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

Doti, James L.

doi:10.1007/s10818-021-09309-9

Cited by 8 publications

(11 citation statements)

References 21 publications

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“…Many models of disease spread incorporate socioeconomic and demographic variables such as median household income; proportion of the population over 65; proportion of black or Hispanic residents; and the political leanings of county residents [ 26 , 34 , 77 ]. Socioeconomic variables are latent variables representing factors that may cause the disease to spread faster or slower in a given county.…”

Section: Methodsmentioning

confidence: 99%

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

Lucas

Vahedi

Karimzadeh

2022

Int J Data Sci Anal

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With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeling, compartmental models, are based on the assumption of spatiotemporal homogeneity of the spread of the virus, which may cause forecasting to underperform, especially at high spatial resolutions. In this paper, we approach the forecasting task with an alternative technique—spatiotemporal machine learning. We present COVID-LSTM, a data-driven model based on a long short-term memory deep learning architecture for forecasting COVID-19 incidence at the county level in the USA. We use the weekly number of new positive cases as temporal input, and hand-engineered spatial features from Facebook movement and connectedness datasets to capture the spread of the disease in time and space. COVID-LSTM outperforms the COVID-19 Forecast Hub’s Ensemble model (COVIDhub-ensemble) on our 17-week evaluation period, making it the first model to be more accurate than the COVIDhub-ensemble over one or more forecast periods. Over the 4-week forecast horizon, our model is on average 50 cases per county more accurate than the COVIDhub-ensemble. We highlight that the underutilization of data-driven forecasting of disease spread prior to COVID-19 is likely due to the lack of sufficient data available for previous diseases, in addition to the recent advances in machine learning methods for spatiotemporal forecasting. We discuss the impediments to the wider uptake of data-driven forecasting, and whether it is likely that more deep learning-based models will be used in the future.

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Section: Methodsmentioning

confidence: 99%

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

Lucas

Vahedi

Karimzadeh

2022

Int J Data Sci Anal

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“…Many models of disease spread incorporate socioeconomic and demographic variables such as median household income; proportion of the population over 65; proportion of black or Hispanic residents; and the political leanings of county residents [32,24,75]. Socioeconomic variables are latent variables representing factors that may cause the disease to spread faster or slower in a given county.…”

Section: Socioeconomic Variablesmentioning

confidence: 99%

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

Lucas¹,

Vahedi²,

Karimzadeh³

2021

Preprint

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With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeling, compartmental models, are based on the assumption of spatiotemporal homogeneity of the spread of the virus, which may cause forecasting to underperform, especially at high spatial resolutions. In this paper we approach the forecasting task with an alternative technique-spatiotemporal machine learning. We present COVID-LSTM, a data-driven model based on a Long Short-term Memory deep learning architecture for forecasting COVID-19 incidence at the county-level in the US. We use the weekly number of new positive cases as temporal input, and hand-engineered spatial features from Facebook movement and connectedness datasets to capture the spread of the disease in time and space. COVID-LSTM outperforms the COVID-19 ForecastHub's Ensemble model (COVIDhub-ensemble) on our 17-week evaluation period, making it the first model to be more accurate than the COVIDhub-ensemble over one or more forecast periods. Over the 4-week forecast horizon, our model is on average 50 cases per county more accurate

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“…One reason for the choice of state comparisons is that the U.S. CDC (Centers for Disease Control and Prevention) does not generate county‐level estimates of excess mortality. 5 We have found only two spatial analyses of U.S. COVID‐19‐related mortality at the state level, IHME ( 2021 ) and Doti ( 2021 ), 6 both modeling COVID‐19 deaths. Thus, our paper is the first state‐level spatial analysis of excess mortality, and the first state state‐level spatial analysis of mortality that explicitly includes political variables.…”

Section: Introductionmentioning

confidence: 99%

“…(2021), Knittel and Ozaltun (2020), McLaren (2020), Karmakar et al . (2021) at the county level, and Doti (2021) at the state level, who also includes state interventions on social distancing. Considering also the role of partisanship and COVID‐19 infections and deaths are Liao and De Maio (2021) and Desmet and Wacziarg (2021).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Excess Mortality Versus COVID‐19 Death Rates: A Spatial Analysis of Socioeconomic Disparities and Political Allegiance Across U.S. States

Aron

Muellbauer

2022

Review of Income and Wealth

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Excess mortality is a more robust measure than the counts of COVID-19 deaths typically used in epidemiological and spatial studies. Measurement issues around excess mortality, considering data quality and comparability both internationally and within the US, are surveyed. This paper is the first state-level spatial analysis of cumulative excess mortality for the US in the first full year of the pandemic. There is strong evidence that states with higher Democrat vote shares experienced lower excess mortality (consistent with county-level studies of COVID-19 deaths and partisanship). Important demographic and socio-economic controls from a broad set tested were racial composition, age structure, population density, poverty, income, temperature, timing of arrival of the pandemic and political allegiance. Interaction effects suggest the Democrat vote share effect of reducing mortality was even greater in states where the pandemic arrived early. Omitting political allegiance leads to a significant underestimation of the mortality disparities for minority populations.

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

Cited by 8 publications

References 21 publications

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

Excess Mortality Versus COVID‐19 Death Rates: A Spatial Analysis of Socioeconomic Disparities and Political Allegiance Across U.S. States

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