Spatial variability of COVID-19 and its risk factors in Nigeria: A spatial regression method

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

“…The result of our review showed that income is employed on all state, county, city, and urban district scales. On the state scale , poverty and income were not significant factors to explain the variation in COVID-19 rate of infection and death in studies conducted in the US, France, Nigeria, and Bangladesh ( White and Hébert-Dufresne, 2020 ; Bayode et al, 2022 ; Tchicaya et al, 2021 ; Rahman et al, 2021 ). On the county scale , income just appeared in the papers conducted in the US and the results seem to be contrasting.…”

“…Transportation infrastructures mainly appeared in papers on a lower scale such as urban districts. On the state scale, the number of high-speed railways in China increased the number of cases ( Z. Hu et al, 2021 ), while Bayode et al (2022) reported no significant relationship between COVID-19 cases and international airports. On the county scale, in Zambia, the number of cases increased due to closeness to airports ( Phiri et al, 2021 ) but in the case of Germany and South Korea, the physical characteristics of transportation infrastructures were not significant factors ( Scarpone et al, 2020 ; Jo et al, 2021 ).…”

“…As population density is easier to calculate, most of them used this variable instead of other types of density. Based on our review, many studies conducted in countries such as the US ( Sarmadi et al, 2021 ; White and Hébert-Dufresne, 2020 ), Italy ( Ilardi et al, 2020 ), Iran ( Ahmadi et al, 2020 ), Bangladesh ( Sharif et al, 2021 ; Alam, 2021 ; Rahman et al, 2021 ), Oman ( Al Kindi et al, 2021 ), France ( Tchicaya et al, 2021 ), Nigeria ( Bayode et al, 2022 ) and Scotland ( Rideout et al, 2021 ) found population density as a positive and significant predictor of COVID-19 cases on state scale. However, about 40 % of studies reported that population density is not a significant factor to explain the difference in COVID-19 cases in different states ( Ramírez-Aldana et al, 2020 ; Gupta et al, 2020 ; Perone, 2021 ; Gargiulo et al, 2020 ; Basellini and Camarda, 2021 ; Pilkington et al, 2021 ; Sen-Crowe et al, 2021 ).…”

“…36 papers have included income as a factor to explain the dynamics of COVID-19 cases. As the data for this variable is available for most cities, income is the most common socio-economic factor repeated in reviewed papers ( Bayode et al, 2022 ). The result of our review showed that income is employed on all state, county, city, and urban district scales.…”

Assessment of Structural Barriers and Racial Group Disparities of COVID-19 Mortality With Spatial Analysis

“…Such spatial econometric approaches have been adopted to study spatial epidemiologic processes 39 , 40 and are increasingly common in relevant policy and public health literature, 41 , 42 , 43 including COVID-19 studies at smaller regional scales. 44 , 45 , 46 We operationalized a second-order queen contiguity spatial weight in spatial models to model the spatial interdependence; that is, we allowed an interaction between a county and its neighbors as well as the neighbors of its neighbors (eFigure 2 in the Supplement ). We excluded some variables to avoid multicollinearity issues with the SDOH indexes.…”