Document Type : Original Article
Subjects
With the emergence of the COVID-19 virus in 2019 and the toll surpassing 1,000 victims, national and international organizations have increasingly focused on the issue of health and disease in cities and residential areas. They believe that if this crisis is left unchecked, millions could become infected, and millions might lose their lives to the extent that they likened this crisis to wartime conditions. As one of the cities situated in arid regions and among the first cities in the country to officially declare the existence of this disease, Qom, like other cities worldwide, is grappling with the impact of this disease on its social, cultural, economic, and other aspects. The current research also aims to present models for controlling and reducing the rate of disease transmission in urban areas in arid regions (in the metropolis of Qom) through the spatial analysis of the COVID-19 virus based on demographic and geographic indicators in arid regions.
The research type in the present study, in terms of purpose, is practical, and in nature, it is based on descriptive-analytical methods. In this research, information on individuals infected with the COVID-19 virus from 2019 to 2021 was extracted from the University of Medical Sciences database in Qom Province. By reviewing global and national experiences and studies in the field of spatial analysis of diseases, the relevant variables for this research were identified. Methods such as the Nearest Neighbour Index (NNI) and Moran's test were employed in this study. The Global Moran's model and the general G-statistic were utilized to measure spatial autocorrelation statistics.
The findings indicate a significant relationship between the population of neighborhoods in Qom city and the number of individuals infected with COVID-19, with the disease following a cluster pattern across the city. In the cluster pattern, the eastern part of Qom was identified as red dots, while the western part was identified as clean dots. Additionally, based on Moran's test, it was determined that the data related to COVID-19 in the neighborhoods of Qom city exhibit spatial autocorrelation. Therefore, it can be concluded that the infection rate with COVID-19 among citizens in the neighborhoods of Qom city follows a cluster pattern.
The general G-statistic was employed to identify the classification of spatial patterns. It was determined that those infected with the COVID-19 virus in the neighborhoods of Qom exhibit a cluster type with high concentration points, indicating a high concentration of infection near each other. Furthermore, Getis-Ord's Gi statistic was used to illustrate the spatial distribution of the infection pattern, revealing that 10 neighborhoods show positive spatial autocorrelation with a higher infection rate at a 99% confidence level.
In this study, similar to Mengyang Liu et al. (2020), it was found that the spatial and temporal distribution of COVID-19 exhibits a clustering pattern. Furthermore, by examining indicators such as gender, age, nationality, population density, infection rate, year of infection and death, as well as the season of infection, it was determined that the results of this study align with researchers such as Alberto-Mateo-Urdiales et al. (2021), Sumona Mondal et al. (2022), Whanhee Lee et al. (2021), Hamit Coşkun et al. (2021), Yi Han et al. (2021), Nigel Stephen Walford (2020), Yang Ye, Hongfei Qiu (2021), Kianfar & Mesgari (2022), Yang et al. (2021), Ki-Jung Kim and Youngbin Lym (2022), Claudio S. Quilodrán (2021), Álvaro Briz-Redón, Ángel Serrano-Aroca (2020), Russell S. Kirby (2017), Mengyang Liu (2021), and Diego F. Cuadros et al. (2020). In other words, similar to the work of Alberto-Mateo-Urdiales et al. (2021), it is concluded that the number of households in a neighborhood is consistent with the rate of COVID-19 infection. Also, like Sumona Mondal et al. (2022), Whanhee Lee (2021), Hamit Coşkun et al. (2021), Yi Han (2021), Alberto Mateo-Urdiales et al. (2021), as well as Nigel Stephen Walford's research (2020) and Yang Ye, Hongfei Qiu Q (2021), is consistent with the fact that population density is related to the rate of COVID-19 infection in the neighborhoods of Qom city. It is also consistent with the research of Yang (2021) and Whanhee Lee (2021) and their colleagues regarding the relationship between nationality and COVID-19 infection in urban areas. Considering that the average temperature in different seasons of the year has a significant change, this difference in temperature and change of season is directly related to COVID-19. In fact, this research is in line with that of people such as Ki-Jung Kim and Youngbin Lym (2022) and Álvaro Briz-Redón, Ángel Serrano-Aroca (2020), Sumona-Mondal et al. (2022), and Whanhee Lee et al. (2021), who found that people living in areas with higher population density were more susceptible to the spread of COVID-19. This was confirmed in the present study. In fact, the central areas of the city and places with high population density have the highest number of people infected with COVID-19 and deaths from the disease. This research, like Walford (2021), concludes that mortality is higher in certain occupational and age groups and residents of high-density areas. The results of this research can be used to extend and advance knowledge of medical geography at the city level and as a systematic analysis and research for use by decision-makers and urban planners in the field of safe cities.
In other words, similar to the study by Alberto Mateo-Urdiales et al. (2021), we have concluded that the number of households in neighborhoods is in line with the rate of COVID-19 infection. Similarly, researchers such as Sumona Mondal (2022), Whanhee Lee (2021), Hamit Coşkun (2021), Yi Han (2021), Alberto Mateo-Urdiales and colleagues (2021), and also in line with the research of Nigel Stephen Walford (2020) and Yang Ye, Hongfei Qiu (2021), it is consistent with the finding that population density is related to the rate of COVID-19 infection in the neighborhoods of Qom city. Furthermore, the relationship between nationality and COVID-19 infection in urban areas aligns with the research of Yang (2021) and Whanhee Lee (2021) and their colleagues. Considering that the average temperature varies significantly in different seasons of the year, and this temperature difference and seasonal change are directly related to COVID-19, this research aligns with the studies of individuals such as Ki-Jung Kim and Youngbin Lym (2022) and Álvaro Briz-Redón, Ángel Serrano-Aroca (2020).
Sumona Mondal and colleagues (2022) and Whanhee Lee and colleagues (2021) have identified that individuals living in areas with higher population density are more vulnerable to the spread of COVID-19. This finding is also confirmed in the present study. In fact, central neighborhoods of the city and areas with high population density have the highest number of individuals infected with COVID-19 and deaths resulting from this infection. This research, similar to Walford (2021), concludes that mortality is higher in specific occupational and age groups and in residents of areas with high population density. The findings of this study can be utilized to expand and advance the knowledge of medical geography at the city level and serve as a systematic analysis and research for use by decision-makers and urban planners in the field of creating safe cities.
