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基于机器学习的美国县疫苗接种率预测模型。

Predictive Modeling of Vaccination Uptake in US Counties: A Machine Learning-Based Approach.

机构信息

School of Kinesiology, University of British Columbia, Vancouver, BC, Canada.

Faculty of Science, University of British Columbia, Vancouver, BC, Canada.

出版信息

J Med Internet Res. 2021 Nov 25;23(11):e33231. doi: 10.2196/33231.

Abstract

BACKGROUND

Although the COVID-19 pandemic has left an unprecedented impact worldwide, countries such as the United States have reported the most substantial incidence of COVID-19 cases worldwide. Within the United States, various sociodemographic factors have played a role in the creation of regional disparities. Regional disparities have resulted in the unequal spread of disease between US counties, underscoring the need for efficient and accurate predictive modeling strategies to inform public health officials and reduce the burden on health care systems. Furthermore, despite the widespread accessibility of COVID-19 vaccines across the United States, vaccination rates have become stagnant, necessitating predictive modeling to identify important factors impacting vaccination uptake.

OBJECTIVE

This study aims to determine the association between sociodemographic factors and vaccine uptake across counties in the United States.

METHODS

Sociodemographic data on fully vaccinated and unvaccinated individuals were sourced from several online databases such as the US Centers for Disease Control and Prevention and the US Census Bureau COVID-19 Site. Machine learning analysis was performed using XGBoost and sociodemographic data.

RESULTS

Our model predicted COVID-19 vaccination uptake across US counties with 62% accuracy. In addition, it identified location, education, ethnicity, income, and household access to the internet as the most critical sociodemographic features in predicting vaccination uptake in US counties. Lastly, the model produced a choropleth demonstrating areas of low and high vaccination rates, which can be used by health care authorities in future pandemics to visualize and prioritize areas of low vaccination and design targeted vaccination campaigns.

CONCLUSIONS

Our study reveals that sociodemographic characteristics are predictors of vaccine uptake rates across counties in the United States and, if leveraged appropriately, can assist policy makers and public health officials to understand vaccine uptake rates and craft policies to improve them.

摘要

背景

尽管 COVID-19 大流行在全球造成了前所未有的影响,但美国等国家报告的 COVID-19 病例数量居全球之首。在美国,各种社会人口因素在造成地区差异方面发挥了作用。地区差异导致疾病在美国各县之间的传播不均衡,这凸显了需要有效的、准确的预测建模策略,为公共卫生官员提供信息,并减轻医疗保健系统的负担。此外,尽管 COVID-19 疫苗在美国广泛普及,但疫苗接种率已趋于停滞,这需要进行预测建模以确定影响疫苗接种率的重要因素。

目的

本研究旨在确定美国各县的社会人口因素与疫苗接种率之间的关联。

方法

从美国疾病控制与预防中心和美国人口普查局 COVID-19 网站等多个在线数据库中获取完全接种和未接种疫苗者的社会人口数据。使用 XGBoost 对社会人口数据进行机器学习分析。

结果

我们的模型预测了美国各县的 COVID-19 疫苗接种率,准确率为 62%。此外,它还确定了位置、教育程度、族裔、收入和家庭上网情况是预测美国各县疫苗接种率的最关键社会人口特征。最后,该模型生成了一张色标图,显示了低疫苗接种率和高疫苗接种率地区,可以为卫生保健当局在未来的大流行中用于可视化和优先考虑低疫苗接种地区,并设计有针对性的疫苗接种活动。

结论

本研究表明,社会人口特征是美国各县疫苗接种率的预测因素,如果加以适当利用,可以帮助政策制定者和公共卫生官员了解疫苗接种率,并制定改善疫苗接种率的政策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67ed/8623305/1945b1c53ae8/jmir_v23i11e33231_fig1.jpg

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