The Impact of High-Density Urban Wind Environments on the Distribution of COVID-19 Based on Machine Learning: A Case Study of Macau

Abstract

The COVID-19 epidemic has become a global challenge, and the urban wind environment, as an important part of urban spaces, may play a key role in the spread of the virus. Therefore, an in-depth understanding of the impact of urban wind environments on the spread of COVID-19 is of great significance for formulating effective prevention and control strategies. This paper adopts the conditional generative confrontation network (CGAN) method, uses simulated urban wind environment data and COVID-19 distribution data for machine training, and trains a model to predict the distribution probability of COVID-19 under different wind environments. Through the application of this model, the relationship between the urban wind environment and the spread of COVID-19 can be studied in depth. This study found that: (1) there are significant differences in the different types of wind environments and COVID-19, and areas with high building density are more susceptible to COVID-19 hotspots; (2) the distribution of COVID-19 hotspots in building complexes and the characteristics of the building itself are correlated; and (3) similarly, the building area influences the spread of COVID-19. In response to long COVID-19 or residential area planning in the post-epidemic era, three principles can be considered for high-density cities such as Macau: building houses on the northeast side of the mountain; making residential building layouts of “strip” or “rectangular” design; and ensuring that the long side of the building faces southeast (the windward side). (4) It is recommended that the overall wind speed around the building be greater than 2.91 m/s, and the optimal wind speed is between 4.85 and 8.73 m/s. This finding provides valuable information for urban planning and public health departments to help formulate more effective epidemic prevention and control strategies. This study uses machine learning methods to reveal the impact of urban wind environments on the distribution of COVID-19 and provides important insights into urban planning and public health strategy development.