includes variables such as infection rates, death rates, demographic profiles, and socio-economic factors. Our analysis identifies distinct clusters of counties based on their pandemic responses and outcomes, facilitating a detailed examination of "high-performing" and "lower-performing" groups. These classifications are informed by a combination of COVID-specific datasets and broader socio-economic data, allowing for a comprehensive understanding of the factors that contribute to differing levels of pandemic impact. The findings underscore the importance of tailored public health responses that consider local conditions and capabilities. Additionally, this study introduces an innovative visualization tool that aids in hypothesis testing and further research, enhancing the ability of policymakers and public health officials to deploy more effective and targeted interventions in future health crises. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08186 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Clustering Analysis of US COVID-19 Rates, Vaccine Participation, and Socioeconomic Factors Maleki, Morteza Computational Engineering, Finance, and Science Physics and Society The COVID-19 pandemic has presented unprecedented challenges worldwide, with its impact varying significantly across different geographic and socioeconomic contexts. This study employs a clustering analysis to examine the diversity of responses to the pandemic within the United States, aiming to provide nuanced insights into the effectiveness of various strategies. We utilize an unsupervised machine learning approach, specifically K-Means clustering, to analyze county-level data that includes variables such as infection rates, death rates, demographic profiles, and socio-economic factors. Our analysis identifies distinct clusters of counties based on their pandemic responses and outcomes, facilitating a detailed examination of "high-performing" and "lower-performing" groups. These classifications are informed by a combination of COVID-specific datasets and broader socio-economic data, allowing for a comprehensive understanding of the factors that contribute to differing levels of pandemic impact. The findings underscore the importance of tailored public health responses that consider local conditions and capabilities. Additionally, this study introduces an innovative visualization tool that aids in hypothesis testing and further research, enhancing the ability of policymakers and public health officials to deploy more effective and targeted interventions in future health crises. |
| title | Clustering Analysis of US COVID-19 Rates, Vaccine Participation, and Socioeconomic Factors |
| topic | Computational Engineering, Finance, and Science Physics and Society |
| url | https://arxiv.org/abs/2404.08186 |