Determining the spatial effects of COVID-19 using the spatial panel data model

Guliyev, Hasraddin

doi:10.1016/j.spasta.2020.100443

Cited by 140 publications

(128 citation statements)

References 9 publications

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“…With the increased availability of health care data online and the development of spatial analysis techniques, multiple analyses by the GIS tool (Guliyev 2020;Rosenkrantz et al 2020) found that the distribution of COVID-19 cases (Desjardins et al 2020;Shim et al 2020;Lau et al 2020) and its risk factors (Mollalo et al 2020) exhibits patterns of spatial heterogeneity. A study by Lau et al (2020) showed that the number of flight routes was a highly relevant factor of the COVID-19 spread.…”

mentioning

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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mentioning

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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“…For a discussion of these two models and the restrictions that lead to OLS, SLX, and SEM see [26], [27], as well as [28]. Note, other research using these spatial econometric models for investigating the virus, but not social distance, has found significant spatial influences on the disease [29,30].…”

Section: Modelmentioning

confidence: 99%

Demographic, jurisdictional, and spatial effects on social distancing in the United States during the COVID-19 pandemic

et al. 2020

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Social distancing, a non-pharmaceutical tactic aimed at reducing the spread of COVID-19, can arise because individuals voluntarily distance from others to avoid contracting the disease. Alternatively, it can arise because of jurisdictional restrictions imposed by local authorities. We run reduced form models of social distancing as a function of county-level exogenous demographic variables and jurisdictional fixed effects for 49 states to assess the relative contributions of demographic and jurisdictional effects in explaining social distancing behavior. To allow for possible spatial aspects of a contagious disease, we also model the spillovers associated with demographic variables in surrounding counties as well as allow for disturbances that depend upon those in surrounding counties. We run our models weekly and examine the evolution of the estimated coefficients over time since the onset of the COVID-19 pandemic in the United States. These estimated coefficients express the revealed preferences of individuals who were able to and chose to stay at home to avoid the disease. Stay-at-home behavior measured using cell phone tracking data exhibits considerable cross-sectional variation, increasing over nine-fold from the end of January 2020 to the end of March 2020, and then decreasing by about 50% through mid-June 2020. Our estimation results show that demographic exogenous variables explain substantially more of this variation than predictions from jurisdictional fixed effects. Moreover, the explanations from demographic exogenous variables and jurisdictional fixed effects show an evolving correlation over the sample period, initially partially offsetting, and eventually reinforcing each other. Furthermore, the predicted social distance from demographic exogenous variables shows substantial spatial autoregressive dependence, indicating clustering in social distancing behavior. The increased variance of stay-at-home behavior coupled with the high level of spatial dependence can result in relatively intense hotspots and coldspots of social distance, which has implications for disease spread and mitigation.

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“…The preponderance of these studies had identified a number of factors as possible driving forces behind the deadly virus. These include but not limited to: respiratory syndrome (Al-Raddadi et al 2020 ); governance, technology, and citizen behavior (Shaw et al 2020 ); socio-economic impacts (Nicola et al 2020 ); temperature (Briz-Redón and Serrano-Aroca 2020 ); spatial variation (Guliyev 2020 ); climatic factors (Altamimi and Ahmed 2020 ; Tosepu et al 2020 ); prevalence and control measures (Ceylan 2020 ; Zhao et al 2020 ) and mortality rates (Ferdinand and Nasser 2020 ; Wang et al 2020 ); social and political economy (Daniel 2020 ; Greer et al 2020 ; Saleh 2020 ); and lockdown impacts (Ajide et al 2020 ; Ibrahim et al 2020 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the Political Economy and Multidimensional Factors of COVID-19 Cases in Nigeria

et al. 2020

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Determining the spatial effects of COVID-19 using the spatial panel data model

Cited by 140 publications

References 9 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

Demographic, jurisdictional, and spatial effects on social distancing in the United States during the COVID-19 pandemic

Modeling the Political Economy and Multidimensional Factors of COVID-19 Cases in Nigeria

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