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Article

Revealing the Impact of Urban Form on COVID-19 Based on Machine Learning: Taking Macau as an Example

Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(21), 14341; https://doi.org/10.3390/su142114341
Received: 22 August 2022 / Revised: 27 October 2022 / Accepted: 29 October 2022 / Published: 2 November 2022
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)

Abstract

:
The COVID-19 pandemic has led to a re-examination of the urban space, and the field of planning and architecture is no exception. In this study, a conditional generative adversarial network (CGAN) is used to construct a method for deriving the distribution of urban texture through the distribution hotspots of the COVID-19 epidemic. At the same time, the relationship between urban form and the COVID-19 epidemic is established, so that the machine can automatically deduce and calculate the appearance of urban forms that are prone to epidemics and may have high risks, which has application value and potential in the field of planning and design. In this study, taking Macau as an example, this method was used to conduct model training, image generation, and comparison of the derivation results of different assumed epidemic distribution degrees. The implications of this study for urban planning are as follows: (1) there is a correlation between different urban forms and the distribution of epidemics, and CGAN can be used to predict urban forms with high epidemic risk; (2) large-scale buildings and high-density buildings can promote the distribution of the COVID-19 epidemic; (3) green public open spaces and squares have an inhibitory effect on the distribution of the COVID-19 epidemic; and (4) reducing the volume and density of buildings and increasing the area of green public open spaces and squares can help reduce the distribution of the COVID-19 epidemic.

1. Introduction

1.1. Research Background

As of 16 August 2022, since the outbreak of global transmission in early 2020, the total number of reported cases of COVID-19 was 590 million, affecting 7% of the world’s population and 210 countries, with a mortality rate of 1.09% [1]. According to global anti-epidemic research over the past two years, various studies have found that the spread of COVID-19 is related to many factors: housing quality and living conditions, crowding, regional climate, air pollutants, population migration, and government intervention [2,3,4,5]. At present, the methods for studying COVID-19 from an epidemiological perspective are mainly traditional kinetic models and statistical models: classic compartmental models, SIR [6], SIER [7], SEIRS [8], and SEIHR [9]. They rely on people flow data for core analysis. Therefore, in the use of traditional models, less consideration is given to the information elements of material space. With the development of artificial intelligence technology, machine learning methods can be used to analyze the characteristics of the COVID-19 epidemic, so as to achieve efficient epidemic prevention and control [10].

1.2. Literature Review

Machine learning techniques have been widely used in infectious disease research, including severe acute respiratory syndrome (SARS), H1N1 influenza virus, and Middle East respiratory syndrome coronavirus (MERS-CoV) [11]. Currently, machine learning-based COVID-19 research can be divided into the following four categories: (1) epidemiological causal inference of nonlinear and intervariable interactions and complex processing of multidimensional data [12,13]; (2) disease prediction, diagnosis, prognosis, and clinical decision making [14,15,16,17,18]; (3) the use of random forests to identify and analyze diseases, so as to conduct genome-wide association studies [19,20,21]; and (4) spatial epidemiological research based on the combination of geospatial information and remote sensing data, including the meteorological data and case distribution map [22], the urbanization and COVID-19 vulnerability distribution map [23], and the distribution map of predicted transmission [24,25]. It is clear from the literature that the first three categories are currently the most widely used, especially in the medical field. The research direction of spatial epidemiology needs to be further explored, which also depends on the further mining of geographic information data in the future.

1.3. Problem Statement and Objectives

Most of the machine learning-based research on the COVID-19 epidemic focuses on the prediction of the COVID-19 virus, with fewer studies on the impact of urban environmental factors on the epidemic. In this paper, the sample of the study is improved. First, the COVID-19 virus hotspot distribution map is used as training set A, and the city morphology map is used as training set B. The ultimate goal is to use the distribution of the COVID-19 virus in cities to predict urban form, so as to deeply study the impact of urban form on the COVID-19 virus. Second, a conditional generative adversarial network (CGAN) is employed to analyze the relationship between urban spatial risk factors and urban form. Lastly, assuming different risk distribution patterns, the effects of different urban patterns on the COVID-19 virus are analyzed.
This study proposes an image-based CGAN, so that the COVID-19 virus heat map can be used as a material for predicting urban morphology maps, and the research results can reflect the promotion or inhibition effect of urban morphology on the COVID-19 virus. This research process can also be used in other studies related to epidemics and urban forms. The research process is as follows: (1) taking Macau as a case study, statistics of the transmission trajectory of new coronavirus patients in the city, as well as a COVID-19 hotspot map are established as the material for machine learning; (2) in the area corresponding to the COVID-19 hotspot map, different colors are used to extract and simplify the content of the urban form, and it is used as secondary material for machine learning; (3) the above materials are used as input into the CGAN and the generated weight model is tested. Furthermore, the distribution of the COVID-19 epidemic in other regions of Macau is imported for forecasting; (4) assuming the distribution of the COVID-19 epidemic to different degrees, including extreme cases of complete distribution and no distribution, the derivation of urban form is carried out; and, lastly, (5) the above test results are compared and analyzed, and the impact of urban form on the distribution of the COVID-19 epidemic is summarized.

2. Materials and Methods

2.1. Study Area and Data Sources

This research is based on the image synthesis technology of machine learning, and an urban morphology map is generated through the footprint heat map of the COVID-19 epidemic. The physical patterns, layouts, and structures that make up an urban center are collectively called the urban form. The study of urban morphology is an indispensable part of studying the form of human settlements, the process of their formation and transformation, and urban planning and design, and it helps to understand and analyze the process and characteristics of urban development. According to a book written by Vitor Oliveira, urban morphology is the science that studies the physical form of cities, as well as the main agents and processes shaping them over time [26]. In this study, the main urban form elements considered are roads, green spaces, water/coastline, buildings, and vacant land.
First, the heatmap of the COVID-19 epidemic footprint is used as training set A, and the corresponding urban morphology map of Macau Peninsula (Macau is an inalienable part of China’s territory, consisting of the Macau Peninsula, Taipa, and Coloane, with a land area of 32.9 square kilometers. The Macau Peninsula is connected to mainland China (Zhuhai City, Guangdong Province) to the north, and to Taipa to the south by the Ponte Governador Nobre de Carvalho (Carvalho Bridge), the Ponte de Amizade (Friendship Bridge), and the Ponte de Sai Van (Sai Van Bridge). Taipa and Coloane are connected by a 2.2 km-long, six-lane highway.) is used as training set B. Then, a conditional generative adversarial network (CGAN) is implemented for training [27]. In the image translation using training set A and training set B, the generator and the discriminator are allowed to play against each other, thus improving the quality of the generated pictures and realizing the ability to generate urban morphology maps [28].
The experimental materials are shown in Figure 1. In the processing of the Macau map, in order to simplify the data, various elements in the map were represented in different colors and presented in the form of color pictures [29]. In this study, five colors were used to represent the elements on the map: roads and squares are red (R = 255, G = 0, B0), green spaces are green (R = 200, G = 215, B = 158), water is blue (R = 158, G = 188, B = 216), buildings are white (R = 255, G = 255, B = 255), and land is black (R = 0, G = 0, B = 0). These five colors represent most of the content in the city map. The footprint hotspot data of the COVID-19 epidemic were generated by researchers from the statistics of the footprint report of a total of 500 patients in Macau (from mid-June to early July 2022), which was fully disclosed by the Macau Health Bureau. The addresses of the footprints were mainly registered according to the building of residence. Despite the longest residence time and the highest risk of carrying the virus, due to personal privacy, some private itineraries were not officially announced. Therefore, this study could only screen the footprints of a total of 3265 confirmed patients on the Macau Peninsula (more details can be found in Appendix B, Table A1). Then, the addresses were converted into latitude and longitude coordinates using Google Maps API Web Services, before being input into ArcGIS Pro to generate hotspot data. Since CGAN requires paired datasets for training, in order to make the data correspond one-to-one, they were uniformly corrected into the Observatorio Meteorologico 1965 Macau Grid.

2.2. Model Construction Process

Since machine learning requires a large number of samples, in order to obtain more accurate experimental data, the sample images were divided into grids with a size of 512 × 512 pixels, and each image slice was about 4 (ha) in area. After weighing the quality and quantity, a 6 × 6 grid was obtained, and 36 images of the urban morphology map, the COVID-19 epidemic heat map, and the hypothetical heatmap were cut into 36 images, yielding a total of 144 samples (Figure 2).
The conditional generative adversarial network (CGAN) is a variant of the generative adversarial network (GAN). Consistent with the original GAN, the CGAN is mainly composed of two adversarial models: a generator responsible for generating images and a discriminator for judging the authenticity of the generated images. As shown in Figure 3, the main principles are as follows: (1) the generator generates fake pictures according to the input picture (Train A) and random vector (Z); (2) the discriminator determines another set of corresponding pictures (Train B) and random vectors as true pictures. At the same time, it is compared with the fake pictures input by the generator, whereby the real pictures are marked as 1, and the fake pictures are marked as 0; (3) if the generated image is judged to be false, the discriminator returns the deviation value between the fake image and the real image to the generator. The generator is subsequently upgraded so that it can generate more realistic pictures. On the contrary, if the discriminator judges that the generated image is real, the discriminator continues to learn from the training set to improve the recognition ability; and (4) through adversarial training, the generator can finally generate fake and real pictures, so as to achieve the goal of generating urban morphology maps.

