Understanding Spatial Variation in COVID-19 across the United States

Desmet, Klaus; Wacziarg, Romain

doi:10.3386/w27329

Cited by 106 publications

(110 citation statements)

References 9 publications

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“…Likewise, CDC minority scores and Black/African-American individuals are correlated with reports that suggest that minority populations and People of Color are disproportionately impacted by COVID-19[13] [15]. In addition, we also show a significant potential impact of baseline public transportation use and mortality[39][40][27]…”

supporting

confidence: 74%

“…These include people older than 65 and and communities affected by racial disparities. We attempt in this paper to expand on previous studies of county level variation in COVID-19 (e.g., see [27]) by evaluating additional socio-environmental data to understand if these disparities have direct effects on COVID-19 outcomes or are indirect through additional risk factors, such as diabetes, food security, air pollution or access to health care. Likewise, no study has been carried out to determine which of these factors are most associated with COVID-19 outcomes while still controlling nonparametrically for all other factors.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Machine Learning of Factors Influencing COVID-19 Across US Counties

McCoy

Mgbara

Horvitzc

et al. 2020

Preprint

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Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the causal agent for COVID-19, is a communicable disease spread through close contact. It is known to disproportionately infect certain communities due to both biological susceptibility and inequitable exposure. In this study, we investigate the most important health, social, and environmental factors impacting both the early and later phases of COVID-19 transmission and mortality in US counties.Methods: We aggregate county-level physical and mental health, environmental pollution, access to health care, demographic characteristics, vulnerable population scores, and other epidemiological data to create a large feature set to analyze COVID-19 outcomes. Because of the high-dimensionality and multicollinearity of the data, we use ensemble machine learning and marginal prediction methods to identify the most salient factors associated with several COVID-19 outbreak measures.Findings: Our variable importance results show that measures of ethnicity, public transportation and preventable diseases are the strongest drivers for both incidence and mortality. Specifically, the CDC measures for minority populations, CDC measures for limited English, and proportion of Black/African-American individuals in a county were the most important features for COVID-19 cases at day 25 and to date. For mortality at day 100 and total to date, we find that public transportation use and proportion of Black/African-American individuals in a county are the strongest predictors. The methods predict that, keeping all other factors fixed, a 10% increase in public transportation use increases mortality at day 100 by 2012 (95% CI [1972, 2356]) and likewise a 10% increase in the proportion of Black/African-American individuals in a county increases total deaths to date by 2067 (95% CI [1189, 2654]). In terms of cases to date, ethnicity turns out to almost twice as important as the next most important factors, which are location, disease prevalence, and transit factors.Interpretations: Our findings indicate that a more focused approach should be taken when managing COVID-19, by considering features of the economy most responsible for transmission and sectors of society most vulnerable to infection and mortality. In particular, our results strongly reinforce others pointing to the disproportionate impact of COVID-19 on minority populations. They also suggests that mitigation measures, including rolling out vaccinations as they become available, will be most efficacious for the US population as a whole when, beyond healthcare workers and first responders, are focused first on the highest-risk communities.Funding: UC Berkeley, Biomedical Big Data Training Fellowship; NSF Grant 2032264 to WMG and AH.

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supporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning of Factors Influencing COVID-19 Across US Counties

McCoy

Mgbara

Horvitzc

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Desmet and Wacziarg (2020) andKnittel and Ozaltun (2020) examine spatial variation in coronavirus cases and deaths in the United States without employing the framework of an SIRD model.…”

mentioning

confidence: 99%

What Explains Temporal and Geographic Variation in the Early US Coronavirus Pandemic?

Allcott¹,

Boxell²,

Conway³

et al. 2020

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“…31 33 40 48 49 The overall quality of a local health system also matters, as high quality practices shield inpatients from the downside risks of infections, and better protect their healthcare workforce. 29 To build our Index of Vulnerability, we focus on indicators capturing the NHS workforce in each CCG, and on a set of proxies for the quality of healthcare supply. To measure NHS workforce, we measure (11) the number of nurses within general practices, GPs and hospital staff, including non-medical personnel.…”

Section: Methodsmentioning

confidence: 99%

Measuring geographical disparities in England at the time of COVID-19: results using a composite indicator of population vulnerability

et al. 2020

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ObjectivesThe growth of COVID-19 infections in England raises questions about system vulnerability. Several factors that vary across geographies, such as age, existing disease prevalence, medical resource availability and deprivation, can trigger adverse effects on the National Health System during a pandemic. In this paper, we present data on these factors and combine them to create an index to show which areas are more exposed. This technique can help policy makers to moderate the impact of similar pandemics.DesignWe combine several sources of data, which describe specific risk factors linked with the outbreak of a respiratory pathogen, that could leave local areas vulnerable to the harmful consequences of large-scale outbreaks of contagious diseases. We combine these measures to generate an index of community-level vulnerability.Setting91 Clinical Commissioning Groups (CCGs) in England.Main outcome measuresWe merge 15 measures spatially to generate an index of community-level vulnerability. These measures cover prevalence rates of high-risk diseases; proxies for the at-risk population density; availability of staff and quality of healthcare facilities.ResultsWe find that 80% of CCGs that score in the highest quartile of vulnerability are located in the North of England (24 out of 30). Here, vulnerability stems from a faster rate of population ageing and from the widespread presence of underlying at-risk diseases. These same areas, especially the North-East Coast areas of Lancashire, also appear vulnerable to adverse shocks to healthcare supply due to tighter labour markets for healthcare personnel. Importantly, our index correlates with a measure of social deprivation, indicating that these communities suffer from long-standing lack of economic opportunities and are characterised by low public and private resource endowments.ConclusionsEvidence-based policy is crucial to mitigate the health impact of pandemics such as COVID-19. While current attention focuses on curbing rates of contagion, we introduce a vulnerability index combining data that can help policy makers identify the most vulnerable communities. We find that this index is positively correlated with COVID-19 deaths and it can thus be used to guide targeted capacity building. These results suggest that a stronger focus on deprived and vulnerable communities is needed to tackle future threats from emerging and re-emerging infectious disease.

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Understanding Spatial Variation in COVID-19 across the United States

Cited by 106 publications

References 9 publications

Ensemble Machine Learning of Factors Influencing COVID-19 Across US Counties

Ensemble Machine Learning of Factors Influencing COVID-19 Across US Counties

What Explains Temporal and Geographic Variation in the Early US Coronavirus Pandemic?

Measuring geographical disparities in England at the time of COVID-19: results using a composite indicator of population vulnerability

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