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Volume 39, Issue 3 (2024)
GeoRes 2024, 39(3): 279-287 |
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Received: 2024/04/22 | Accepted: 2024/07/3 | Published: 2024/07/20
Received: 2024/04/22 | Accepted: 2024/07/3 | Published: 2024/07/20
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Rafiee G, Maleki A, Shahbazi Y, Molaei A. Urban Epidemiological Resilience Modeling Based on PLS Structural Equations, Tabriz, Iran. GeoRes 2024; 39 (3) :279-287
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1- Department of Urbanism, Faculty of Architecture & Urbanism, Tabriz University of Islamic Arts, Tabriz, Iran
2- Department of Architecture, Faculty of Architecture & Urbanism, Tabriz University of Islamic Arts, Tabriz, Iran
2- Department of Architecture, Faculty of Architecture & Urbanism, Tabriz University of Islamic Arts, Tabriz, Iran
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Background
The COVID-19 pandemic has had widespread impacts on global health, economy, society, and the environment since 2020, highlighting the importance of understanding and managing the factors influencing disease spread in urban environments. Urbanization and globalization have also played a significant role in the transmission of the virus, making it essential to consider the physical and environmental factors of cities to enhance resilience against epidemic crises.
Previous Studies
Numerous studies have investigated the environmental and urban factors influencing the spread of COVID-19. Some research has highlighted the impact of natural elements such as green spaces, temperature, humidity, wind, air quality, and indoor environmental conditions on virus transmission (Wu, 2021; Teller, 2021; Valsamatzi-Panagiotou & Penchovsky, 2022; Kumar et al., 2021; Weaver et al., 2022). Another group of researchers has examined the role of urban physical and functional characteristics, such as urban morphology, density, housing types, healthcare services, and transportation in the extent of disease spread (Hussein, 2022; Azuma et al., 2020; Alam & Sultana, 2021). Additionally, some studies have explored the effects of social parameters, including ethnicity, economic status, health insurance coverage, and employment, on virus transmission (Brakefield et al., 2023). These studies underscore the multidimensional nature of the factors contributing to the spread of COVID-19 and have laid the groundwork for more comprehensive research approaches to urban resilience.
Aim(s)
This study was conducted with the aim of examining the role of indicators influenced by the physical structure of cities on urban epidemiological resilience.
Research Type
The present study was applied in nature.
Research Society, Place and Time
This study was conducted during the period from March 20 to June 18, 2020 (the nationwide lockdown period), and its geographical scope was the metropolitan area of Tabriz. Tabriz, with an area of approximately 244.5 square kilometers and a population of over 1.5 million people (based on the 2016 census), was selected as the statistical population for analysis.
Sampling Method and Number
In this study, available sampling was used. Due to limitations in accessing spatial data and detailed information related to COVID-19 cases, only data from 41 urban units within the metropolis of Tabriz were available and suitable for analysis.
Used Devices & Materials
In this study, data analysis and modeling were conducted using SPSS version 26 for Exploratory Factor Analysis (EFA) and Smart PLS version 3 for Structural Equation Modeling (SEM). Additionally, to identify indicators of epidemiological resilience, a systematic search was carried out in scientific databases such as Web of Science, Scopus, and Elsevier covering the period from 2013 to 2023. Data related to COVID-19 cases and environmental indicators were also collected from official statistical and health sources. These data had spatial relevance and were analyzable for 41 urban units within the city of Tabriz.
Findings by Text
In this study, the indicators influencing urban epidemiological resilience were classified into eight main components using Exploratory Factor Analysis (EFA): diversity and design, population and residential density, access to food, access to green spaces, access to healthcare services, environmental pollution, social factors, and economic factors (Table 1). Indicators with a factor loading below 0.3 were excluded, and the final model was reconstructed using validated indicators.
Table 1. Factor loadings and t-Statistics of the study’s indicators and factors
Reliability testing using Cronbach’s alpha and composite reliability showed that all components had acceptable reliability (above 0.7) (Table 2). Convergent validity (with AVE values higher than 0.5) and discriminant validity (according to the Fornell-Larcker criterion) were also confirmed (Table 3).
Table 2. Cronbach’s Alpha, composite and combined reliability, and convergent validity of the components
Table 3. Discriminant validity based on Fornell-Larcker criterion
In the Structural Equation Modeling (SEM) conducted with Smart PLS software, five components had a significant impact on the number of COVID-19 cases: access to food (the strongest factor with β = 0.537), healthcare services, population density, environmental pollution, and social factors (the latter with a negative effect). The components of diversity and design, green space, and economic factors did not show a significant impact and were excluded from the model (Table 4).