3. Results, Analysis, and Discussion

3.1. Training Result

Upon training the model for multiple iterations, we found that the loss values of the trained generator and discriminator fluctuated significantly but tended to decrease overall. In Figure 4, the orange line represents the fluctuation curve in the machine learning process, i.e., the stability of the learning result. It should be noted that the stability of machine learning does not represent the accuracy of the learning results of the model. Different input conditions and the completeness of input data have different effects on the stability of machine learning results, and the correlation between learning conditions and target results can also be indirectly reflected.
The different input data in Figure 5 were taken as an example, while the COVID-19 epidemic distribution hotspot was taken as the input data. The stability of the model results after 200 iterations of learning revealed a certain correlation between the distribution of the COVID-19 epidemic footprint and the components of urban form.
At the same time, Figure 5 shows the comparison of iterative training results for model learning accuracy. The hotspots of the epidemic distribution, the actual urban form, and the derived urban form results (horizontal columns) were iteratively learned 50 times, 100 times, 150 times, and 200 times. The “input” in each group represents the spatial data of the distribution of COVID-19 epidemic hotspots in the target area. “Real” represents the urban form distribution of the target area, which is only used as a reference for comparison of results and does not participate in machine learning training. “Generated” means that the machine has generated a prediction result on the urban form distribution of the target area.
It can be seen that, under the condition of 50 iterations, the learning results of the machine were blurred, and the accuracy was lower than that of the real urban form area. Under the condition of 150 iterations, the results of machine learning improved, but the accuracy was still not ideal. Under the condition of 200 iterations, it can be seen that the similarity between the urban form area obtained from the basic learning results and the actual urban form area reached a high level. At the same time, is also shown that, under the condition of maximizing the saving of machine load and learning time cost, improving the accuracy of this machine learning model could basically meet the requirements of the target after 200 iterations.
With the increase in training, the study also found that: (1) the overall accuracy of training significantly improved; (2) in the urban area, the distribution of roads did not improve significantly; and (3) the range of buildings could be deduced from the distribution of COVID-19 epidemic hotspots. Compared with real urban areas, a high degree of similarity was restored, but the accuracy of building blocks and shapes was not significantly improved. For example, in the 200th iteration, the real urban form had a more regular and cornered road segmentation. However, in the urban form regions derived from machine learning training, the roads were more vivid and curved, although the extent of the buildings was roughly the same.

3.2. Results Comparison of Different Types of Urban Forms

Furthermore, this study took the three urban form areas as three typical categories for comparative analysis. As shown in Figure 6, A2, B2, and C2 are the urban areas formed in three different periods in the city. A2 is an area with many residential and industrial buildings distributed in the 1990s, including large-scale buildings. B2 is an area with several mixed commercial and residential buildings from the 1970s. At present, there are no large buildings, instead showing the characteristics of a mixture of medium and small buildings, representing a typical commercial center that has entered modern society. C2 is the area under the scope of World Cultural Heritage protection, representing the most prosperous streets and commercial centers in Macau in the 1920s and 1930s, including low-rise buildings with smaller volumes. At the same time, it also retains the most traditional and primitive urban form of the city.
Through the training results, it can be found that: (1) the distribution of important urban roads (the widest roads) in the target area had little correlation with the distribution of COVID-19 epidemic hotspots (Figure 6A1–A3); (2) the arrangement of building volumes had a certain correlation with the distribution of COVID-19 epidemic hotspots. Buildings with large volumes highly overlapped with the distribution of COVID-19 epidemic hotspots (Figure 6B1–B3); and (3) when the base area of buildings presented similar plots, the number of buildings in the plot did not affect the distribution of COVID-19 epidemic hotspots (Figure 6C1–C3).

3.3. Model Application and Analysis

The city of Macau consists of three originally independent islands, namely the Macau Peninsula, Taipa, and Coloane, through land reclamation. From the perspective of urban development and intensity, compared with the relative lag in Coloane’s construction, the island and Taipa districts have the same high-density, large-scale, and strongly enclosed street market appearance. The current connection model for the island was able to clearly reflect the internal connection between the main distribution of the epidemic and the urban form. Therefore, the known epidemic distribution points in Taipa were selected as new information to be implanted into the model, which could reflect the prediction and judgment of the urban form of Taipa made by the connection model.
From the analysis of the results generated by the model operation, it was found that the density of the epidemic distribution points was closely related to the objective factors of the city: (1) according to the epidemic distribution shown in A1 in Figure 7, when it is concentrated in a single location, it is often proportional to the construction intensity of that location. As shown in A2 in Figure 7, there are many dense buildings, and the arrangement of undeveloped vacant land along the road and the built environment forms a spatial transition and partition, effectively controlling the epidemic in a specific area. (2) Furthermore, when the epidemic situation is distributed in multiple places and multiple points, as shown in B1 of Figure 7, it shows the urban form generated by B2 of Figure 7. The main reason is that the convenience of road traffic organization is improved, the accessibility between built environments is enhanced, and the degree of enclosure is high, resulting in the rapid mobility and large coverage of the epidemic, which has the greatest impact on the city. (3) Additionally, in areas where the urban road network is concentrated, road intersection squares and surrounding open spaces (black plots) can effectively slow or prevent the spread of the epidemic when the epidemic presents a single sporadic distribution, as shown in Figure 7C1. At this time, the size of the building becomes an important indicator of the degree of impact of the epidemic. (4) Moreover, when the epidemic situation is distributed locally, as shown in D1 of Figure 7, although the built environment is complex, dense, and large in number, as shown in D2, the weakening of urban road accessibility becomes a key factor in the inability of the epidemic to have a large-scale and high-concentration impact.
As analyzed above, the correlation model between urban morphological elements and the impact of the epidemic inferred from the epidemic distribution in Taipa showed that urban road accessibility, built-up environment density, and appropriate land space have an important impact on the spread of the epidemic. Therefore, adjusting the relative relationship among urban morphological elements has a positive effect on epidemic prevention and control.

3.4. Assuming the Epidemic Distribution to Derive the Results of Urban Form

Lastly, images of the distribution of COVID-19 outbreaks with different shapes were assumed in this study. Then, machine learning was used to deduce and generate the urban form. In Figure 8, gray is the hypothetical COVID-19 epidemic distribution area (A1, C1, D1, E1, and F1), white is the presumed distribution area (B1) when the COVID-19 epidemic peaks, and black is the area where no COVID-19 outbreak is assumed. Meanwhile, A1 to F1 represent different distributions that may exist when the COVID-19 outbreak occurs. A2 to F2 are urban-form areas derived from machine learning.
The results of the study found that: (1) urban form is related to the distribution of the epidemic, but it has a weak relationship with the intensity of the distribution of the epidemic; (2) furthermore, urban form is related to the shape of the epidemic distribution, and the results of the derived urban form are roughly consistent with the scope of the epidemic distribution area in the outline; and (3) areas with more epidemic distribution have a higher building density. In areas with fewer outbreaks, buildings are more sparsely distributed.

4. Discussion: Pandemic and Sustainable Living

In order to combat the wealth gap, climate change, gender equality, and other issues, in 2015, the United Nations launched the “2030 Sustainable Development Goals” (SDGs), proposing 17 core goals for global governments and enterprises to jointly move towards sustainable development. SDG Goal 11 is “building cities and villages that are inclusive, safe, resilient, and sustainable”. On 9 July 2020, UN-Habitat and the World Health Organization jointly hosted an online forum on “Urban Form and COVID-19: Reflections on Density, Overcrowding, Public Space, and Health” [30,31]. In the current context of the spread of COVID-19, how can local governments take action to implement the United Nations 2030 Agenda for Sustainable Development? Moreover, how should government officials, professionals, and scholars better understand the relationship between urban form and disease transmission and prevention? Perhaps, this is one of the issues we need to think about in regard to how to sustainably develop urban life. The reality of facing COVID-19 is that, in low-income neighborhoods, developers have no incentive to increase floor space or require additional infrastructure improvements. Especially in some high-density cities, people live in tighter quarters, often in multigenerational households, and they work in jobs that require face-to-face interaction. The risk of contagion increases as communities lack physical structures and amenities to enhance livability, and residents have no choice but to go out every day to find work or services.
Combining machine learning with the results of the relationship between the COVID-19 heat map and urban form to shape sustainable urban life, the following suggestions can be made in the field of planning and design:
(1) Reconsider the scale, design, and spatial distribution of public spaces. Public spaces can help reduce the risk of spreading COVID-19. Due to the long-term stay-at-home orders, people began to seek physical recovery and psychological pressure relief from green spaces, and the demand for urban green spaces has also increased. However, in the face of cities of different scales, accurate data are required for the allocation of public spaces in order to achieve a better balance of community resources.
(2) Attach importance to the design of green open space. In this study, it was found that there were few or no outbreak clusters in the green space distribution area. It is also possible that the ecological purification effect brought by the plant landscape on green land can effectively slow the spread of the epidemic. At the same time, people enter the green open space to get physical exercise, further improve their physical and mental health, and incorporate an auxiliary role in resisting diseases. Therefore, in urban planning, it is also necessary to consider the combination of various types of green open space (pocket parks, terrace recreation areas, atria of high-density buildings, and rooftops of commercial buildings).
(3) Avoid large-scale architectural designs in the development of residential areas. A single, large building can easily cause crowds. Once a danger occurs, it is easy to cause safety hazards if not evacuated promptly and effectively. Therefore, from architectural planning, a more scattered, multibuilding, and organically combined design mode can be considered. At the same time, attention should be paid to fully reserving space for disaster prevention and emergency response in architectural design. At the same time, the design and construction process should be full of “elasticity” and “resilience”, so as to comprehensively improve disaster prevention and mitigation capabilities.
(4) Pay attention to the semi-enclosed building combination. COVID-19 spreads via aerosols. However, many shopping malls today are introverted “shopping boxes”. In the face of major epidemics, this has instead become a weakness. The primary consideration for consumers is how to strengthen their own protection in this space. Therefore, some lifestyles are gradually changing, and some businesses with open spaces and better ecological environments are deliberately selected for consumption. Open blocks not only disperse the flow of people, but also prevent the accumulation of dense spaces. The advantages of openness, better ventilation, and more outdoor space make it easier for these areas to become the first choice for leisure consumption. Promoting the development of greenway commerce, park block commerce, and “park+” urban commercial complexes, and integrating the concept of green and sustainable living with architecture and commerce are also design models for the current response to the COVID-19 epidemic.

