1. Introduction
Since the emergence of COVID-19 (coronavirus disease 2019), which was triggered by a novel coronavirus, the economic and social order of cities worldwide has been significantly affected. Although the impact of COVID-19 is weakening, urban epidemics remain frequent. The fast spread of the disease characterizes this serious public health event, the widest range of infections, and the difficulty of prevention and control [
1]. The World Health Organization (WHO) has reported 14.9 million COVID-19-related deaths in 2020–2022. The World Health Organization has warned countries worldwide about “X” diseases. The term “Disease X” is often used in research, modeling, and public health discussions to explore medicine’s disease control and management strategies. “Disease X” denotes an unforeseen and unidentified outbreak of an infectious disease. It represents the notion that a significant global pandemic could be triggered by a “pathogen X”, which, though currently unknown, has the potential to infect humans. “Disease X” is detrimental to the sustainability of the city. For example, COVID-19 could be a type of “Disease X”. This concept highlights the importance of preparedness for new and emerging infectious threats [
2]. The movement and concentration of urban populations can exacerbate the outbreak of epidemics. Well-developed urban road networks greatly facilitate the movement of regional populations. However, this further increases the risk of epidemic spread and outbreak [
3]. Before epidemic outbreaks, urban spaces with higher accessibility and proximity are the centers of the city and the areas of greater vitality in urban development [
4,
5]. After epidemic outbreaks, as these more vibrant places cluster more pedestrians, this becomes a space with a high degree of urban epidemic aggregation [
6]. The study identifies the risk of epidemics in urban areas where road space and various types of infrastructure space are clustered. Based on the identification results, the article optimizes measures to contain further deterioration caused by epidemics [
7].
The scope of this study is the Tongzhou District, Beijing. The Tongzhou District is the urban sub-center of Beijing. The Tongzhou District is among those in COVID-19 that were less affected by the outbreak in the early stages and recovered more slowly in the later stages. Although urban vitality increased in the middle and late stages, the level of development was still lower than the recovery pattern before the epidemic [
8]. Ensuring the sustainability of urban recovery remains a challenge.
Past spatial studies of urban epidemic risk have focused on comprehensive demographic, economic, and social analyses. In 2022, Nazia’s team used standard deviation ellipses, spatial autocorrelation, and Geo Detector to analyze the geographic distribution and spatial heterogeneity characteristics of COVID-19 patients and deaths [
9]. In 2022, Li Weiwei’s team used probes to analyze the geographical distribution of COVID-19 in China and the factors influencing it [
10]. In the same year, Ming Sun’s team used a comprehensive overlay of the road network and facility POI data to predict the epidemic risk space in a big city and the overlay of facility mixing degree and population data to predict the epidemic risk space in a small town [
11,
12]. However, previous studies needed to have relevant research on the spatial impact of urban infrastructure on epidemics. In 2023, Rollier’s team used spatial regression modeling and spatio-temporal analysis models to explore the relationship between population mobility and the spatial spread of the COVID-19 virus in Belgium. The results revealed that virus transmission is faster in areas with high population mobility. In future virus prevention, managers should focus on controlling areas with high mobility [
13]. In 2024, Li Yue’s team used a spatial Durbin model to analyze urban traffic flows during COVID-19 in the Glasgow area. The results show that traffic has an important impact on the city’s response to the crisis, urban resource allocation, and urban planning. However, epidemics have a direct effect on changes in urban traffic flows [
14]. Previous epidemiological risk studies have focused more on analyzing simple population dispersions, simple transport systems, or simple facility spaces. These studies have not sufficiently analyzed the deeper causes of crowd movement within cities. For example, Ren and Jiao’s use of roads identifies hazardous areas that can only be matched to corroborate with part of the locations and does not provide a complete picture of the full epidemiological hazard area [
11,
12]. The movement of people in cities is often closely related to urban infrastructure and traffic. The spread of epidemics is also related to these two factors. Previous studies have only examined a single indicator without integrating the two for a comprehensive analysis. As a result, the results do not truly reflect the impact of urban operational mechanisms on the spread of epidemics.
This research examines the impact of various city infrastructures on the risk of urban disease transmission. Consequently, the study analyzes the pattern of epidemic disease spread in relation to the spatial distribution of urban facilities, focusing on urban planning to safeguard public health and mitigate epidemic risks sustainably. Utilizing the sDNA (spatial design network analysis) model, spatial autocorrelation, geographically weighted regression, and other analytical methods, the research explores how infrastructure influences epidemic risks across different urban areas. The goal is to identify high-risk areas associated with different types of facilities and to develop more effective outbreak control strategies.
The contributions and innovations of this study are mainly in the two aspects of indicator selection and research framework. In terms of indicator selection, this study integrates infrastructure point data with traffic indicators and embeds them in the fishnet to form an indicator of infrastructure proximity. This makes the infrastructure data in the fishnet not only reflect the distribution of facilities, but also the accessibility of each regional infrastructure. In terms of the research framework, this study integrates sDNA, GWR, and other methods to form a more comprehensive research system to obtain results that are more in line with the analysis of the epidemic hazard of facilities in the Tongzhou District. Managers can take targeted, sustainable management measures based on the epidemic hazard areas obtained for each type of infrastructure.