Table 4. Significance coefficients (t-values) in the urban epidemiological resilience model
The final model was able to explain 67% of the variance in the number of infections (R² = 0.668) and demonstrated high predictive accuracy (Q² = 0.652). Furthermore, the model's Goodness-of-Fit Index (GOF = 0.709) indicated high overall model quality (Figure 2). These results highlight the importance of service-related, social, and environmental factors in enhancing urban epidemiological resilience.
Figure 2. Determinants of urban epidemiological resilience
Main Comparisons to Similar Studies
The results of this study are consistent with numerous international studies and underscore the significance of environmental factors in the spread or control of epidemics. For example, the direct correlation between population density and increased COVID-19 cases aligns with the findings of [Ma et al., 2021; Li et al., 2020; Huang et al., 2021; Yip et al., 2021; Nasiri et al., 2022], who identified high density as a key driver in the urban transmission of the virus. Moreover, the unexpected negative impact of greater access to healthcare services, which led to higher infection rates, corresponds with the findings of [Zhang et al., 2021; Qiu et al., 2020], who linked dense concentrations of medical facilities with greater epidemic severity in cities such as New York and Wuhan. Similarly, the identification of food centers as quarantine breakpoints and epidemic intensifiers matches the results reported by [Verma et al., 2021; Alidadi & Sharifi, 2022]. The study also confirmed the role of air pollution, in line with [Domingo & Rovira, 2020; Martin et al., 2021], showing that high pollution levels increase vulnerability to the virus. Finally, the results also supported the indirect influence of social factors, such as unemployment and migration, consistent with the findings of [Alidadi & Sharifi, 2022].
Suggestions
Based on the study's findings and the significant role of environmental factors in enhancing urban epidemiological resilience, it is recommended to adopt risk-sensitive land use planning, promote health-oriented urban design, implement service management plans that improve environmental hygiene and reduce pollution, and strengthen both health and green infrastructure.
Conclusion
The environmental factors influencing non-pharmaceutical interventions to enhance urban epidemiological resilience include population and residential density, access to food and quarantine breach points, access to healthcare services, environmental pollution, and social factors, all of which explain a significant portion of the variations in epidemic dynamics.
Acknowledgments: None reported by the authors.
Ethical Permission: None reported by the authors.
Conflict of Interest: None reported by the authors.
Authors’ Contributions: Rafiee Gh (First author), Main Researcher/ Discussion Writer (50%); Maleki A (Second author), Methodologist (30%); Shahbazi Y (Third author), Introduction Writer (10%); Molaei A (Fourth Author) Statistical Analyst (10%)
Funding: None reported by the authors.
The COVID-19 pandemic has had widespread impacts on global health, economy, society, and the environment since 2020, highlighting the importance of understanding and managing the factors influencing disease spread in urban environments. Urbanization and globalization have also played a significant role in the transmission of the virus, making it essential to consider the physical and environmental factors of cities to enhance resilience against epidemic crises.
Previous Studies
Numerous studies have investigated the environmental and urban factors influencing the spread of COVID-19. Some research has highlighted the impact of natural elements such as green spaces, temperature, humidity, wind, air quality, and indoor environmental conditions on virus transmission (Wu, 2021; Teller, 2021; Valsamatzi-Panagiotou & Penchovsky, 2022; Kumar et al., 2021; Weaver et al., 2022). Another group of researchers has examined the role of urban physical and functional characteristics, such as urban morphology, density, housing types, healthcare services, and transportation in the extent of disease spread (Hussein, 2022; Azuma et al., 2020; Alam & Sultana, 2021). Additionally, some studies have explored the effects of social parameters, including ethnicity, economic status, health insurance coverage, and employment, on virus transmission (Brakefield et al., 2023). These studies underscore the multidimensional nature of the factors contributing to the spread of COVID-19 and have laid the groundwork for more comprehensive research approaches to urban resilience.
Aim(s)
This study was conducted with the aim of examining the role of indicators influenced by the physical structure of cities on urban epidemiological resilience.
Research Type
The present study was applied in nature.
Research Society, Place and Time
This study was conducted during the period from March 20 to June 18, 2020 (the nationwide lockdown period), and its geographical scope was the metropolitan area of Tabriz. Tabriz, with an area of approximately 244.5 square kilometers and a population of over 1.5 million people (based on the 2016 census), was selected as the statistical population for analysis.
