Three coronaviruses have emerged in the past two decades (i.e., SARS-CoV, MERS-CoV, and SARS-CoV-2) [1
]. Among them, the novel coronavirus (SARS-CoV-2) and the COVID-19 pandemic it causes pose a particularly serious threat to global health, as the numbers of new COVID-19 cases and deaths continue to increase at alarming rates in some countries. COVID-19 was designated as a pandemic by the World Health Organization (WHO) on 11 March 2020 [2
]. To mitigate the pandemic and control its spread in human populations, most governments have implemented drastic intervention measures, which include travel restrictions, stay-at-home orders, school closings, and restrictions of public gatherings [3
]. These control strategies seek to mitigate the spread of COVID-19 by forcing or encouraging people to practice social distancing and reducing risky social interactions.
Measures like social distancing, travel restrictions, and stay-at-home orders are non-pharmaceutical interventions for controlling the spread of COVID-19 by reducing the close contact among people and changing their behaviors [6
]. Several studies have observed the benefits of non-pharmaceutical interventions for controlling pandemics. These studies found that changes in individual behavior can have large effects on reducing the transmission of infectious disease [7
]. The benefits of human behavioral changes via social distancing seem obvious. However, human behavior may change in ways that lead to opposite consequences during a pandemic since human behavior is also shaped by the built environment [12
]. For instance, people still need to obtain groceries, medicines, and essential services during a pandemic. Further, some people may still want to conduct certain outdoor activities (e.g., hiking, picnicking, or taking beach vacations) or social activities at different venues or places to maintain their mental health during long stay-at-home orders [14
]. Hence, it is critical to investigate the relationship between the built environment and COVID-19 transmission risk in order to effectively control the pandemic. On one hand, the new knowledge generated can inform the development and enhance the effectiveness of non-pharmaceutical interventions by identifying more targeted strategies to reduce people’s risky contact with others. Further, informing people to avoid conducting high-risk activities and visiting high-risk places would help generate the behavioral changes for mitigating the spread of infectious diseases. On the other hand, certain features of the built environment can be modified or dynamically managed to promote healthy behaviors and reduce the risk of contracting COVID-19 during the pandemic.
This study thus investigates the relationship between built-environment features and COVID-19 transmission risk in Hong Kong. It seeks to answer the following questions: (1) What are the key geographic patterns of the COVID-19 cases in Hong Kong? (2) What built-environment features and areas are associated with a higher risk of COVID-19 transmission in the study area? To answer these questions, we first assess COVID-19 risk using two different indicators derived from the data on COVID-19 cases and their locations available on the Hong Kong Department of Health COVID-19 webpage. The first indicator is incidence rate (R1: case density) which is calculated based on the number of confirmed cases per 1,000 people in each of the Tertiary Planning Units (TPU) in Hong Kong. The second one is venue density (R2), which is computed based on the number of venues or buildings visited by the confirmed cases in each TPU. Then, the spatial distribution and the frequency distributions of R1 and R2 are analyzed. Finally, we use global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) to investigate the relationship between certain built-environment features and COVID-19 risk. The results show that COVID-19 risk declines dramatically over space from the TPUs with the highest risk, indicating that the risk of COVID-19 transmission tends to be concentrated in particular areas of Hong Kong. Furthermore, the rate of decay for R1, as reflected by the frequency distribution of the incidence rate, is greater than that of R2. This implies that the incidence rate indicator may underestimate the risk of COVID-19 in some suburban areas. The adjusted percent deviance explained of the GPR model is 0.44 for R1 and 0.58 for R2, which suggests a close relationship between the built-environment variables and COVID-19 risk. The GWPR models perform better than the GPR regression models, and the results indicate that the relationships between the selected built-environment variables and COVID-19 risk (i.e., R1 and R2) vary spatially across the study area.
2. The Built Environment and the Spread of Infectious Disease
At the time of writing, COVID-19 has become a global pandemic that constitutes a serious threat to public health in many countries, and Hong Kong is experiencing a rampant third wave of the pandemic. Past studies have found that local built-environment features and people’s socioeconomic characteristics significantly influence viral transmission and incidence rates [16
]. On one hand, the built environment heavily influences the space–time patterns and intensity of people’s social interactions. The location, density, and accessibility of different built-environment features (e.g., subway stations) may affect the density of people moving through certain spaces and thus may have a significant impact on disease transmission (e.g., higher built-environment densities tend to increase people’s interactions) [17
]. Different types of housing and the amount of green space in an area (which facilitates social distancing and the avoidance of crowdedness) may also be important factors. Specific types of venues and areas where superspreading events and cluster outbreaks tend to occur (e.g., pubs, restaurants, and karaoke venues) are also high-risk built-environment features for disease transmission.