5. Conclusions

Through machine learning, this study used the heat map of the distribution of the COVID-19 epidemic in Macau to derive the urban form, and the following conclusions can be drawn:
(1) Through CGAN, the distribution area of the COVID-19 epidemic can be used to deduce urban forms that may be high-risk and prone to epidemics. This method has potential applications and practical value in the field of future urban design. The relationship between the spatial form of urban risk-prone areas and the degree of epidemic distribution is predicted by the model, and urban design iteration is carried out, which also has certain universality and reference in other cities.
(2) From the results of model training and model application, it can be seen that, when the urban epidemic distribution heatmap was used as the input data for learning, the stability of the model learning results was poor, but the accuracy gradually improved. Under the consideration of saving machine load and learning time cost, the prediction accuracy of the model after 200 iterations of learning and training could basically meet the requirements of target prediction.
(3) From the comparison of the epidemic distribution heat map, the actual urban form area, and the derived urban form area, it can be seen that the combination of urban forms is related to the risk of epidemic occurrence. Larger buildings have a high degree of overlap with the distribution of COVID-19 epidemic hotspots. Areas with a high degree of road enclosure highly overlap with the distribution of COVID-19 epidemic hotspots. These two types of urban forms require special attention. Green public open spaces and squares have an inhibitory effect on the distribution of the COVID-19 epidemic. Reducing the building volume and density can not only increase the area of green public open space and squares, but also help reduce the distribution of the COVID-19 epidemic.
The spread of the COVID-19 outbreak has caused the public to rethink the issue of public health governance. At the same time, it also allows government departments, planners and architects, and experts and scholars to rethink the sustainable development of urban decision making. Needless to say, in addition to epidemic spread, policymakers and planners must consider many other factors when considering residential density, such as economic thresholds and dynamism, social mix and dynamism, urban sprawl, and per capita infrastructure costs. The impact of building density and urban form is part of the comprehensive consideration, which has significance for auxiliary decision making. The method of deriving urban form through machine learning can refer to design types that avoid high risks, which can be used as an important reference for urban planning and design in practical applications. In order to reduce the possibility of outbreaks of epidemic risk in urban space design, architects and researchers can make comparisons on the basis of the results derived from the distribution of epidemic hotspots, as well as adjust the design of urban textures, such as building density, roads, and green space layout.

Author Contributions

Conceptualization, Y.C. and L.Z.; methodology, L.Z.; software, L.Z.; validation, Y.C. and L.Z.; formal analysis, J.S.; investigation, L.H.; resources, J.S.; data curation, L.H.; writing—original draft preparation, Y.C. and L.Z.; writing—review and editing, Y.C. and L.Z.; visualization, Y.C. and L.Z.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Specialized Subsidy Scheme for Higher Education Fund of the Macau SAR Government in the Area of Research in Humanities and Social Sciences (and Specialized Subsidy Scheme for Prevention and Response to Major Infectious Diseases) (No. HSS-MUST-2020-09).

Institutional Review Board Statement

Not applicable for studies not involving humans or animals.

Informed Consent Statement

Not applicable for studies not involving humans.

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

Funded by the Specialized Subsidy Scheme for Higher Education Fund of the Macau SAR Government in the Area of Research in Humanities and Social Sciences (and Specialized Subsidy Scheme for Prevention and Response to Major Infectious Diseases) (No. HSS-MUST-2020-09) for all the help and supports to this research. We are very grateful to the students who assisted in the collection of trajectory and statistical raw data: Hoi Ian Tam, Linsheng Huang, Lei Zhang, Shaoxuan Li, Senyu Lou, Shan Jiang, Junxin Song, Nan Xu, Yanrong Wang, Tong Ling, Liangqiu Lu, Wenjian Li, Ut Chong Leong.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Machine learning environment configuration: the operating system is Windows 11 (X64), the Cuda version is 11.5, the deep learning framework is Pytorch, the graphics card is GeForce GTX 3070 (16G), and the processor is AMD Ryzen 9 5900HX (3.30 GHz).