4. Discussion
This paper uses physical spatial data such as roads and infrastructure and indicators such as road proximity, topological depth, and spatial autocorrelation. This research uses fishnet and geographically weighted regression to analyze the actual hazard areas, ensuring sustainable urban health planning. This thesis takes the Tongzhou District in Beijing as the study area, and the following discussion is derived from the results of the above analyses:
Epidemic hazard area analysis: This paper analyzes the road network in Tongzhou District using sDNA and spatial autocorrelation to derive epidemic hazard identification areas prone to congestion and with high epidemiological potential. It was found that areas with high road accessibility and low topological depth, especially the urban sub-center and Yizhuang New Town, are high hazard areas. In contrast, low hazard areas are located in marginal areas with low population activity. This study analyzes the COVID-19 hazard data using the fishnet tool to derive the actual hazard zones. Due to the relaxation of control measures, there were increased medium- and high-risk points at the end of 2022, and the high hazard areas were concentrated in the urban sub-center, Yizhuang and Songzhuang. Comparing the hazard identification area obtained by spatial autocorrelation analysis with the actual hazard area derived from fishnet analysis, it is clear that a spatial analysis of roads alone is not sufficient [
11,
12], especially in areas such as Songzhuang.
Infrastructure epidemic hazard analysis: Factors such as roads, hospitals, schools, and shopping centers are associated with epidemic incidence [
42]. This suggests that infrastructure factors may be associated with epidemic transmission. In this paper, a preliminary infrastructure hazard control program for the entire region is developed by analyzing the area where the range of proximity aggregation of each type of infrastructure intersects the actual hazardous area with the scatter fit analysis of the proportion of each of the two. This study uses the GWR model for infrastructure risk identification in the actual hazard area. Eight variables, including residential and traffic facilities, are found to be positively associated with epidemic hazard (the regression coefficients are positive). This may be related to the pattern of activities during the epidemic and the management style of the government. Finally, the spatial analyses of these variables in this study revealed different hazard areas, some associated with rivers and administrative areas and others with functional areas. This study identifies more precisely the location of epidemiological risk for each type of facility. District and town governments can develop different control strategies for the results of hazard identification for each type of infrastructure in the actual hazard area. This will enhance the sustainability of urban resilience and healthy development.
This study has implications for the management of epidemics and sustainable public health development in other cities. Other cities can strengthen epidemic prevention and management by targeting areas with high road accessibility and high infrastructure aggregation such as restaurants and shopping. In urban planning, managers can increase the number of medical facilities and epidemiological sites in these areas. This would ensure that in the event of an epidemic outbreak, healthcare facilities can be used quickly to slow down the spread of the epidemic. The combination of geographically weighted regression and fishnet in this study can identify high hazard areas for various types of infrastructure. Other cities can use this approach to analyze multiple types of spatial data to develop epidemic management measures that have less impact on the functioning of the city.
However, this study still has limitations. Firstly, the epidemiological data in the study only partially reflect the transmission process due to the different ways of classifying medium- and high-risk areas at each stage, and it is not possible to distinguish whether the epidemiological data are externally imported or internally transmitted. Future studies need more accurate access to data. Second, the epidemic in the Tongzhou District from the end of 2019 to the middle of 2022 was strictly managed, with less crowd activity and lower epidemic transmission. Epidemic data—COVID-19 hazard area data—appeared centrally in the second half of 2022, and the study results can only reflect the epidemiological potential due to crowd dispersal and facility activity in the Tongzhou District in the post-epidemic period. The study’s results do not provide a comprehensive analysis of infection in the early stages of the epidemic. Although the results of the analysis do not accurately reflect the early epidemic risk, managers can use the results of this thesis to predict the scope of the early epidemic potential and risk prevention. Third, the infrastructure POI data crawl is a September 2022 cross-section. This only takes into account the phase of post-epidemic COVID-19 when it was most infectious and does not account for differences between phases. Future research could refine the data to consider the effects of spatio-temporal variation. Research should analyze differences in the risk of epidemic transmission by infrastructure over time and under different management models. Fourth, in terms of data, future studies could add data such as the intensity of crowd dispersion and nighttime lighting to enrich the number of variables analyzed. In terms of model analysis, future studies can introduce the multi-scale geographically weighted regression model (MGWR) and SHAP model [
43] for analysis and compare the effects with the results of the GWR model and OLS model. Finally, some of the analyzed results are not consistent with conventional understanding. For example, infrastructure variables such as life, sports, and leisure facilities are negatively correlated with epidemic hazard in the model, which is inconsistent with common sense. Future studies should explore these anomalous results and their impact mechanisms in detail.
5. Conclusions
This research identifies epidemic hazards through urban roads and infrastructures. And this study compares actual hazard areas with hazard identification areas to assess the effectiveness of using road space alone for hazard identification. The results show that the identification of epidemic risk by using road space alone is inaccurate. This study proposes a sustainable method for identifying and controlling epidemiological risks in the whole area of the Tongzhou District based on infrastructure proximity. It uses the GWR model to identify and control the infrastructure within the actual hazard area.
This study expands the study of epidemic hazards in different types of urban infrastructure, provides new perspectives for future sustainability epidemic prevention and control, and provides a new method for analysis of epidemic risk in other cities. By combining the spatial design network model and the GWR model, it breaks through the limitation of identifying only hazard areas. Compared with initial studies that relied only on data such as POIs and roads to identify aggregations to predict epidemiological hazard areas and simple geostatistical analyses using actual endemic data [
44], this study achieves refined, hierarchical control, and optimization of individual facilities.
This study reveals the impact of infrastructure layout and facility proximity on epidemic transmission through a multi-method and multi-data fusion analysis. Related research such as urban planning and public health can be further validated in more diverse areas by combining accurate data to enhance the accuracy and usefulness of the research model. Based on the results of this study, city managers can make rational healthcare facility layouts and pedestrian and road management in urban planning for areas at high risk of epidemics. City managers can focus on prevention in areas of the facilities that are at higher risk for epidemics. This can enhance the resilience of cities against epidemics and contribute to long-term urban epidemic management.