Our knowledge about the influences of the built and social environments on the spread of highly contagious infectious diseases like COVID-19 is still highly limited to date, and some apparently useful findings from recent studies may be unreliable. For instance, in a recent study of 913 metropolitan counties in the U.S., Hamidi et al. [18
] found that metropolitan size is more important than density in influencing the spread of the COVID-19 pandemic in the U.S., based on county-based COVID-19 infection and mortality rates. But this finding is contrary to the results we obtained using much smaller areal units on the COVID-19 pandemic in Hong Kong; urban density seems to be an important factor affecting the incidence rate and transmission risk of COVID-19. An important methodological issue ignored in Hamidi et al. [18
] is that whether density matters may depend on the spatial scale of the analytical units or geographic areas (e.g., counties, census tracts, or census block groups) used in the analysis. This is the well-known modifiable areal unit problem (MAUP), which means that research findings may vary due to the use of different spatial scales or zonal schemes of the geographic areas for deriving the area-based variables (e.g., urban density and infection rates).
Further, studies have found that the coronavirus can survive on various specific environmental surfaces for a long time outside of its host organism [19
]. For instance, Casanova et al. [20
] observed that the coronavirus can remain infectious in water and sewage for days to weeks. In 2003, a clustered outbreak of SARS-CoV happened in a high-rise residential building in Hong Kong through the faulty and contaminated sewage system of the building [21
]. Van et al. [22
] reported that the SARS-CoV-2 virus can remain in aerosols after 3 hours and on plastic and stainless-steel surfaces after 72 hours. The various built-environment surfaces on which the coronavirus can survive are distributed among various venues (e.g., pubs and restaurants) across urban spaces, and infected individuals may leave the virus on certain surfaces (e.g., door handles, elevator buttons, and tableware) in the venues they visited or stayed (e.g., restaurants or hotels). Hence, many built-environment features that allow people to carry out their daily activities may become potential sources of infection transmission [23
Many studies have investigated the relationship between the built environment, human behavior, and health [25
]. For instance, researchers have observed that neighborhoods with healthy and diverse food environments can reduce the incidence of obesity [29
]. Dense green spaces, lower building height, and a good sky view may encourage people to undertake more outdoor activities (e.g., running, walking, cycling, picnicking, and hiking) and may lead to better health [31
]. People’s immunity may improve as a result of more physical activities and exposures to green spaces, which in turn may reduce stress, obesity, and vulnerability to infectious disease [33
]. These findings are relevant to studies on COVID-19 risk because people with poor health are more vulnerable and more easily infected, which often manifest in certain patterns of comorbidities (e.g., people with chronic diseases like diabetes and hypertension are far more likely to die from COVID-19). On the other hand, studies have found that people tend to visit areas with high accessibility and high-density commercial areas for social activities with their friends (e.g., drinking in bars, watching movies, and dining in restaurants) to reduce their stress [35
]. Further, areas with high-density transport facilities and diverse land-use types may encourage people to conduct more short-distance travel activities due to the convenience of activity opportunities [37
On the whole, these studies highlight that the spatial distribution of built-environment features influences the geographic patterns of not only human contacts and social interactions but also people’s health and immunity (by increasing certain health-promoting activities, which in turn may modify people’s COVID-19 risk). This suggests that the risk of contracting COVID-19 may be reduced by modifying risky human behaviors through modifying their interaction patterns (e.g., stay-at-home orders) and certain features of the built environment [38
]. For instance, the nodal accessibility of an area, which represents how well the area is connected with other areas via the transport network, can be dynamically modified (e.g., travel restrictions) to help control the spread of a pandemic [39
]. In the long run, improving urban designs and restructuring urban spaces (e.g., including more green spaces in high-density areas) may modify pedestrian flows and the concentrations of activities in certain areas, which may help mitigate the spread of future pandemics [41
]. Hence, the geographic patterns of human behaviors and interactions can be modified using built-environment-related non-pharmaceutical intervention measures to control the spread of COVID-19 [42
]. It is thus important to first understand the transmission dynamics of COVID-19 by examining the relationships among built-environment features and COVID-19 risk.
5. Discussion and Conclusions
Understanding the relationship between the built environment and COVID-19 risk could support health authorities to respond to the pandemic. In this paper, we utilized GPR and GWPR models to investigate the relationship between built-environment features and COVID-19 risk in Hong Kong at the TPU level. The risk of COVID-19 is assessed using the incidence rate (R1) and venue density (R2). The main findings of the study are summarized as follows.