Appendix B

On 19 June 2022, health officials in Macau announced that it had found dozens of positive cases of COVID-19 in an unprecedented outbreak. Driven by the Omicron BA.5.1 subvariant, the COVID-19 outbreak was the city’s first since October 2021. While the exact source of the virus that seeded the outbreak the month prior is still unknown, it was reported that those cases were traced to a prison worker and a butcher who frequently travelled between the casino hub and the neighboring Chinese city of Zhuhai. However, in the process of statistics, the main occupations of the groups that caused this major epidemic first were: domestic helpers (people of Myanmar nationality), construction site workers (non-local employees). Then, it spread to different groups of people. The footprint hotspot data of the COVID-19 epidemic was generated by researchers based on the statistics of the footprint report of a total of 500 patients in Macau (from mid-June to early July 2022), which was fully disclosed by the Macau Health Bureau. Reference website (Chinese webpage, column “Itinerary of Positive Cases”): https://www.ssm.gov.mo/apps1/PreventCOVID-19/ch.aspx#clg22916, accessed on 1 July 2022.
Table A1. The distribution information of 500 cases obtained by the author’s statistics.
Table A1. The distribution information of 500 cases obtained by the author’s statistics.
Case No.GenderAgeDocument Type or
Nationality
Statistical AreaBuilding NameNumber of Units in the BuildingElevator/
Staircase
Date Detected Positive (DD/MM/YY)
01-618Female28BurmeseSan KioEDF. YIM LAI11Staircase18 June 2022
02-618Female30EDF. YIM LAI11Staircase18 June 2022
03-618Female36EDF. YIM LAI11Staircase18 June 2022
04-618Female36EDF. YIM LAI11Staircase18 June 2022
05-618Female32EDF. YIM LAI11Staircase18 June 2022
06-618Female25EDF. YIM LAI11Staircase19 June 2022
07-618Female35EDF. YIM LAI11Staircase19 June 2022
08-618Female30EDF. YIM LAI11Staircase19 June 2022
09-618Female32EDF. YIM LAI11Staircase19 June 2022
10-618Male37Macau, ChinaEDF. YIM LAI11Staircase19 June 2022
11-618Female85EDF. YIM LAI11Staircase19 June 2022
12-618Male0.75EDF. TAT CHEONG84Staircase19 June 2022
13-618Male34EDF. TAT CHEONG84Staircase19 June 2022
14-618Female31EDF. TAT CHEONG84Staircase19 June 2022
15-618Female26BurmeseEDF. YIM LAI11Staircase19 June 2022
16-618Female31EDF. YIM LAI11Staircase19 June 2022
17-618Female31EDF. YIM LAI11Staircase19 June 2022
18-618Female29EDF. YIM LAI11Staircase19 June 2022
19-618Female33Macau, ChinaPraia Grande e PenhaEscada da Árvore35Elevator19 June 2022
20-618Female32FilipinoBaixa de MacauDaly Welcome Hotel5Elevator19 June 2022
21-618Male23Macau, ChinaBaixa da TaipaEDF. PALMER184Elevator19 June 2022
22-618Female30IndonesianZAPECENTRO INTERNACIONAL DE MACAU104Elevator19 June 2022
23-618Male89Macau, ChinaSan KioEDF. PARKWAY MANSION184Elevator19 June 2022
24-618Male36Universidade e Baía de Pac OnISLAND PARK1Staircase19 June 2022
25-618Female34ISLAND PARK1Staircase19 June 2022
26-618Male64San KioEDF. TAT CHEONG84Staircase19 June 2022
27-618Female65EDF. TAT CHEONG84Staircase19 June 2022
28-618Female36Horta e Costa e Ouvidor ArriagaCENTRO CHIU FOK28Staircase19 June 2022
29-618Female3CENTRO CHIU FOK28Staircase19 June 2022
30-618Male35Baixa da TaipaEDF. JARDIM DE WA BAO1212Elevator19 June 2022
31-618Female42IndianTamagnini BarbosaEDF. JARDIM IAT LAI1607Elevator19 June 2022
32-618Female43Chinese mainlandHorta e Costa e Ouvidor ArriagaRua de Fernão Mendes Pinto 43–61939Elevator19 June 2022
33-618Male62Macau, ChinaAreia Preta e Iao HonEDF. MAN LE8Staircase19 June 2022
34-618Female43San KioEDF. TAT CHEONG84Staircase19 June 2022
35-618Female3Baixa da TaipaEDF. LEI SENG1104Elevator20 June 2022
36-618Female33EDF. LEI SENG1104Elevator20 June 2022
37-618Female61Chinese mainlandEDF. LEI SENG1104Elevator20 June 2022
38-618Female40FilipinoPatane e São PauloRua de D. Belchior Carneiro 8–14129Staircase20 June 2022
39-618Female61Macau, ChinaSan KioEDF. YAN ON8Staircase20 June 2022
40-618Female34Móng Há e ReservatórioRampa dos Cavaleiros 8–8B340Elevator20 June 2022
41-618Female59Chinese mainlandParkview Garden 21 June 2022
42-618Female65Macau, ChinaFai Chi KeiVAI CHOI GARDEN451Elevator20 June 2022
43-618Female60Areia Preta e Iao HonEDF. MAN LE8Staircase19 June 2022
44-618Female37Chinese mainlandSan KioEDF. HOU VAN KENG73Staircase20 June 2022
45-618Male25Macau, ChinaDoca do LamauEDF. YOHO CITY CENTER102Elevator21 June 2022
46-618Female56EDF. YOHO CITY CENTER102Elevator21 June 2022
47-618Male16Jardins do Oceano e Taipa PequenaO PICO99Staircase21 June 2022
48-618Male32Areia Preta e Iao HonEDF. MAN LE8Staircase21 June 2022
49-618Female26FilipinoBaixa da TaipaEDF. HOI YEE FA YUEN (BLOCO 1)477Elevator21 June 2022
50-618Male26IndonesianZAPECENTRO INTERNACIONAL DE MACAU (TORRE VI)104Elevator21 June 2022
51-618Female47Macau, ChinaSan KioEDF. LEI FAT12Staircase21 June 2022
52-618Female57Doca do LamauEDF. NGA SAN266Elevator21 June 2022
53-618Male10Praia Grande e PenhaEDF. TAK WA/EDF. HIO FAI16Staircase21 June 2022
54-618Female44ZAPEEDF. NAM SENG97Elevator21 June 2022
55-618Female52Areia Preta e Iao HonEDF. JARDIM HOI KENG720Elevator21 June 2022
56-618Female74Fai Chi KeiEDIFÍCIO FAI IENG436Elevator21 June 2022
57-618Male29FilipinoPatane e São PauloEDF. CHENG HENG4Staircase22 June 2022
58-618Male35Macau, ChinaSan KioEDF. YEE CHEONG9Staircase22 June 2022
59-618Female30BurmeseHorta e Costa e Ouvidor ArriagaEDF. VA FAI167Elevator22 June 2022
60-618Female28Chinese mainlandBaixa de MacauHotel Lisboa 22 June 2022
61-618Female57Macau, ChinaTamagnini BarbosaEDF. JARDIM CIDADE136Elevator21 June 2022
62-618Female38Chinese mainlandAreia Preta e Iao HonEDF. U WA127Elevator21 June 2022
63-618Male49Macau, ChinaMóng Há e ReservatórioJARDINS SUN YICK1214Elevator22 June 2022
64-618Male30BurmeseSan KioEDF. SOK FAN18Staircase22 June 2022
65-618Female62Macau, ChinaNATAPUnshun New Village C2295Elevator21 June 2022
66-618Female42Chinese mainlandAreia Preta e Iao HonEDF. FEI CHOI KONG CHEONG563Elevator22 June 2022
67-618Male22Fai Chi KeiVAI CHOI GARDEN970Elevator22 June 2022
68-618Female43Macau, ChinaColoaneRua dos Bombaxes353Elevator22 June 2022
69-618Female31Doca do LamauEDF. YOHO CITY CENTER102Elevator22 June 2022
70-618Female54EDF. SAN LEI14Staircase22 June 2022
71-618Female71San KioEDF. LHUONG LOU12Staircase22 June 2022
72-618Female46Horta e Costa e Ouvidor ArriagaEDF. IONG TOU8Staircase22 June 2022
73-618Male36Chinese mainlandMóng Há e ReservatórioRampa dos Cavaleiros 8–8B340Elevator22 June 2022
74-618Female13Macau, ChinaEDF. PAK WAI 22 June 2022
75-618Female30Chinese mainlandAreia Preta e Iao HonEDF. CONCÓRDIA SQUARE298Elevator22 June 2022
76-618Female13Macau, ChinaSan KioEDF. TAT CHEONG84Staircase22 June 2022
77-618Female45Barra/ManducoEDF. SON HONG13Staircase22 June 2022
78-618Female45Chinese mainlandSan KioEDF. FAI WONG20Staircase22 June 2022
79-618Male50Areia Preta e Iao HonEDF. MAU TAN65Staircase22 June 2022
80-618Female24FilipinoBarra/ManducoEDF. VA LOK10Staircase22 June 2022
81-618Male34Macau, ChinaNATAPEDF. HOI PAN GARDEN126Elevator22 June 2022
82-618Female29BurmeseSan KioEDF. YIM LAI11Staircase22 June 2022
83-618Female39Macau, ChinaConselheiro Ferreira de AlmeidaEDF. SENG FAT12Staircase22 June 2022
84-618Female42VietnameseZAPECENTRO INTERNACIONAL DE MACAU104Elevator22 June 2022
85-618Female15Macau, ChinaBarra/ManducoEDF. HOI PAN126Elevator22 June 2022
86-618Male41NATAPLA MARINA549Elevator22 June 2022
87-618Female60Areia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO242Elevator22 June 2022
88-618Male63Chinese mainlandEDF. COMANDANTE PINTO RIBEIRO242Elevator22 June 2022
89-618Male45EDF. FEI CHOI KONG CHEONG563Elevator22 June 2022
90-618Male7Macau, ChinaEDF. MAN LE8Staircase22 June 2022
91-618Male38Baixa da TaipaRua de Nam Keng 20–42493Elevator22 June 2022
92-618Female42Areia Preta e Iao HonLOK CHI HOUSE702Elevator22 June 2022
93-618Female47Chinese mainlandNATAPEDF. U WA127Elevator22 June 2022
94-618Female29San KioEDF. YAN ON8Staircase22 June 2022
95-618Female37Macau, ChinaColoaneCHUK WAN HOU YUEN 22 June 2022
96-618Male30IndonesianZAPECENTRO INTERNACIONAL DE MACAU104Elevator22 June 2022
97-618Male29CENTRO INTERNACIONAL DE MACAU104Elevator22 June 2022
98-618Male29CENTRO INTERNACIONAL DE MACAU104Elevator22 June 2022
99-618Female51FilipinoSan KioEDF. PAK HENG125Staircase22 June 2022
100-618Male45NepaleseEDF. SOK FAN18Staircase22 June 2022
101-618Male73Macau, ChinaDoca do LamauKAI HOU COURT14Staircase23 June 2022
102-618Female48San KioEDF. LEI FAT12Staircase22 June 2022
103-618Male74Horta e Costa e Ouvidor ArriagaEDF. LUEN TAK12Staircase23 June 2022
104-618Male63Conselheiro Ferreira de AlmeidaEDF. SENG FAT12Staircase23 June 2022
105-618Female27Móng Há e ReservatórioTravessa de Má Káu Séak 58–106264Elevator23 June 2022
106-618Male34Chinese mainlandAreia Preta e Iao HonEDF. SON LEI56Staircase23 June 2022
107-618Male70San KioEDF. YAN ON8Staircase22 June 2022
108-618Female5Macau, ChinaEDF. YAN ON8Staircase22 June 2022
109-618Female21Chinese mainlandPatane e São PauloEDF. TONG WA6Staircase23 June 2022