Sampling Method and Number
In this study, available sampling was used. Due to limitations in accessing spatial data and detailed information related to COVID-19 cases, only data from 41 urban units within the metropolis of Tabriz were available and suitable for analysis.
Used Devices & Materials
In this study, data analysis and modeling were conducted using SPSS version 26 for Exploratory Factor Analysis (EFA) and Smart PLS version 3 for Structural Equation Modeling (SEM). Additionally, to identify indicators of epidemiological resilience, a systematic search was carried out in scientific databases such as Web of Science, Scopus, and Elsevier covering the period from 2013 to 2023. Data related to COVID-19 cases and environmental indicators were also collected from official statistical and health sources. These data had spatial relevance and were analyzable for 41 urban units within the city of Tabriz.
Findings by Text
In this study, the indicators influencing urban epidemiological resilience were classified into eight main components using Exploratory Factor Analysis (EFA): diversity and design, population and residential density, access to food, access to green spaces, access to healthcare services, environmental pollution, social factors, and economic factors (Table 1). Indicators with a factor loading below 0.3 were excluded, and the final model was reconstructed using validated indicators.
Table 1. Factor loadings and t-Statistics of the study’s indicators and factors
Reliability testing using Cronbach’s alpha and composite reliability showed that all components had acceptable reliability (above 0.7) (Table 2). Convergent validity (with AVE values higher than 0.5) and discriminant validity (according to the Fornell-Larcker criterion) were also confirmed (Table 3).
Table 2. Cronbach’s Alpha, composite and combined reliability, and convergent validity of the components
Table 3. Discriminant validity based on Fornell-Larcker criterion
In the Structural Equation Modeling (SEM) conducted with Smart PLS software, five components had a significant impact on the number of COVID-19 cases: access to food (the strongest factor with β = 0.537), healthcare services, population density, environmental pollution, and social factors (the latter with a negative effect). The components of diversity and design, green space, and economic factors did not show a significant impact and were excluded from the model (Table 4).
Table 4. Significance coefficients (t-values) in the urban epidemiological resilience model
The final model was able to explain 67% of the variance in the number of infections (R² = 0.668) and demonstrated high predictive accuracy (Q² = 0.652). Furthermore, the model's Goodness-of-Fit Index (GOF = 0.709) indicated high overall model quality (Figure 2). These results highlight the importance of service-related, social, and environmental factors in enhancing urban epidemiological resilience.
Figure 2. Determinants of urban epidemiological resilience
Main Comparisons to Similar Studies
The results of this study are consistent with numerous international studies and underscore the significance of environmental factors in the spread or control of epidemics. For example, the direct correlation between population density and increased COVID-19 cases aligns with the findings of [Ma et al., 2021; Li et al., 2020; Huang et al., 2021; Yip et al., 2021; Nasiri et al., 2022], who identified high density as a key driver in the urban transmission of the virus. Moreover, the unexpected negative impact of greater access to healthcare services, which led to higher infection rates, corresponds with the findings of [Zhang et al., 2021; Qiu et al., 2020], who linked dense concentrations of medical facilities with greater epidemic severity in cities such as New York and Wuhan. Similarly, the identification of food centers as quarantine breakpoints and epidemic intensifiers matches the results reported by [Verma et al., 2021; Alidadi & Sharifi, 2022]. The study also confirmed the role of air pollution, in line with [Domingo & Rovira, 2020; Martin et al., 2021], showing that high pollution levels increase vulnerability to the virus. Finally, the results also supported the indirect influence of social factors, such as unemployment and migration, consistent with the findings of [Alidadi & Sharifi, 2022].
Suggestions
Based on the study's findings and the significant role of environmental factors in enhancing urban epidemiological resilience, it is recommended to adopt risk-sensitive land use planning, promote health-oriented urban design, implement service management plans that improve environmental hygiene and reduce pollution, and strengthen both health and green infrastructure.
Conclusion
The environmental factors influencing non-pharmaceutical interventions to enhance urban epidemiological resilience include population and residential density, access to food and quarantine breach points, access to healthcare services, environmental pollution, and social factors, all of which explain a significant portion of the variations in epidemic dynamics.
Acknowledgments: None reported by the authors.
Ethical Permission: None reported by the authors.
Conflict of Interest: None reported by the authors.
Authors’ Contributions: Rafiee Gh (First author), Main Researcher/ Discussion Writer (50%); Maleki A (Second author), Methodologist (30%); Shahbazi Y (Third author), Introduction Writer (10%); Molaei A (Fourth Author) Statistical Analyst (10%)
Funding: None reported by the authors.
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