Work

A human behavior-based model for respiratory infectious diseases prediction

Year: 2025

Type: article

Abstract: Objectives: The research aims to develop a human behavior-based model to predict respiratory infectious diseases. Methods: This research employs semi-supervised machine learning techniques in conjunction with an RGB-depth camera to collect micro-level data. We employed computational fluid dynamics to simulate the dispersion of virus concentration in outpatient environments. Furthermore, we evaluated the infection risk of respiratory infectious diseases (RIDs) by utilizing a dose-response model. Results: A total of 201,600 behavioral data points were collected. The average interpersonal distance observed during medical procedures was 0.62 meters. The most common facial orientation between patients and healthcare workers (HCWs) was face-to-face, accounting for 30.48% of interactions. The predicted average viral RNA load exposures per second during various medical procedures were as follows: Otoscopy: 0.014314 viral RNA loads/s; Rhinoscopy: 0.014411 viral RNA loads/s; Laryngoscopy: 0.014379 viral RNA loads/s; External auditory canal irrigation: 0.018803 viral RNA loads/s. Simulations of preventive measures indicated that N95 masks reduced the probability of infection to 2.44%, surgical masks to 14.81%, and cotton masks to 36.05%. Conclusion: This research presents an innovative micro-level exposure risk model for respiratory infectious diseases (RIDs), which provides significant insights into the risk of infection. However, it is important to acknowledge certain limitations, including the distinctiveness of the data sources utilized and the insufficient examination of transmission pathways. Subsequent studies should aim to enhance the dataset, fine-tune model parameters, and integrate further transmission pathways to augment both the accuracy and applicability of the model.

Source: Frontiers in Public Health

DOI: 10.3389/fpubh.2025.1578178