110-618Female24Macau, ChinaTamagnini BarbosaEDF. JARDIM IAT LAI1607Elevator23 June 2022
111-618Male73Doca do LamauUnshun New Village C2295Elevator23 June 2022
112-618Female50Chinese mainlandAreia Preta e Iao HonEDF. FEI CHOI KONG CHEONG563Elevator23 June 2022
113-618Female48EDF. FEI CHOI KONG CHEONG563Elevator23 June 2022
114-618Male41Macau, ChinaEDF. LEI TIM638Elevator23 June 2022
115-618Female38San KioPátio da Quina 1–923Staircase23 June 2022
116-618Female40NAPE e Aterros da Baía da Praia GrandeTORRE LAGO PANORÂMICO896Elevator23 June 2022
117-618Male82San KioEDF. VENG KIN12Staircase23 June 2022
118-618Female41Móng Há e ReservatórioEDF. DRAGON TOWER19Elevator23 June 2022
119-618Female53Chinese mainlandFai Chi KeiEDF. FAI I5Staircase23 June 2022
120-618Male65Macau, ChinaPatane e São PauloEDF. CHEUNG WAN27Staircase23 June 2022
121-618Female33Cidade e Hipódromo da TaipaWAI HENG KOK538Elevator23 June 2022
122-618Female38San KioEDF. NG FOK45Staircase23 June 2022
123-618Female38Móng Há e ReservatórioEDF. KIN CHIT186Elevator23 June 2022
124-618Male26Chinese mainlandZAPECASA REAL HOTEL 23 June 2022
125-618Female49NATAPEDF. U WA127Elevator23 June 2022
126-618Female54EDF. U WA (BLOCO 12)127Elevator23 June 2022
127-618Male29NepaleseSan KioEDF. SOK FAN18Staircase23 June 2022
128-618Female62Chinese mainlandAreia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO (TORRE I)112Elevator23 June 2022
129-618Female65Macau, ChinaPatane e São PauloEDF. TIM CHUI10Staircase23 June 2022
130-618Male69San KioEDF. TAT CHEONG41Staircase23 June 2022
131-618Female27Chinese mainlandJu Long Xuan Restaurant Elevator23 June 2022
132-618Male36Macau, ChinaHorta e Costa e Ouvidor ArriagaEDF. VA FAI167Elevator23 June 2022
133-618Female2EDF. VA FAI167Elevator23 June 2022
134-618Female31BurmeseEDF. VA FAI167Elevator23 June 2022
135-618Male16Macau, ChinaAreia Preta e Iao HonLOK CHI HOUSE313Elevator24 June 2022
136-618Female63Chinese mainlandSan KioEDF. TAT CHEONG41Staircase23 June 2022
137-618Male40Macau, ChinaBaixa da TaipaTravessa da Povoação de Sam Ka 44–66147Elevator23 June 2022
138-618Female11Horta e Costa e Ouvidor ArriagaEDF. VA FAI167Elevator23 June 2022
139-618Male38VietnameseAreia Preta e Iao HonEDF. YAU SENG90Elevator23 June 2022
140-618Female32Barra/ManducoEDF. SON SENG69Staircase23 June 2022
141-618Female4Macau, ChinaAreia Preta e Iao Hon EDF. LOK FU GARDEN (LOK CHI HOUSE)313Elevator24 June 2022
142-618Female59Chinese mainlandMóng Há e ReservatórioRua Alegre 14–60353Elevator23 June 2022
143-618Female38Macau, ChinaHorta e Costa e Ouvidor ArriagaEDF. VA FAI167Elevator23 June 2022
144-618Male5Chinese mainlandSan KioEDF. TAT CHEONG41Staircase23 June 2022
145-618Female42EDF. TAK CHEONG6Staircase23 June 2022
146-618Male49Macau, ChinaColoaneEDF. LOK KUAN BLOCO IV Elevator23 June 2022
147-618Female34FilipinoSan KioEDF. PAK HENG125Staircase24 June 2022
148-618Female41Chinese mainlandNATAPEDF. U WA127Elevator23 June 2022
149-618Female34BurmeseSan KioEDF. TIM CHUI10Staircase23 June 2022
150-618Male40Macau, ChinaAreia Preta e Iao HonEDF. SON LEI56Staircase23 June 2022
151-618Male5Chinese mainlandNAPE e Aterros da Baía da Praia GrandeTORRE LAGO PANORÂMICO896Elevator24 June 2022
152-618Male47Macau, ChinaSan KioPátio da Quina 1–923Staircase24 June 2022
153-618Male33Chinese mainlandZAPECENTRO INTERNACIONAL DE MACAU (TORRE VI)104Elevator23 June 2022
154-618Male31NepaleseHorta e Costa e Ouvidor ArriagaEDF. VENG KEI30Staircase24 June 2022
155-618Female41Chinese mainlandNATAPEDF. U WA127Elevator23 June 2022
156-618Female52EDF. U WA127Elevator23 June 2022
157-618Female47Macau, ChinaPraia Grande e PenhaEDF. LUEN FAI20Staircase23 June 2022
158-618Male31FilipinoSan KioEDF. PAK HENG125Staircase24 June 2022
159-618Male53Macau, ChinaPatane e São PauloEDF. LAI HOU (BLOCO 4)88Staircase23 June 2022
160-618Female57Chinese mainlandTamagnini BarbosaKIAN FU SAN CHUEN150Elevator23 June 2022
161-618Male52Macau, ChinaBarra/ManducoEDF. SON HONG13Staircase24 June 2022
162-618Male35ZAPEEDF. LEI SAN118Elevator24 June 2022
163-618Male81Tamagnini BarbosaVAI YIN GARDEN264Elevator24 June 2022
164-618Male42Chinese mainlandSan KioEDF. HENG LONG6Staircase24 June 2022
165-618Male32Macau, ChinaFai Chi KeiEDF. YUET FA176Elevator24 June 2022
166-618Male30Areia Preta e Iao HonEDF. SON LEI56Staircase24 June 2022
167-618Male25EDF. SON LEI56Staircase24 June 2022
168-618Female33Chinese mainlandEDF. COMANDANTE PINTO RIBEIRO (TORRE I)112Elevator24 June 2022
169-618Female81Macau, ChinaTamagnini BarbosaVAI YIN GARDEN264Elevator24 June 2022
170-618Female47Chinese mainlandZAPEEDF. I TOU96Elevator24 June 2022
171-618Male74Macau, ChinaDoca do LamauEDF. NGA SAN241Elevator24 June 2022
172-618Male59Chinese mainlandVAN SION SON CHUN2295Elevator24 June 2022
173-618Female58Macau, ChinaAreia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO (TORRE I)112Elevator23 June 2022
174-618Female9Chinese mainlandSan KioEDF. TAT CHEONG41Staircase23 June 2022
175-618Female38Tamagnini BarbosaEDF. CHUI YI164Staircase24 June 2022
176-618Female33San KioEDF. YAN ON8Staircase24 June 2022
177-618Female42VietnameseAreia Preta e Iao HonEDF. MAU TAN190Staircase24 June 2022
178-618Male53Macau, ChinaBarra/ManducoEDF. KUONG FAT22Staircase24 June 2022
179-618Female33Areia Preta e Iao HonEDF. MAN LE8Staircase24 June 2022
180-618Male66Baixa da TaipaEDF. LEI SENG209Elevator24 June 2022
181-618Female66Chinese mainlandHorta e Costa e Ouvidor ArriagaEDF. VA FAI167Elevator24 June 2022
182-618Female20Macau, ChinaTamagnini BarbosaJARDIM DO MAR DO SUL250Elevator23 June 2022
183-618Female42Baixa da TaipaSAN SAI KAI FA UN251Elevator23 June 2022
184-618Male66Horta e Costa e Ouvidor ArriagaEDF. VA FAI167Elevator22 June 2022
185-618Female35Chinese mainlandEDF. VA FAI167Elevator23 June 2022
186-618Male39Macau, ChinaSan KioEDF. IONG LONG22Staircase23 June 2022
187-618Male70Baixa de MacauEDF. POU KA95Staircase24 June 2022
188-618Female39EDF. MAN YI5Staircase24 June 2022
189-618Female29Chinese mainlandEDF. CHEONG VA13Staircase24 June 2022
190-618Male33FilipinoBarra/ManducoEDF. HOU FAT5Staircase24 June 2022
191-618Female3Macau, ChinaSan KioEDF. IONG LONG22Staircase24 June 2022
192-618Female68Chinese mainlandHorta e Costa e Ouvidor ArriagaEDF. YUE XIU GARDENS(BLOCO 1)280Elevator24 June 2022
193-618Male24BurmeseSan KioEDF. TIM CHUI40Staircase24 June 2022
194-618Female38Macau, ChinaNATAPLA MARINA(BLOCO 4)549Elevator24 June 2022
195-618Female52Chinese mainlandBarra/ManducoEDF. KUONG FAT22Staircase25 June 2022
196-618Male40ZAPEWALDO HOTEL & CASINO Elevator24 June 2022
197-618Male52Areia Preta e Iao HonEDF. SON LEI56Staircase23 June 2022
198-618Female50Barra/ManducoPENG KEI EDF21Staircase25 June 2022
199-618Male67Macau, ChinaAreia Preta e Iao HonEDF. SON LEI56Staircase25 June 2022
200-618Male60ColoaneEDF. ON SON270Elevator25 June 2022
201-618Female26Patane e São PauloEDF. TAT SAN9Staircase25 June 2022
202-618Male50Ilha VerdeEDIFICIO ILHA VERDE2356Elevator25 June 2022
203-618Female49Chinese mainlandAreia Preta e Iao HonEDF. SAN MEI ON117Staircase25 June 2022
204-618Male43Macau, ChinaColoaneEDF. LOK KUAN BLOCO V Elevator25 June 2022
205-618Male39NATAPEDF. HOI PAN GARDEN (BLOCO 10)126Elevator25 June 2022
206-618Male53Chinese mainlandAreia Preta e Iao HonEDF. SON LEI56Staircase25 June 2022
207-618Female33IndonesianBaixa de MacauEDF. IU SON5Staircase25 June 2022
208-618Female59Macau, ChinaConselheiro Ferreira de AlmeidaEDF. POU LEI24Staircase25 June 2022
209-618Female54Fai Chi KeiEDF. WENG HOI88Elevator24 June 2022
210-618Female30VietnameseBaixa de MacauEDF. NGA WA11Staircase25 June 2022
211-618Female38FilipinoSan KioEDF. SOK FAN18Staircase24 June 2022
212-618Male19Macau, ChinaIlha VerdeEDF. CHENG CHOI44Staircase23 June 2022
213-618Male46Baixa de MacauEDF. MAN Y5Staircase25 June 2022
214-618Male62Chinese mainlandAreia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO (TORRE I)112Elevator23 June 2022
215-618Male3Macau, ChinaBaixa de MacauEDF. MAN Y5Staircase25 June 2022
216-618Female39FilipinoEDF. MAN Y5Staircase25 June 2022
217-618Male3Macau, ChinaEDF. MAN Y5Staircase25 June 2022
218-618Male58Areia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO (TORRE I)112Elevator23 June 2022
219-618Female24Chinese mainlandBaixa da TaipaEDF. HOI YEE FA YUEN (BLOCO 3)151Elevator25 June 2022
220-618Female36Macau, ChinaAreia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO (TORRE I)112Elevator23 June 2022
221-618Male51Chinese mainlandEDF. SON LEI56Staircase25 June 2022
222-618Male37EDF. SON LEI56Staircase25 June 2022
223-618Male52Macau, ChinaPraia Grande e PenhaEDF. LUEN FAI20Staircase25 June 2022
224-618Female29BurmeseSan KioEDF. YAU KEI16Staircase25 June 2022
225-618Male27NepaleseHorta e Costa e Ouvidor ArriagaEDF. VENG KEI30Staircase25 June 2022
226-618Female35FilipinoPatane e São PauloChing Hing Mansion4Staircase25 June 2022
227-618Male31Chinese mainlandAreia Preta e Iao HonEDF. HONG TAI56Staircase24 June 2022
228-618Male54FilipinoEstrada da Areia Preta 9–13C23Staircase25 June 2022
229-618Female49Macau, ChinaPatane e São PauloEDF. TAT SAN9Staircase25 June 2022
230-618Male13Conselheiro Ferreira de AlmeidaEDF. ESPERANÇA17Staircase25 June 2022
