Reducing spatial clustering to prevent Mycobacterium tuberculosis transmission in a busy Zambian hospital: a modelling study based on person movements, environmental and clinical data

This repository contains anonymized person-tracking and environmental data, along with all code, for the study by Banholzer et al. (2025). The dataset will be shared upon publication of the paper. The study
quantifies airborne Mycobacterium tuberculosis (Mtb) transmission risk in the outpatient department of a hospital in Lusaka, Zambia, and evaluates the effects of two operational interventions targeting spatial
clustering: (1) an optimised waiting-area layout with physical distancing measures; and (2) a one-way patient flow system.

The dataset includes preprocessed person movement trajectories (position coordinates at 1-second resolution) for 52 study days (June–August 2024), environmental sensor measurements (CO2, temperature, humidity),
daily air exchange rate estimates, linked tracking tables identifying TB patient movements, and Monte Carlo parameter samples for the transmission model. Individual-level clinical data are not shared due to
patient privacy; however, all preprocessed outputs necessary to reproduce the modelling results are provided.

Data are released under a CC BY-NC 4.0 license. By using these data, you agree to the terms of the data use agreement described in the README, including: no re-identification of individuals, acknowledgment of
CIDRZ and NHRA as the data source and approving authority, and restriction to non-commercial scientific research.

This OSF project is linked to the GitHub repository at https://github.com/nbanho/tb_patient_flows, which contains a detailed README with the full repository structure, software requirements, and step-by-step
instructions for reproducing all results.