The Geography of COVID-19 growth in the US: Counties and Metropolitan Areas

Wheaton, William C.; Thompson, Anne K.

doi:10.2139/ssrn.3570540

Cited by 34 publications

(34 citation statements)

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“…They conclude that "COVID-19 death rates are lower in dense counties and higher in less dense counties (page 12). Wheaton and Thompson's (2020) findings reveal that density and the total number of infections are inversely related, but that density has no significant effect when the infection rate serves as the dependent variable.…”

Section: Figure 1 Cumulative Average Number Of Covid19 Deaths In Thementioning

confidence: 83%

“…A number of academic studies have studied the impact of demographic and socioeconomic forces on the incidence of COVID-19. These studies have focused attention on counties and metropolitan statistical areas (Hamidi, Sabouri, and Ewing, 2020;Liu et al, 2020;Wheaton and Thompson, 2020). No academic research, however, has examined or identified the variables that explain state-level differences in COVID-19 death rates.…”

Section: Introductionmentioning

confidence: 99%

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A Model to Explain Statewide Differences in COVID-19 Death Rates

Doti

2020

SSRN Journal

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COVID-19 death rates per 100,000 vary widely across the nation. As of September 1, 2020, they range from a low of 4 in Hawaii to a high of 179 in New Jersey. Although academic research has been conducted at the county and metropolitan levels, no research has rigorously examined or identified the demographic and socioeconomic forces that explain state-level differences. This study presents an empirical model and the results of regression tests that help identify these forces and shed light on the role they play in explaining COVID-19 deaths. A stepwise regression model we tested exhibits a high degree of explanatory power. It suggests that two measures of density explain most of the state-level differences. Less significant variables included the poverty rate and racial/ethnic differences. We also found that variables relating to health, air travel, and government mandates were not significant in explaining COVID-19 deaths at the state level. This study also examines the elasticities of those variables we found significant. We measured both average and constant elasticities to determine the relationship between changes in COVID-19 deaths and percentage changes in the relevant explanatory variables. In an analysis of residuals, we found that the unexplained variation was found to be related mainly to factors site-specific to individual states. Unlike the empirical results of several academic studies, our model found that the density of a state is the most important factor explaining COVID-19 deaths. The role that density plays in the transmission of COVID-19 has important policy implications in responding to the challenges posed by the coronavirus and future pandemics.

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Section: Figure 1 Cumulative Average Number Of Covid19 Deaths In Thementioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

A Model to Explain Statewide Differences in COVID-19 Death Rates

Doti

2020

SSRN Journal

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“…They conclude that "COVID-19 death rates are lower in dense counties and higher in less dense counties" (p. 12). Wheaton and Thompson's (2020) findings reveal that density and the total number of infections are inversely related, but that density has no significant effect when the infection rate serves as the dependent variable. These findings run counter to the commonly accepted view that greater social interaction leads to higher COVID-19 infections and deaths.…”

Section: Introductionmentioning

confidence: 83%

“…A number of academic studies have studied the impact of demographic and socioeconomic forces on the incidence of COVID-19. These studies have focused attention on counties and metropolitan statistical areas (Hamidi et al 2020;Liu et al 2020;Wheaton and Thompson 2020). At the state level, the print and electronic media have extensively reported on differences in COVID-19 infection and deaths (Olsen, Washington Post, 2019;Rosenthal, New York Times, 2020;Tavernise and Mervosh, New York Times, 2020), but these reports are largely anecdotal and lacking in academic rigor.…”

Section: Introductionmentioning

confidence: 99%

Examining the impact of socioeconomic variables on COVID-19 death rates at the state level

Doti

2021

J Bioecon

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This study uses a step-wise regression model to identify the socioeconomic variables most significant in explaining COVID-19 death rates on a state-level basis. The regression tests cover the 1/1/2020 to 12/1/2020 period as well as the first and second halves of 2020. This study also uses the Oxford stringency index to measure more precisely the efficacy of governmental mandates at the state level. The results in this study rigorously showed that while the density variables were the most significant explanatory variables during the first half of the year, their significance fell during the second half. Use of the Oxford stringency index revealed that more stringent mandates led to significant reductions in COVID-19 death rates, especially during the second half of the year. The study’s findings also reveal that a higher poverty rate in a state is significantly associated with higher COVID-19 death rates during all three periods tested.

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“…Furthermore, higher consumption is associated with higher income levels, lower poverty, and higher employment rates. Therefore, income and employment-related activities trigger frequent travel and physical contact with people, increasing the risk of transmission of COVID-19 [42]. Another explanation behind this could be that low-income people might have less access to testing facilities, resulting in the underreporting of the positive COVID-19 cases in low-income areas [11,43].…”

Section: Discussionmentioning

confidence: 99%

Identification of risk factors contributing to COVID-19 incidence rates in Bangladesh: A GIS-based spatial modeling approach

Rahman¹,

Zafri²,

Ashik³

et al. 2021

Heliyon

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Background: COVID-19 pandemic outbreak is an unprecedented shock throughout the world, which has generated a massive social, human, and economic crisis. Identification of risk factors is crucial to prevent the COVID-19 spread by taking appropriate countermeasures effectively. Therefore, this study aimed to identify the potential risk factors contributing to the COVID-19 incidence rates at the district-level in Bangladesh. Method: Spatial regression methods were applied in this study to fulfill the aim. Data related to 28 demographic, economic, built environment, health, and facilities related factors were collected from secondary sources and analyzed to explain the spatial variability of this disease incidence. Three global (ordinary least squares (OLS), spatial lag model (SLM), and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) regression models were developed in this study. Results: The results of the models identified four factors: percentage of the urban population, monthly consumption, number of health workers, and distance from the capital city, as significant risk factors affecting the COVID-19 incidence rates in Bangladesh. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh, with an R 2 value of 78.6%. Conclusion: Findings and discussions from this research offer a better insight into the COVID-19 situation, which helped discuss policy implications to negotiate the future epidemic crisis. The primary policy response would be to decentralize the urban population and economic activities from and around the capital city, Dhaka, to create self-sufficient regions throughout the country, especially in the northwestern region.

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The Geography of COVID-19 growth in the US: Counties and Metropolitan Areas

Cited by 34 publications

References 0 publications

A Model to Explain Statewide Differences in COVID-19 Death Rates

A Model to Explain Statewide Differences in COVID-19 Death Rates

Examining the impact of socioeconomic variables on COVID-19 death rates at the state level

Identification of risk factors contributing to COVID-19 incidence rates in Bangladesh: A GIS-based spatial modeling approach

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