231-618Male63Chinese mainlandMóng Há e ReservatórioXing Hua New Estate353Elevator25 June 2022
232-618Female32BurmeseSan KioEDF. TIM CHUI40Staircase25 June 2022
233-618Male36Macau, ChinaEDF. CHONG KIO19Staircase25 June 2022
234-618Female57Chinese mainlandDoca do LamauVAN SION SON CHUN2295Elevator25 June 2022
235-618Female29Baixa da TaipaSAN SAI KAI FA UN251Elevator25 June 2022
236-618Female65Macau, ChinaAreia Preta e Iao HonEDF. LEI TIM160Elevator25 June 2022
237-618Female15EDF. LEI TIM160Elevator25 June 2022
238-618Female23Barra/ManducoEDF. SON HONG13Staircase25 June 2022
239-618Female23EDF. SON HONG13Staircase25 June 2022
240-618Female5Areia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO (TORRE I)112Elevator25 June 2022
241-618Female35FilipinoEDF. COMANDANTE PINTO RIBEIRO112Elevator25 June 2022
242-618Female33Macau, ChinaEDF. COMANDANTE PINTO RIBEIRO (TORRE I)112Elevator25 June 2022
243-618Female53Baixa de MacauEDF. POU KA95Staircase25 June 2022
244-618Female34Chinese mainlandEDF. SENG YUE21Staircase25 June 2022
245-618Male31Macau, ChinaEDF. POU KA95Staircase25 June 2022
246-618Male2EDF. POU KA95Staircase25 June 2022
247-618Female75EDF. POU KA95Staircase25 June 2022
248-618Female31EDF. POU KA95Staircase25 June 2022
249-618Female59Tamagnini BarbosaJARDIM DO MAR DO SUL250Elevator25 June 2022
250-618Female32BurmeseHorta e Costa e Ouvidor ArriagaEDF. KAM LOK (BLOCOS I)15Staircase25 June 2022
251-618Female89Macau, ChinaSan KioEDF. YAN ON24Staircase25 June 2022
252-618Female46Patane e São Paulo 25 June 2022
253-618Male51Chinese mainlandBaixa de MacauRua do Campo 56–9652Elevator24 June 2022
254-618Female22Macau, ChinaHorta e Costa e Ouvidor ArriagaEDF. TAI PENG12Staircase25 June 2022
255-618Male32FilipinoBarra/ManducoEDF. LAI HENG25Staircase25 June 2022
256-618Female34Macau, ChinaDoca do LamauUnshun New Village B2295Elevator25 June 2022
257-618Male53Chinese mainlandBaixa de MacauRua do Campo 56–9652Elevator25 June 2022
258-618Female27Macau, ChinaBaixa da TaipaSAN SAI KAI FA UN251Elevator26 June 2022
259-618Male38Chinese mainlandAreia Preta e Iao HonEDF. SON LEI56Staircase25 June 2022
260-618Male45Macau, ChinaEDF. LOK FU GARDEN702Staircase26 June 2022
261-618Female63Doca do LamauKAI HOU COURT14Staircase25 June 2022
262-618Female10Chinese mainlandSan KioEDF. HOU VAN KENG73Staircase25 June 2022
263-618Male37NepaleseEDF. SOK FAN18Staircase24 June 2022
264-618Male10Macau, ChinaAreia Preta e Iao HonEDF. LEI TIM638Elevator25 June 2022
265-618Female31Chinese mainlandSan KioEDF. WENG HOI6Staircase25 June 2022
266-618Female70Horta e Costa e Ouvidor ArriagaEDF. LUEN TAK12Staircase25 June 2022
267-618Female5Areia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO242Elevator26 June 2022
268-618Female33Macau, ChinaBaixa da TaipaEDF. DO LAGO400Elevator26 June 2022
269-618Male59Chinese mainlandAreia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO242Elevator26 June 2022
271-618Male65EDF. COMANDANTE PINTO RIBEIRO242Elevator26 June 2022
272-618Female59EDF. NAM FAI431Elevator26 June 2022
273-618Female40Barra/ManducoEDF. SI KAI7Staircase26 June 2022
274-618Male43ColoaneEDF. KOI NGA250Elevator26 June 2022
275-618Male43Areia Preta e Iao HonEDF. HONG TAI56Staircase26 June 2022
276-618Male31VietnameseBaixa de MacauRua do Campo 56–9652Elevator26 June 2022
277-618Male39Chinese mainlandRua do Campo 56–9652Elevator26 June 2022
278-618Female72Macau, ChinaColoane EDF. LOK KUAN BLOCO V 26 June 2022
279-618Female35Areia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO242Elevator26 June 2022
280-618Male35NATAPLA MARINA339Elevator26 June 2022
281-618Female8LA MARINA339Elevator26 June 2022
282-618Male50Baixa da TaipaEDF. NOVA TAIPA GARDEN (BLOCO 24-LÍRIO)377Elevator25 June 2022
283-618Female34Areia Preta e Iao HonEDF. LEI TIM638Elevator26 June 2022
284-618Male43Chinese mainlandEDF. MAU TAN23Staircase26 June 2022
285-618Female48FilipinoJardins do Oceano e Taipa PequenaJARDINS DO OCEANO (APRICOT COURT, HIBISCUS COURT)337Elevator27 June 2022
286-618Female57Chinese mainlandAreia Preta e Iao HonEDF. VILLA BELA212Elevator26 June 2022
287-618Female28BurmeseSan KioEDF. TIM CHUI 26 June 2022
288-618Female58Macau, ChinaAreia Preta e Iao HonEDF. VILLA BELA212Elevator26 June 2022
289-618Male35NATAPEDF. KAM HOI SAN128Elevator26 June 2022
290-618Female55Chinese mainlandAreia Preta e Iao HonEDF. HONG TAI56Staircase26 June 2022
291-618Male44EDF. SON LEI56Staircase26 June 2022
292-618Female34Macau, ChinaNATAPEDF. HOI PAN GARDEN128Elevator26 June 2022
293-618Female42Chinese mainlandHorta e Costa e Ouvidor ArriagaEDF. VENG CHAN16Staircase26 June 2022
294-618Female74Macau, ChinaAreia Preta e Iao HonEDF. SON LEI56Staircase26 June 2022
295-618Male68Patane e São PauloEDF. TAT SAN9Staircase26 June 2022
296-618Male26Chinese mainlandFai Chi KeiEDF. YUET TAK181Elevator26 June 2022
297-618Female65Macau, ChinaPatane e São PauloEDF. I SON17Staircase26 June 2022
298-618Male3Horta e Costa e Ouvidor ArriagaRua de Fernão Mendes Pinto 43–61939Elevator26 June 2022
299-618Female36Baixa de MacauRua das Estalagens3Staircase26 June 2022
316-618Male28Barra/ManducoI FONG SON SAN CHUN130Staircase26 June 2022
323-618Male61EDF. KUAN HONG69Elevator26 June 2022
324-618Female50Baixa da TaipaRua de Nam Keng 20–42493Elevator26 June 2022
325-618Female30Tamagnini BarbosaEDF. JARDIM IAT LAI1607Elevator26 June 2022
326-618Female24Chinese mainlandNATAPEDF. U WA128Elevator26 June 2022
327-618Female58Macau, ChinaZAPEEDF. I TOU217Elevator26 June 2022
328-618Female35Chinese mainlandEDF. I TOU217Elevator26 June 2022
329-618Female5Macau, ChinaBaixa da TaipaRua de Nam Keng 20–42493Elevator26 June 2022
330-618Male33Doca do LamauEDF. NGA SAN266Elevator26 June 2022
331-618Male40Chinese mainlandZAPECENTRO INTERNACIONAL DE MACAU (TORRE VI)104Elevator26 June 2022
332-618Female60Macau, ChinaBarra/ManducoEDF. KUAN ON13Staircase26 June 2022
333-618Male32San KioEDF. TAT CHEONG41Staircase26 June 2022
334-618Female6Chinese mainlandAreia Preta e Iao HonRua da Saúde 8–42D200Elevator26 June 2022
335-618Male32San KioRua do Rosário
Rua Heng Long
6Staircase26 June 2022
336-618Female60Areia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO242Elevator26 June 2022
337-618Female1Macau, ChinaFai Chi KeiEDF. VANG KEI501Elevator27 June 2022
338-618Male51Chinese mainlandBaixa da TaipaRua dos Hortelãos 26 June 2022
339-618Female51Macau, ChinaAreia Preta e Iao HonEDF. SON LEI56Staircase26 June 2022
340-618Male59EDF. SON LEI56Staircase26 June 2022
341-618Male17EDF. SON LEI56Staircase26 June 2022
342-618Female31Baixa da TaipaEDF.LEI SENG1104Elevator26 June 2022
343-618Female45FilipinoAreia Preta e Iao HonEstrada da Areia Preta 9–13C23Elevator26 June 2022
344-618Female10Chinese mainlandEDF. COMANDANTE PINTO RIBEIRO242Elevator26 June 2022
345-618Male2EDF. COMANDANTE PINTO RIBEIRO242Elevator26 June 2022
346-618Female40EDF. COMANDANTE PINTO RIBEIRO242Elevator26 June 2022
347-618Male35Macau, ChinaMóng Há e ReservatórioFU PO GARDEN152Elevator26 June 2022
348-618Female43FilipinoAreia Preta e Iao HonEstrada da Areia Preta 9–13C23Elevator26 June 2022
349-618Female11Chinese mainlandEDF. COMANDANTE PINTO RIBEIRO242Elevator26 June 2022
350-618Female46NATAPEDF.U WA128Elevator26 June 2022
351-618Female3Macau, ChinaAreia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO242Elevator26 June 2022
355-618Male63Doca do LamauVILA NOVA YUNSHUN2295Elevator26 June 2022
356-618Male49Chinese mainlandBaixa de MacauRua de Cinco de Outubro7Staircase26 June 2022
357-618Female62Macau, ChinaDoca do LamauVILA NOVA YUNSHUN2295Elevator26 June 2022
358-618Female50Patane e São PauloEDF.NGA KENG256Elevator26 June 2022
359-618Female58Barra/ManducoEDF. CHONG KIU7Staircase26 June 2022
360-618Male37Chinese mainlandRua do Dr. Lourenço Pereira Marques 75–75869Elevator26 June 2022
361-618Male38Macau, ChinaHorta e Costa e Ouvidor ArriagaEDF. VENG SENG30Staircase25 June 2022
362-618Female50FilipinoBarra/ManducoEDF. LAI HENG25Staircase26 June 2022
363-618Male46EDF. LAI HENG25Staircase26 June 2022
364-618Male39Patane e São PauloRua de D. Belchior Carneiro 8–14129Staircase27 June 2022
365-618Female46Barra/ManducoEDF.LAI HENG25Staircase26 June 2022
366-618Female56Chinese mainlandColoaneEDF. KOI NGA250Elevator26 June 2022
367-618Female27Macau, ChinaNATAPEDF.KAM HOI SAN(BLOCO 10)128Elevator27 June 2022
368-618Male35Chinese mainlandAreia Preta e Iao HonEDF. HONG TAI56Staircase27 June 2022
369-618Female61Macau, ChinaNATAPEDF. POLYTEC GARDEN1460Elevator27 June 2022
370-618Female48Chinese mainlandAreia Preta e Iao HonEDF.SON LEI56Staircase18 June 2022
371-618Female34Horta e Costa e Ouvidor ArriagaEDF.VENG CHAN16Staircase27 June 2022
372-618Female84Macau, ChinaSan KioEDF. SOK FAN18Staircase27 June 2022
373-618Male47Areia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO242Elevator27 June 2022
374-618Female28BurmeseSan KioEDF. TIM CHUI 26 June 2022
375-618Male11Chinese mainlandBarra/ManducoRua do Dr. Lourenço Pereira Marques 75–75869Elevator26 June 2022
376-618Female24Macau, ChinaBaixa de MacauEDF. KIU WAI20Staircase27 June 2022
