Estimating Mycobacterium tuberculosis transmission in a South African clinic: Spatiotemporal model based on person movements. - Nicolas Banholzer; Keren Middelkoop; Juane Leukes; Ernest Weingartner; Remo Schmutz; Kathrin Zürcher; Matthias Egger; Robin Wood; Lukas Fenner (PLoS Computational Biology)
The risk of Mycobacterium tuberculosis (Mtb) transmission can be high in crowded clinics. We developed a spatiotemporal model of airborne Mtb transmission based on the Wells-Riley equation. We collected environmental, clinical and person-tracking data in a South African clinic during COVID-19, when community or surgical masks were compulsory and ventilation was increased. We matched person movements with clinical records to identify the spatiotemporal location of infectious TB patients. We modeled the concentration of infectious doses (quanta) and estimated the individual risk of infection. Over five days, video sensors tracked 1,438 clinic attendees. CO2 levels were low (median 431 ppm, IQR 406 ppm-458 ppm); the quanta concentration was higher in the morning than in the afternoon, and highest in the waiting room. The estimated risk of infection per clinic attendee was 0.05% (80%-credible interval (CrI) 0.01%-0.06%). It increased with the number of close contacts with infectious patients and the time spent in the clinic, and was 1.3-fold (95%-CrI 1.2-1.4) higher in scenarios without mask use and 2.1-fold (95%-CrI 0.9-5.0) higher with pre-pandemic ventilation rates, emphasizing the importance of ventilation. Spatiotemporal modeling can identify high-risk areas and evaluate the impact of infection control measures in clinics.