Department of Mathematics, Engineering and Computer Science, Chemeketa Community College, Salem, OR, United States.
Evolution Equations Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Front Public Health. 2020 Nov 12;8:579190. doi: 10.3389/fpubh.2020.579190. eCollection 2020.
On March 13, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus SARS-CoV2 a pandemic. Since then the virus has infected over 9.1 million individuals and resulted in over 470,000 deaths worldwide (as of June 24, 2020). Here, we discuss the spatial correlation between county population health rankings and the incidence of COVID-19 cases and COVID-19 related deaths in the United States. We analyzed the spread of the disease based on multiple variables at the county level, using publicly available data on the numbers of confirmed cases and deaths, intensive care unit beds and socio-demographic, and healthcare resources in the U.S. Our results indicate substantial geographical variations in the distribution of COVID-19 cases and deaths across the US counties. There was significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran = 0.174 and 0.264, < 0.0001). A similar result was found for the global spatial correlation between the percentage of Black American and deaths due to COVID-19 at the county level in the U.S. (Moran = 0.264, < 0.0001). There was no significant spatial correlation between the Hispanic population and COVID-19 cases and deaths; however, a higher percentage of non-Hispanic white was significantly negatively spatially correlated with cases (Moran = -0.203, < 0.0001) and deaths (Moran = -0.137, < 0.0001) from the disease. This study showed significant but weak spatial autocorrelation between the number of intensive care unit beds and COVID-19 cases (Moran = 0.08, < 0.0001) and deaths (Moran = 0.15, < 0.0001), respectively. These findings provide more detail into the interplay between the infectious disease and healthcare-related characteristics of the population. Only by understanding these relationships will it be possible to mitigate the rate of spread and severity of the disease.
2020 年 3 月 13 日,世界卫生组织(WHO)宣布由新型冠状病毒 SARS-CoV2 引起的 2019 年冠状病毒病(COVID-19)为大流行。自此,该病毒已感染超过 910 万人,并导致全球超过 47 万人死亡(截至 2020 年 6 月 24 日)。在这里,我们讨论了美国县人口健康排名与 COVID-19 病例和 COVID-19 相关死亡之间的空间相关性。我们基于县级的多个变量分析了疾病的传播,使用了美国确诊病例和死亡人数、重症监护病床以及社会人口和医疗保健资源的公开数据。我们的结果表明,美国各县 COVID-19 病例和死亡的分布存在显著的地域差异。美国各县非裔美国人比例与 COVID-19 病例之间存在显著的正全球空间相关性(Moran = 0.174 和 0.264,< 0.0001)。在美国县一级,非裔美国人比例与 COVID-19 死亡之间也存在类似的全球空间相关性(Moran = 0.264,< 0.0001)。西班牙裔人口与 COVID-19 病例和死亡之间没有显著的空间相关性;然而,较高的非西班牙裔白人比例与 COVID-19 病例(Moran = -0.203,< 0.0001)和死亡(Moran = -0.137,< 0.0001)显著负相关。本研究表明,重症监护病床数量与 COVID-19 病例(Moran = 0.08,< 0.0001)和死亡(Moran = 0.15,< 0.0001)之间存在显著但较弱的空间自相关。这些发现为传染病与人口的医疗保健相关特征之间的相互作用提供了更详细的信息。只有了解这些关系,才能降低疾病的传播速度和严重程度。