During the process of major epidemic prevention and control, rapid assessment of the risk level of infectious disease transmission is an important basis for subsequent emergency response measures.

This study aims to develop a risk assessment model based on machine learning (ML) algorithms to quickly identify high-risk places for respiratory infectious disease transmission, thereby facilitating rapid and precise epidemic prevention and control.

Data in 201 public places involving 43 COVID-19 cases in a District of Beijing during the implementation of the "dynamic zero-COVID" policy in April 2022 were collected, including place type, area, ventilation conditions, and personnel flow. An ML algorithm-based model with the secondary attack risk value of the case activity place as the dependent variable was created to assess the risk level of the infected places.

The results of the Random Forest (RForest), Gradient Boosting Decision Tree (GBDT) and Extreme Gradient Boosting (XGBoost) models for the occurrence of close-contact transitions in the investigated places showed that GBDT performed best with receiver operating characteristic area under the curve (AUC) of 0.782 and a sensitivity of 0.647. The initial Ct value of the first case (the lowest value), health treasure scan code record query, mask wearing status of the first case, and residence time of the first case were the most important features in the model. Stratified by the place type, areas with high traffic, such as Chinese restaurants, fast food bento shops and bars weighed importantly.

The GBDT model established in this study can provide effective assistance for risk assessment of respiratory infectious disease epidemic sites, and help policy makers prioritize and make decisions under limited resources. Given the possible similar transmission mechanisms among respiratory infectious diseases, this model is expected to provide a reference for risk assessment of other respiratory infectious diseases.