The Effect of Mobility on the Spread of COVID-19 in Light of Regional Differences in the European Union

Cheshmehzangi, Ali; Li, Zitao; Ren, Junhang; Kong, Dezhou; Shen, Yifan; Bao, Sinan; Xu, Jian; Su, Zhaohui; Dawodu, Ayotunde

doi:10.3390/su13105395

Cited by 29 publications

(8 citation statements)

References 44 publications

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“…Our scan of the literature led us to conclude that including urban mobility in the model would have greatly increased its complexity, so we chose to exclude it and focus on the objective that we had set for ourselves. However, more studies on the subject are appearing every day [72][73][74][75].…”

Section: Discussionmentioning

confidence: 99%

Evidence of the Relationship between Social Vulnerability and the Spread of COVID-19 in Urban Spaces

Galacho-Jiménez

Carruana-Herrera

Molina

et al. 2022

IJERPH

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Modeling the social-spatial structure of urban spaces can facilitate the development of guidelines aimed at curbing the spread of the COVID-19 pandemic while also acting as an instrument that helps decision-making concerning mitigation policies. The modeling process starts with categorization of urban spaces based on the concept of social vulnerability. A model is created based on this concept and the theory of analysis of social areas. Statistical techniques of factor analysis and geostatistics are applied. This generates a map of social differentiation that, when related to data on the evolution of the contagion, generates a multidimensional model of social vulnerability. The application of this model towards people (social structure) and the environment where they live (spatial structure) is specified. Our model assumes the uniqueness of cities, and it is intended to be a broadly applicable model that can be extrapolated to other urban areas if pertinent revisions are made. Our work demonstrates that aspects of the social and urban structures may be validly used to analyze and explain the spatial spread of COVID-19.

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Section: Discussionmentioning

confidence: 99%

Evidence of the Relationship between Social Vulnerability and the Spread of COVID-19 in Urban Spaces

Galacho-Jiménez

Carruana-Herrera

Molina

et al. 2022

IJERPH

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“…The COVID-19 pandemic has also affected the whole of Europe, first involving only some European countries, Italy in particular, and gradually spreading to all member states. 3 A study that examines the degree of impact of the epidemic in the first six months (February to July 2020) of the pandemic in nine EU countries based on their characteristics was carried out by Cheshmehzangi et al (2021). They found that the timeliness of relevant policies and the effectiveness of government implementation indirectly limit the spread of the epidemic by reducing population mobility.…”

Section: Literature Reviewmentioning

confidence: 99%

The Effects of Social Distancing Measures on COVID-19 Spreads in European Countries

Pažický

2023

Review of Economic Perspectives

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This study investigates the effects of social distancing measures on various types of social mobility, using country- and day-fixed effects on a panel of daily data comprising 29 European countries. Although social distancing measures proved to be significant for all types of mobility in the examined period, they are best captured by retail and recreation mobility. Linear effects of restrictive measures on COVID-19 cases and deaths are examined by OLS regression with country- and day-fixed effects on a panel of 29 European countries, while non-linear effects were investigated by quantile regressions. Stricter mobility restrictions significantly reduced COVID-19 cases and deaths, but the variant of the virus was also an important determinant. Although the Delta variant was much more infectious, its mortality reduced. However, the impact of social distancing measures on COVID-19 cases and deaths was not constant but strengthened with increasing quantiles of the distribution of cases and deaths, suggesting that an early response from policy-makers was very important. Vaccination brought benefits for both cases and deaths, but a particularly beneficial effect can be seen on COVID-19 deaths. The vaccination benefits grew with the share of the vaccinated population. Distrust in public institutions proved to have a negative impact on both COVID-19 cases and deaths. The inclusion of a set of control variables (health, economic, social and demographic) revealed that country characteristics such as cardiovascular mortality, the share of male smokers, economic development, the proportion of the population living in extreme poverty, population density, the quality of education or the share of rural population were important determinants of COVID-19 spreads. The analysis of the linear and nonlinear effects of the stringency of measures on various categories of sales according to the digital cash collection system (eKasa) in Slovakia revealed that sales in essential sectors for consumers, such as retail and grocery stores, were relatively resistant to tightening measures, while sectors that are less essential for consumers were more sensitive to social distancing measures.

