Spatiotemporal Analysis of Covid-19 in Turkey

Aral, Neşe; Bakır, Hasan

doi:10.1016/j.scs.2021.103421

Cited by 38 publications

(26 citation statements)

References 80 publications

(147 reference statements)

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“…As for air quality parameters, while some of the cities showed a positive relationship between the spread of COVID-19 and air pollutants, some showed reverse correlations. Aral and Bakır ( 2022 ) conducted a study in which they proved that the distribution of COVID-19 cases in Turkey is based on spatial changes and spatiotemporal aggregation; that is, they suggested that the spread of COVID-19 cases is heterogeneous among Turkish cities. The unsteady distribution can be attributed to the socioeconomic and sociocultural status of the people living in these cities.…”

Section: Resultsmentioning

confidence: 99%

“…The unsteady distribution can be attributed to the socioeconomic and sociocultural status of the people living in these cities. The demographic and geographical differences of the cities can be considered as factors that can be effective in this heterogeneousness (Aral and Bakır 2022 ). Another study performed by Islam et al ( 2021 ), who investigated the relationship between meteorological parameters and COVID-19-confirmed cases from 206 countries/regions, suggested that there was no association between cases and parameters including 7-day lagged temperature, relative humidity, UV index, but there were observed a positive association with 14-day-lagged temperature and a negative association with 14-day-lagged wind speed.…”

Section: Resultsmentioning

confidence: 99%

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Transmission of COVID-19 pandemic (Turkey) associated with short-term exposure of air quality and climatological parameters

Akan

2022

Environ Sci Pollut Res

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The study aims to investigate associations between air pollution, climate parameters, and the diffusion of COVID-19-confirmed cases in Turkey using Spearman's correlation test as an empirical methodology by Statgraphics Centurion XVI (version 16.1) and to determine the risk factors accelerating the spread of SARS-CoV-2 virus. The present study demonstrates the strong impacts of air pollutants and weather conditions on the transmission of COVID-19 morbidity. Particularly, O 3 and PM 10 from air quality parameters exhibited the strongest correlation with the number of daily cases in Kütahya (rs = -0.62; p < 0.05) and Sivas (rs = -0.62; p < 0.05) provinces, respectively. In meteorological parameters, rainfall showed the highest impact (rs = 0.76; p < 0.05) on the number of daily COVID-19 cases in Denizli distinct. Moreover, this study suggested that the diffusion of the novel coronavirus SARS-CoV-2 in regions with high levels of air pollution and low wind speed is dominant. To prevent the negative effects of the future pandemic crisis on public health and economic systems, manifold implications to encourage strategies to reduce air pollution in the polluted region such as being prevalent the usage of renewable energy technologies in particular electricity generation and sustainable policies such as improving the health system should be implemented by decision-makers. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-18403-4.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Transmission of COVID-19 pandemic (Turkey) associated with short-term exposure of air quality and climatological parameters

Akan

2022

Environ Sci Pollut Res

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“…According to the analysis, seven clusters were detected based on the census data and the spatial distribution of the people were explored using unsupervised neural network method. A study by Aral and Bakir (2022) investigated the spatiotemporal pattern of the COVID-19 spread in Turkey. The results revealed that the population density and elderly dependency ratio were crucial in explaining the new COVID-19 confirmed cases.…”

Section: Literature Reviewmentioning

confidence: 99%

How residential energy consumption has changed due to COVID-19 pandemic? An agent-based model

Khalil

Fatmi

2022

Sustainable Cities and Society

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“…According to the findings, there is a difference in water and electricity consumption at block level over time. Aral et al [ 29 ] finds the spatiotemporal pattern of the Covid-19 in Turkey. They also employ spatial regression to uncover the relevant factors affecting the Covid-19 cases.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal changes pattern in the hotspot's footprint: a case study of confirmed, recovered and deceased cases of Covid-19 in India

Tabarej

Minz

2022

Spat. Inf. Res.

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Hotspot detection and the analysis for the hotspot's footprint recently gained more attention due to the pandemic caused by the coronavirus. Different countries face the effect of the virus differently. In India, very little research has been done to find the virus transmission. The paper's main objective is to find changing pattern of the footprint of the hotspot. The confirmed, recovered, and deceased cases of the Covid-19 from April 2020 to Jan 2021 is chosen for the analysis. The study found a sudden change in the hotspot district and a similar change in the footprint from August. Change pattern of the hotspot's footprint will show that October is the most dangerous month for the first wave of the Corona. This type of study is helpful for the health department to understand the behavior of the virus during the pandemic. To find the presence of the clustering pattern in the dataset, we use Global Moran’s I. A value of Global Moran’s I greater than zero shows the clustering in the data set. Dataset is temporal, and for each type of case, the value Global Moran’s I > 0, shows the presence of clustering. Local Moran’s I find the location of cluster i.e., the hotspot. The dataset is granulated at the district level. A district with a high Local Moran’s I surrounded by a high Local Moran’s I value is considered the hotspot. Monte Carlo simulation with 999 simulations is taken to find the statistical significance. So, for the 99% significance level, the p-value is taken as 0.001. A hotspot that satisfies the p-value threshold is considered the statistically significant hotspot. The footprint of the hotspot is found from the coverage of the hotspot. Finally, a change vector is defined that finds the pattern of change in the time series of the hotspot’s footprint. Supplementary Information The online version contains supplementary material available at 10.1007/s41324-022-00443-8.

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Spatiotemporal Analysis of Covid-19 in Turkey

Cited by 38 publications

References 80 publications

Transmission of COVID-19 pandemic (Turkey) associated with short-term exposure of air quality and climatological parameters

Transmission of COVID-19 pandemic (Turkey) associated with short-term exposure of air quality and climatological parameters

How residential energy consumption has changed due to COVID-19 pandemic? An agent-based model

Spatio-temporal changes pattern in the hotspot's footprint: a case study of confirmed, recovered and deceased cases of Covid-19 in India

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