Black/African American Communities are at Highest Risk of COVID-19: Spatial Modeling of New York City ZIP Code-Level Testing Results

DiMaggio, Charles; Klein, Michael; Berry, Cherisse; Frangos, Spiros G.

doi:10.1101/2020.05.14.20101691

Cited by 20 publications

(21 citation statements)

References 21 publications

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“…These studies encouraged the formulation of environmental policies to control pollution sources, which can reduce the harmful effects of air pollutants. Studies from Li et al ( 2020 ) and DiMaggio et al ( 2020 ) showed that compared with the general population of the USA, black Americans were at apparently higher risk of COVID-19 infection and mortality nationwide. It is probably because black Americans suffer more from poverty, environmental pollution, overcrowded housing and less access to health care than do the general population of the USA.…”

Section: Discussionmentioning

confidence: 99%

Distribution of the environmental and socioeconomic risk factors on COVID-19 death rate across continental USA: a spatial nonlinear analysis

Luo

Yan

McClure

2020

Environ Sci Pollut Res

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The COVID-19 outbreak has become a global pandemic. The spatial variation in the environmental, health, socioeconomic, and demographic risk factors of COVID-19 death rate is not well understood. Global models and local linear models were used to estimate the impact of risk factors of the COVID-19, but these do not account for the nonlinear relationships between the risk factors and the COVID-19 death rate at various geographical locations. We proposed a local nonlinear nonparametric regression model named geographically weighted random forest (GW-RF) to estimate the nonlinear relationship between COVID-19 death rate and 47 risk factors derived from the US Environmental Protection Agency, National Center for Environmental Information, Centers for Disease Control and the US census. The COVID-19 data were employed to a global regression model random forest (RF) and a local model GW-RF. The adjusted R 2 of the RF is 0.69. The adjusted R 2 of the proposed GW-RF is 0.78. The result of GW-RF showed that the risk factors (i.e. going to work by walking, airborne benzene concentration, householder with a mortgage, unemployment, airborne PM 2.5 concentration and per cent of the black or African American) have a high correlation with the spatial distribution of the COVID-19 death rate, and these key factors driven from the GW-RF were mapped, which could provide useful implications for controlling the spread of the COVID-19 pandemic.

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Section: Discussionmentioning

confidence: 99%

Distribution of the environmental and socioeconomic risk factors on COVID-19 death rate across continental USA: a spatial nonlinear analysis

Luo

Yan

McClure

2020

Environ Sci Pollut Res

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“…Interestingly, the risk among Asian children living in the United States is reduced ( Uehara and Belay, 2012 ), suggesting a role for environmental factors. Similarly, it is quite possible that environmental factors impact the ethnicity-based risk in MIS-C, as Black and Hispanic populations are more likely to be exposed to SARS-CoV2 ( DiMaggio et al, 2020 ; Vahidy et al, 2020 ). This factor is especially relevant at our hospital, and other metropolitan centers, which serve patients from diverse backgrounds.…”

Section: Discussionmentioning

confidence: 99%

Mapping Systemic Inflammation and Antibody Responses in Multisystem Inflammatory Syndrome in Children (MIS-C)

Gruber

Patel

Trachman

et al. 2020

Preprint

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Initially, the global outbreak of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spared children from severe disease. However, after the initial wave of infections, clusters of a novel hyperinflammatory disease have been reported in regions with ongoing SARS-CoV-2 epidemics. While the characteristic clinical features are becoming clear, the pathophysiology remains unknown. Herein, we report on the immune profiles of eight Multisystem Inflammatory Syndrome in Children (MIS-C) cases. We document that all MIS-C patients had evidence of prior SARS-CoV-2 exposure, mounting an antibody response with normal isotype-switching and neutralization capability. We further profiled the secreted immune response by high-dimensional cytokine assays, which identified elevated signatures of inflammation (IL-18 and IL-6), lymphocytic and myeloid chemotaxis and activation (CCL3, CCL4, and CDCP1) and mucosal immune dysregulation (IL-17A, CCL20, CCL28). Mass cytometry immunophenotyping of peripheral blood revealed reductions of mDC1 and non-classical monocytes, as well as both NK- and T- lymphocytes, suggesting extravasation to affected tissues. Markers of activated myeloid function were also evident, including upregulation of ICAM1 and FcR1 in neutrophil and non-classical monocytes, well-documented markers in autoinflammation and autoimmunity that indicate enhanced antigen presentation and Fc-mediated responses. Finally, to assess the role for autoimmunity secondary to infection, we profiled the auto-antigen reactivity of MIS-C plasma, which revealed both known disease-associated autoantibodies (anti-La) and novel candidates that recognize endothelial, gastrointestinal and immune-cell antigens. All patients were treated with anti- IL6R antibody or IVIG, which led to rapid disease resolution tracking with normalization of inflammatory markers.

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“…This means that East Asians must be exhibiting significantly less susceptibility and/or transmissibility than people of other races. Only a few days back, a study has emerged [41] claiming that black people in New York City are more susceptible to infection than white people, so such racial factors might well be present here too. It had at one time been thought that the virus does not care about racial and national boundaries – that unfortunately doesn’t seem to be the case any longer.…”

Section: Implications Of the Mathematical Resultsmentioning

confidence: 99%

Retarded Logistic Equation as a Universal Dynamic Model for the Spread of COVID-19

Shayak

Sharma

2020

Preprint

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In this work we propose the retarded logistic equation as a dynamic model for the spread of COVID-19 all over the world. This equation accounts for asymptomatic transmission, pre-symptomatic or latent transmission as well as contact tracing and isolation, and leads to a transparent definition of the instantaneous reproduction number R. For different parameter values, the model equation admits different classes of solutions. These solution classes correspond to, inter alia, containment of the outbreak via public health measures, exponential growth despite public health measures, containment despite reopening and second wave following reopening. We believe that the spread of COVID in every localized area such as a city, district or county can be accounted for by one of our solution classes. In regions where R > 1 initially despite aggressive epidemic management efforts, we find that if the mitigation measures are sustained, then it is still possible for R to dip below unity when far less than the region's entire population is affected, and from that point onwards the outbreak can be driven to extinction in time. We call this phenomenon partial herd immunity. Our analysis indicates that COVID-19 is an extremely vicious and unpredictable disease which poses unique challenges for public health authorities, on account of which case races among various countries and states do not serve any purpose and present delusive appearances while ignoring significant determinants.

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Black/African American Communities are at Highest Risk of COVID-19: Spatial Modeling of New York City ZIP Code-Level Testing Results

Cited by 20 publications

References 21 publications

Distribution of the environmental and socioeconomic risk factors on COVID-19 death rate across continental USA: a spatial nonlinear analysis

Distribution of the environmental and socioeconomic risk factors on COVID-19 death rate across continental USA: a spatial nonlinear analysis

Mapping Systemic Inflammation and Antibody Responses in Multisystem Inflammatory Syndrome in Children (MIS-C)

Retarded Logistic Equation as a Universal Dynamic Model for the Spread of COVID-19

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