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Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study’s findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19. 

The U.S. has merely 4% of the world population, but contains 25% of the world’s COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts. 

Abstract Vertical records of ocean-bottom seismographs (OBSs) are usually noisy at low frequencies, and one important noise source is the varying ocean-bottom pressure that results from ocean-surface water waves. The relation between the ocean-bottom pressure and the vertical seafloor motion, called the compliance pressure transfer function (PTF), can be derived using background seismic data. During an earthquake, earthquake signals also generate ocean-bottom pressure fluctuations, and the relation between the ocean-bottom pressure and the vertical seafloor motion is named the seismic PTF in this article. Conventionally, we use the whole pressure records and the compliance PTF to remove the compliance noise; the earthquake-induced pressure and the seismic PTF are ignored, which may distort the original signals. In this article, we analyze the data from 24 OBSs with water depth ranging from 107 to 4462 m. We find that for most stations, the investigated frequency range (0.01–0.2 Hz) can be divided into four bands depending on the water depth. In band (I) of lowest frequencies (<0.11, <0.05, and <0.02  Hz for water depth of 107, 1109, and 2650 m, respectively), the vertical seafloor acceleration is composed mostly of pressure compliance noise, which can be removed using the compliance PTF. The compliance PTF is much smaller than the seismic PTF, so distortion of earthquake signals is negligible. In band (II) of higher frequencies (0.11–0.20, 0.05–0.11, and 0.02–0.05 Hz for water depth of 107, 1109, and 2650 m, respectively), the vertical acceleration and ocean-bottom pressure are largely uncorrelated. In bands (III) and (IV) of even higher frequencies (>0.11 and >0.08  Hz for water depth of 1109 and 2650 m, respectively), the compliance noise is negligible, and the ocean-bottom pressure is mostly caused by the seafloor motion. Thus, the compliance can be safely ignored in frequency band (I).