First, both R1 and R2 have a remarkable decay effect over space. It implies that there are a few areas with a high COVID-19 risk. Similar results have been observed in the studies by Desjardins et al. [53
] and Gatto et al. [54
] in the U.S. and Italy in the early stage of the pandemic. Further, the rate of decay of R1 is higher than that of R2, reflecting that the number of TPUs with a high R1 is smaller than the number of TPUs with a high R2. This implies that the incidence rate indicator may underestimate the COVID-19 risk in some suburban areas with a large area of public space. Second, the GPR model results reveal a close relationship between selected built-environmental variables and COVID-19 risk. Note that Nguyen et al. [55
] reported similar results by using large Google Street View image datasets on American neighborhoods (i.e., zip code area). They found that land-use diversity and higher accessibility have positive associations with higher COVID-19 cases without considering spatial nonstationarity. Meanwhile, our results show a negative association between population density and the risk of COVID-19. The result differs from those of the studies of Amram et al. [56
] and Xiong et al. [57
] in the U.S. and China (i.e., population density has a positive or nonsignificant association with COVID-19 risk). These conflicting results raise several hypotheses that are worth discussing. First of all, as we mentioned in Section 2
, using different spatial scales of the analytical units or geographic areas (e.g., counties, census tracts, or census block groups) in the analysis may lead to different results (i.e., the modifiable areal unit problem). Then, several other variables could potentially prevent disease transmission over the neighborhoods in a dense and developed city. For instance, dense areas in a developed city may have better access to health care facilities and better adherence to social distancing by residents (e.g., more than 97.5% of the people in Hong Kong wore a mask when they went out and more than 85% of them avoided crowded places during February and March 2020 [58
Taking into account spatial nonstationarity, our results present that the GWPR models perform better than GPR models based on the value of adjusted percent deviance explained and AIC. The low global Moran’s I values (R1: −0.03; R2: −0.02) in the residual maps indicate that there is no systematic error in the models. The results show that the relationships between selected built-environmental variables and COVID-19 risk (i.e., R1 and R2) vary spatially across the study area. For built-environment features such as transport facility density, private residential density, building height, population density, commercial density, green space density, and sky view, the relative proportions of TPUs with positive and negative coefficients indicate a complicated relationship between selected built-environment variables and COVID-19 risk.
Based on the results of the two sets of regression models, we observed the complex relationships between selected built-environment variables and COVID-19 risk. The results also suggest other interesting observations that are worth discussing. For instance, most of the confirmed cases who lived in Tung Chung were actually infected in their workplaces (i.e., Central). It implies that the strong spatial interactions between suburban areas (e.g., Tung Chung) and the downtown area (e.g., Central) may result in similar patterns in COVID-19 risk (e.g., transport facility density for R1 and nodal accessibility for R2). Moreover, green space density and sky view in the downtown area (e.g., Central and Tsim Sha Tsui) and some suburban areas (e.g., Tung Chung and Tuen Mun) have opposite effects on COVID-19 risk.
Our findings have several important implications for non-pharmaceutical intervention measures for the government during the pandemic. First, the results of the frequency and spatial distributions of COVID-19 risk indicate that a few TPUs have a higher COVID-19 risk. Health authorities should thus focus on these areas for follow-up interventions in order to control the COVID-19 pandemic. Second, the results suggest that the incidence rate may underestimate the risk of COVID-19 in some suburban areas with large areas of public recreational space (e.g., parks). On one hand, areas with dense transport facilities, higher nodal accessibility, more green space, and a good sky view tend to attract more visitors. On the other hand, people who live in suburban areas may have to go to work in the downtown area, which has a higher risk. Moreover, the GPR and GWPR models indicate that dynamic interventions of COVID-19 transmission risk should be considered during the pandemic. For instance, restricting people from going to the country parks and certain residential areas in the suburb or limiting group activities in these areas may help control the pandemic.
In this study, we found that the spatial patterns of built-environment features influence the spatial patterns of COVID-19 risk across the study area. The findings suggest that COVID-19 can be explained by pertinent built-environment features. The methods used in the study can be applied to other cities in the world to investigate the relationship between the built environment and COVID-19 risk. Comparative studies would be helpful in revealing the effects of different human behaviors and intervention measures in different sociocultural contexts. This is a fruitful possible direction for future research.
Meanwhile, there are several limitations in this study. First, the study used COVID-19 data from 27 January to 14 April 2020, during which the Hong Kong Government had implemented several non-pharmaceutical intervention measures in response to the COVID-19 pandemic. The results from our regression models thus captured the relationship between built-environment features and COVID-19 risk under these non-pharmaceutical interventions. It is unclear whether the relationship between the built environment and COVID-19 risk we identified will also hold under normal times (i.e., without intervention measures). In the future, we plan to explore this in the post-pandemic period. Second, this study used the incidence rate and venue density as measures of COVID-19 risk. Other indicators, such as the decrease in survived coronavirus estimated based on the last visiting time by confirmed cases, could be considered in the future. As the decay effect of coronavirus survival over time is related to different environmental surfaces, incorporating this variable into the analysis may provide more insights into COVID-19 risk and its relationship with specific types of built environments. Third, this study did not consider the temporal dynamics of the spatial interaction among TPUs (e.g., the spatial interactions among TPUs may change between different times of a day or before and after the implementation of intervention policies), which may lead to different associations between the built environment and COVID-19 risk.