377-618Male33Chinese mainlandAreia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO (TORRE I)242Elevator28 June 2022
378-618Female32EDF.HONG TAI56Staircase27 June 2022
379-618Female50Macau, ChinaNATAPEDF. U WA (BLOCO 12)127Elevator27 June 2022
380-618Female25Chinese mainlandEDF. U WA127Elevator27 June 2022
381-618Male34Ilha VerdeEDF. KUAI TAK24Staircase27 June 2022
382-618Female36Macau, ChinaHorta e Costa e Ouvidor ArriagaEDF. TIN FOOK17Staircase27 June 2022
384-618Female8NATAPEDF. KAM HOI SAN128Elevator27 June 2022
385-618Female47Chinese mainlandHorta e Costa e Ouvidor ArriagaEDF. VENG CHAN16Staircase27 June 2022
386-618Female11Macau, ChinaNATAPEDF. KAM HOI SAN128Elevator27 June 2022
387-618Female62Chinese mainlandAreia Preta e Iao HonKONG HOI213Staircase27 June 2022
388-618Male38IndianPatane e São PauloEDF. NGAI IN KUOK13Staircase27 June 2022
389-618Male48Macau, ChinaTamagnini BarbosaCHOI FAI KOK363Elevator27 June 2022
391-618Male36Conselheiro Ferreira de AlmeidaEDF. ESPERANÇA17Staircase27 June 2022
392-618Male24ColoaneRua dos Bombaxes353Elevator27 June 2022
393-618Male48Ilha VerdeEDIFICIO ILHA VERDE2356Elevator27 June 2022
394-618Female45VietnameseAreia Preta e Iao HonEDF.SAN MEI ON117Staircase27 June 2022
395-618Female45Chinese mainlandEDF.SAN MEI ON117Staircase27 June 2022
396-618Female57Macau, ChinaEDF. SON LEI56Staircase27 June 2022
397-618Female67EDF. SON LEI56Staircase27 June 2022
398-618Female25Chinese mainlandSan KioEDF. IONG LONG22Staircase27 June 2022
399-618Male38NAPE e Aterros da Baía da Praia GrandeTORRE LAGO PANORÂMICO896Elevator27 June 2022
400-618Male10Baixa de MacauEDF. CHEONG VA13Staircase27 June 2022
401-618Female37FilipinoSan KioEDF.YAU KEI16Staircase27 June 2022
402-618Male59Macau, ChinaBaixa de MacauEDF. POU KA95Staircase27 June 2022
404-618Male51Chinese mainlandAreia Preta e Iao HonEDF. HONG TAI56Staircase27 June 2022
405-618Female53Macau, ChinaNATAPNAM WA SAN CHUN133Elevator27 June 2022
406-618Male65Barra/ManducoEDF. KWAN ON11Staircase27 June 2022
407-618Female31NATAPEDF.KAM HOI SAN(BLOCO 10)128Elevator27 June 2022
408-618Female42San KioEDF. ULTRAMAR29Staircase27 June 2022
409-618Male34NATAPRua da Pérola Oriental 33–101207Elevator27 June 2022
410-618Male67Chinese mainlandAreia Preta e Iao HonEDF. LEI TIM638Elevator27 June 2022
412-618Female37ZAPEEDF. I TOU217Elevator27 June 2022
413-618Male34Macau, ChinaNATAPEDF. JARDIM KONG FOK CHEONG1254Elevator27 June 2022
414-618Female32Barra/ManducoEDF. KWAN ON11Staircase27 June 2022
415-618Female32Chinese mainlandAreia Preta e Iao HonEDF. HONG TAI56Staircase27 June 2022
417-618Male53Macau, ChinaNATAPNAM WA SAN CHUN133Elevator28 June 2022
418-618Female67Baixa de MacauEDF. POU KA95Staircase27 June 2022
419-618Female27Praia Grande e PenhaEDF. LUEN FAI20Staircase28 June 2022
420-618Male32Chinese mainlandAreia Preta e Iao HonEDF. HENG LONG20Staircase27 June 2022
421-618Female3Macau, ChinaIlha VerdeTravessa do Laboratório 23–27493Elevator28 June 2022
422-618Male37EDF. MAYFAIR GARDEN1037Elevator28 June 2022
423-618Female65Baixa da TaipaEDF. DO LAGO400Elevator28 June 2022
424-618Female6EDF. DO LAGO400Elevator28 June 2022
425-618Female35Chinese mainlandHorta e Costa e Ouvidor ArriagaEDF. VENG CHAN16Staircase28 June 2022
426-618Female64Macau, ChinaJardins do Oceano e Taipa PequenaJARDINS DO OCEANO253Elevator28 June 2022
427-618Male72Areia Preta e Iao HonEDF. SAN MEI ON117Staircase28 June 2022
428-618Female32Chinese mainlandSan KioEDF. VENG SENG22Staircase28 June 2022
429-618Female36Barra/ManducoRua do Dr. Lourenço Pereira Marques 75–75869Elevator27 June 2022
430-618Male49Macau, ChinaNATAPEDF. U WA127Elevator28 June 2022
431-618Male84Baixa de MacauTravessa do Paralelo 10–2624Staircase28 June 2022
432-618Male31Barra/ManducoEDF. TAI MEI7Staircase28 June 2022
433-618Female60Jardins do Oceano e Taipa Pequena JARDINS DO OCEANO (BAUHINIA COURT)335Elevator28 June 2022
434-618Male47Chinese mainlandAreia Preta e Iao HonEDF. HONG TAI56Staircase28 June 2022
435-618Female35Macau, ChinaSan KioEDF. KAI KEI COURT288Elevator28 June 2022
436-618Female38ColoaneEDF. ON SON96Elevator28 June 2022
437-618Female56Baixa de MacauEDF. TONG MEI24Staircase28 June 2022
438-618Female12CENTRO COMERCIAL MASTER74Staircase28 June 2022
439-618Female35CENTRO COMERCIAL MASTER74Staircase28 June 2022
440-618Female31IndianNATAPEDF. KAM HOI SAN128Elevator28 June 2022
441-618Male28Chinese mainlandIlha VerdeEDF. MEI KUI KUONG CHEONG (FASE 2) (BLOCO 2-EDF. SUNRISE COURT)547Elevator28 June 2022
442-618Male36FilipinoBaixa de MacauEDF. MAN SENG 28 June 2022
443-618Female65Macau, ChinaUniversidade e Baía de Pac OnEDF. IAT SENG 28 June 2022
444-618Female45Baixa de MacauEDF. POU KA95Staircase28 June 2022
445-618Female58Barra/ManducoEDF. KWAN ON140Staircase28 June 2022
446-618Male29EDF. KWAN ON140Staircase28 June 2022
447-618Male51Chinese mainlandAreia Preta e Iao HonEDF. SAN MEI ON117Staircase28 June 2022
448-618Male32Conselheiro Ferreira de AlmeidaEDF. TIM FOK5Staircase28 June 2022
449-618Female69Macau, ChinaColoaneEDF.IP HENG(BLOCO 8)7Elevator28 June 2022
450-618Male8NATAPLA MARINA549Elevator28 June 2022
451-618Male40Doca do LamauAvenida Marginal do Lam Mau 369–4412976Elevator28 June 2022
452-618Male69ColoaneEDF.LOK KUAN BLOCO V4672Elevator28 June 2022
453-618Male24Chinese mainlandSan KioEDF. KAI CHEONG10Staircase28 June 2022
454-618Male47Areia Preta e Iao HonEDF. SAN MEI ON117Staircase28 June 2022
455-618Female49EDF. SAN MEI ON117Staircase28 June 2022
456-618Female25Móng Há e ReservatórioEDF. HANTEC815Elevator28 June 2022
457-618Male46Areia Preta e Iao HonEDF. SAN MEI ON117Staircase28 June 2022
458-618Female29Baixa de MacauFU VA KOK
EDF. FU WAH COURT
6Staircase28 June 2022
459-618Male53Areia Preta e Iao HonEDF. SAN MEI ON117Staircase28 June 2022
460-618Male27Macau, ChinaNATAPNAM WA SAN CHUN133Elevator28 June 2022
461-618Male50Chinese mainlandAreia Preta e Iao HonEDF. SAN MEI ON117Staircase28 June 2022
462-618Female21Macau, ChinaNATAPNAM WA SAN CHUN133Elevator28 June 2022
463-618Female73Horta e Costa e Ouvidor ArriagaHEONG LAM SAN CHUN122Elevator28 June 2022
466-618Male65Tamagnini BarbosaKIAN FU SAN CHUEN904Elevator28 June 2022
467-618Female32KIAN FU SAN CHUEN904Elevator27 June 2022
468-618Female3Ilha VerdeEDF. CHENG CHOI44Staircase28 June 2022
469-618Female59Chinese mainlandAreia Preta e Iao HonEDF. COMANDANTE PINTO RIBEIRO242Elevator28 June 2022
470-618Female47Horta e Costa e Ouvidor ArriagaEDF. VENG CHAN16Staircase28 June 2022
471-618Male30Macau, ChinaColoaneRua dos Bombaxes353Elevator28 June 2022
472-618Male34Chinese mainlandAreia Preta e Iao HonEDF. HENG LONG64Staircase28 June 2022
473-618Male45Macau, ChinaEDF. VILLA BELA
EDF. DO JARDIM KAM SAU
212Elevator28 June 2022
474-618Female31Barra/ManducoEDF. TAK CHEONG24Staircase25 June 2022
475-618Female70Chinese mainlandBaixa de MacauTravessa do Paralelo 10–2624Staircase28 June 2022
476-618Male43Macau, ChinaTravessa do Paralelo 10–2624Staircase28 June 2022
477-618Female29Doca do LamauLONG HOU FONG263Elevator28 June 2022
478-618Male12NATAPEDF. KAM HOI SAN128Elevator28 June 2022
479-618Female29EDF.U WA(BLOCO12)127Elevator28 June 2022
480-618Male29San KioEDF. VENG SENG22Staircase28 June 2022
481-618Male26VietnameseAreia Preta e Iao HonEDF. SENG YEE60Staircase28 June 2022
482-618Female62Macau, ChinaBaixa de MacauEDF. SENG YUE
EDF. SENG YU
21Staircase28 June 2022
483-618Male60Fai Chi KeiEDF. YUET FA176Elevator28 June 2022
484-618Female26Horta e Costa e Ouvidor ArriagaTravessa de Coelho do Amaral 4–8224Elevator28 June 2022
485-618Female47Chinese mainlandEDF. VENG CHAN16Staircase28 June 2022
486-618Male55Areia Preta e Iao HonEDF. SAN MEI ON117Staircase28 June 2022
487-618Female65Macau, ChinaConselheiro Ferreira de AlmeidaEDF. TIM FOK5Staircase29 June 2022
488-618Female72Baixa de MacauEDF. POU KA95Staircase28 June 2022
489-618Male56Chinese mainlandAreia Preta e Iao HonEDF. SON LEI56Staircase28 June 2022
490-618Female43Macau, ChinaBaixa de MacauEDF. POU KA95Staircase28 June 2022
491-618Male32Patane e São PauloEDF. CHEUNG WAN27Staircase28 June 2022
492-618Female14Areia Preta e Iao HonEDF. HONG TAI56Staircase29 June 2022
493-618Female31BurmeseSan KioEDF. TIM CHUI Staircase28 June 2022
494-618Female39Macau, ChinaAreia Preta e Iao HonEDF. HONG TAI56Staircase28 June 2022
495-618Male72Conselheiro Ferreira de AlmeidaEDF. TIM FOK5Staircase28 June 2022
496-618Female36EDF. TIM FOK5Staircase28 June 2022
497-618Female65Tamagnini BarbosaTAMAGNINI BARBOSA Elevator29 June 2022
498-618Male52Chinese mainlandAreia Preta e Iao HonEDF. SON LEI56Staircase29 June 2022
499-618Female45Macau, ChinaHorta e Costa e Ouvidor ArriagaEDF. HANG WAN KOK (BLOCO A)516Elevator29 June 2022
500-618Male27NATAPEDF.U WA(BLOCO12)127Elevator28 June 2022
Cases 270, 300-315, 317-322, 352-354, 383, 390, 403, 411, 416, and 464-465 were all found in controlled isolation. The impact on the social side is small, so the table does not list this location. Statistical area is the division set in the “Statistical Yearbook” produced by the Statistics and Census Bureau of the Macau Special Administrative Region Government. Currently, Macau has a total of 23 Statistical areas.

