Since COVID-19 spread rapidly worldwide, many countries have experienced significant growth in the number of confirmed cases and deaths. Earlier studies have examined various factors that may contribute to the contagion rate of COVID-19, such as air pollution, smoking, humidity, and temperature. As there is a lack of studies at the neighborhood-level detailing the spatial settings and built environment attributes, this study explored the variations in the size of the COVID-19 confirmed case clusters across the central district Huangzhou in the prefecture of Huanggang adjoining with Wuhan. Infectious cases in the initial outbreak of COVID-19 were identified geographically through GIS mapping. The hypothetic relationships have been investigated with the structural equation model. The results show the statistically significant direct and indirect influences of commercial vitality and transportation infrastructure on the number of confirmed cases in an infectious cluster. The clues which induce a high risk of contagions have been evidenced and provided for the decision-making practice responding to the initial stage of possible severe epidemics, indicating that the local public health and hygienic authorities must implement sufficient measures and adopt effective interventions in the areas and places with a high probability of crowded residents.