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ORIGINAL RESEARCH article
Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1578178
A human behavior-based model for respiratory infectious diseases prediction
Provisionally accepted- 1Southern Medical University, Guangzhou, China
- 2People's Liberation Army General Hospital, Beijing, Beijing Municipality, China
- 3Sixth Medical Center of PLA General Hospital, Beijing, Beijing Municipality, China
- 4Beijing Anding Hospital, Capital Medical University, Beijing, Beijing Municipality, China
- 5Beijing University of Technology, Beijing, Beijing Municipality, China
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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, 2 and integrate further transmission pathways to augment both the accuracy and applicability of the model.
Keywords: Respiratory infectious diseases, Relative distance, Relative facial orientation, Relative position, Model, Behavior
Received: 17 Feb 2025; Accepted: 10 Apr 2025.
Copyright: © 2025 Ma, Ma, Min, Zhi, Zhang, Li and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hui Ma, People's Liberation Army General Hospital, Beijing, 100853, Beijing Municipality, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.