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Figure 1. Experimental research materials and study area.
Figure 1. Experimental research materials and study area.
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Figure 2. Research samples.
Figure 2. Research samples.
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Figure 3. CGAN principle.
Figure 3. CGAN principle.
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Figure 4. Loss values for during training.
Figure 4. Loss values for during training.
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Figure 5. Model training process.
Figure 5. Model training process.
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Figure 6. Results comparison of different types of urban forms. A1, B1, and C1 are the actual epidemic distribution maps; A2, B2, and C2 are the different types of urban morphology maps in Macau; A3, B3, and C3 are the results predicted by the model of A1, B1, and C1.
Figure 6. Results comparison of different types of urban forms. A1, B1, and C1 are the actual epidemic distribution maps; A2, B2, and C2 are the different types of urban morphology maps in Macau; A3, B3, and C3 are the results predicted by the model of A1, B1, and C1.
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Figure 7. The result of deriving the urban morphology from the heat map of the epidemic distribution in Taipa. A1, B1, C1, and D1 are different slices of the actual epidemic distribution in Taipa, Macau. A2, B2, C2, and D2 are the results predicted by A1, B1, C1, and D1 through the model.
Figure 7. The result of deriving the urban morphology from the heat map of the epidemic distribution in Taipa. A1, B1, C1, and D1 are different slices of the actual epidemic distribution in Taipa, Macau. A2, B2, C2, and D2 are the results predicted by A1, B1, C1, and D1 through the model.
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Figure 8. Assumption of the epidemic distribution to derive the results of urban form. A1, B1, C1, D1, E1, and F1 are the researchers’ assumptions about the distribution of different outbreaks. A1 and B1 represent different epidemic distribution intensities. C1, D1 represent different distribution shapes of the epidemic. E1 and F1 represent different positive and negative shapes of the epidemic distribution. A2, B2, C2, D2, E2, F2 are the results of A1, B1, C1, D1, E1, and F1 predicted by the model.
Figure 8. Assumption of the epidemic distribution to derive the results of urban form. A1, B1, C1, D1, E1, and F1 are the researchers’ assumptions about the distribution of different outbreaks. A1 and B1 represent different epidemic distribution intensities. C1, D1 represent different distribution shapes of the epidemic. E1 and F1 represent different positive and negative shapes of the epidemic distribution. A2, B2, C2, D2, E2, F2 are the results of A1, B1, C1, D1, E1, and F1 predicted by the model.
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Chen, Y.; Zheng, L.; Song, J.; Huang, L.; Zheng, J. Revealing the Impact of Urban Form on COVID-19 Based on Machine Learning: Taking Macau as an Example. Sustainability 2022, 14, 14341. https://doi.org/10.3390/su142114341

AMA Style

Chen Y, Zheng L, Song J, Huang L, Zheng J. Revealing the Impact of Urban Form on COVID-19 Based on Machine Learning: Taking Macau as an Example. Sustainability. 2022; 14(21):14341. https://doi.org/10.3390/su142114341

Chicago/Turabian Style

Chen, Yile, Liang Zheng, Junxin Song, Linsheng Huang, and Jianyi Zheng. 2022. "Revealing the Impact of Urban Form on COVID-19 Based on Machine Learning: Taking Macau as an Example" Sustainability 14, no. 21: 14341. https://doi.org/10.3390/su142114341

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