This paper presents an improved SEIR (Susceptible-Exposed-Infectious-Recovered) model to simulate the spread of infectious diseases in urban environments, taking into account environmental factors such as population density, mobility, and air quality. By applying the model to a range of urban case studies, we analyze the impact of each factor on transmission rates and propose strategies for optimal intervention. The results show that cities with higher levels of mobility and pollution experience faster disease spread, which requires targeted health policies.
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