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“…The last days of December 2019 witnessed the emergence of a global landmark event as the first confirmed case of an infection with a novel coronavirus was reported in Wuhan, a bustling metropolis nestled in Hubei Province at the heart of Central China. This pivotal moment sparked a series of consequential events that would soon engulf the globe in uncertainty (Cheshmehzangi et al, 2021). The Hubei health authorities sprang into action on 8 January 2020, launching an extensive survey to decipher the nature and extent of this mysterious viral outbreak.…”

Section: Introductionmentioning

confidence: 99%

Territorial patterns of COVID-19 in Iran

Aryan Kya

2024

ASPAL

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Motives: Spatial analysis has become an essential tool in understanding the underlying factors that contribute to the distribution of viral pandemics, diseases, injuries, and mortality patterns. By visualizing geographical data in spatial maps, researchers can identify local distribution patterns and potential drivers behind these patterns. In health and medical sciences, there has been a growing recognition that spatial analysis and mapping techniques are helpful in addressing various challenges related to the allocation of healthcare resource in both urban and rural areas. Aim: The objective of this study was to analyze the spatial distribution pattern of the COVID-19 pandemic and the Index of Proximity Distribution (IPD) across 31 provinces of Iran between February 2019 and February 2023. A two-stage sampling method combining convenience and cluster sampling was used to examine COVID-19 distribution patterns in 31 provinces of Iran between 22 February 2020 and 22 February 2023. COVID-19 and IPD data were collected as part of this panel study. Data were analyzed using t-tests, chi-square tests, and analysis of variance (ANOVA) in SPSS version 28 (α = 0.05). Subsequently, daily COVID-19 infection data for each province in the analyzed period were processed in ArcGIS software, and the spatial distribution pattern of the pandemic in Iran were visualized by point density analysis. Standard distance and standard deviation ellipse techniques were employed to assess the density or dispersion of infected individuals and to determine the spatial distribution pattern of COVID-19 in Iran. A spatial autocorrelation (Moran’s I) analysis was conducted to identify the spatial distribution pattern of COVID-19 in Iran. Additionally, distance-based spatial autocorrelation was used to examine the prevalence of COVID-19 infection across Iranian provinces. In a grouping analysis, 31 Iranian provinces were classified into five groups based on the number of COVID-19 cases, and spatial statistics were used to examine the prevalence of COVID-19 within each group. A hot spot analysis and a standard distance (SD) analysis were conducted to explore spatial correlations in the number of individuals affected by COVID-19 in each province. Results: Based on the Moran index, a random spatial pattern with a Z-Score of 1.485 was identified in March 2019, whereas a clustered distribution of COVID-19 with a Z-Score of 3.039 was determined in February 2023. The distance-based spatial autocorrelation analysis revealed a positive value of the Moran index (0.136627) at a distance of 383.3 kilometers from Tehran, which points to positive spatial autocorrelation and a higher number of COVID-19 cases in nearby regions. Conversely, the Moran index assumed a negative value of 0.040246 at a distance of 726.6 kilometers from Tehran, which suggests that the number of pandemic cases decreased over distance from Tehran. Moreover, based on the results of the hot spot analysis, Tehran province was identified as a hot cluster with a higher prevalence of COVID-19 cases in that region. In contrast, Bushehr province was classified as a cold cluster with a lower prevalence of COVID-19 cases in comparison with the surrounding regions. These findings provide valuable insights into the spatial distribution and clustering of COVID-19 cases in Iran. The shift from a random spatial pattern in 2019 to clustered distribution in 2023 indicates that the pandemic spread rate increased over time. The positive spatial autocorrelation near Tehran highlights the role of proximity and population movement in the transmission of the virus. Furthermore, the identification of hot spots and cold spots in a country can inform targeted interventions and resource allocation to effectively manage and control the pandemic. Overall, this study demonstrates the value of spatial analysis in identifying the spatial distribution patterns and the dynamics of the COVID-19 pandemic in Iran. The integration of spatial analysis techniques with epidemiological data contributes to a better understanding of spatial-temporal patterns, facilitates effective public health responses and resource allocation strategies. These findings contribute to the growing body of knowledge on the spatial epidemiology of COVID-19 and can aid in informing future preparedness and response efforts in Iran and other regions that face similar challenges.

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The Effect of Mobility on the Spread of COVID-19 in Light of Regional Differences in the European Union

Cited by 29 publications

References 44 publications

Evidence of the Relationship between Social Vulnerability and the Spread of COVID-19 in Urban Spaces

Evidence of the Relationship between Social Vulnerability and the Spread of COVID-19 in Urban Spaces

The Effects of Social Distancing Measures on COVID-19 Spreads in European Countries

Territorial patterns of COVID-19 in Iran

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