Computational Fluid Dynamics-based simulation to analyse viral dispersion and spatial resilience in Tanzania

An airborne transmission of COVID-19 in resource-constrained healthcare facilities, such as isolation centres in Tanzania, poses significant infection risks owing to insufficient ventilation. This study employs Computational Fluid Dynamics (CFD) to analyse airflow patterns within the 716 m² isolation centre of Shinyanga Regional Referral Hospital, aiming to identify high-risk regions and provide architectural remedies. A steady-state k-epsilon turbulence model with an 11×10⁶ element mesh simulated both unoccupied and occupied conditions at breathing heights of 1.0 m and 1.7 m. Results demonstrate notable height-dependent airflow variations: the male ward maintained acceptable velocities (1.34–1.33 m/s) conducive to contaminant removal, but the triage and ante-lobby displayed insufficient inflow (0.08 and 0.031 m/s, respectively) when occupied. At a height of 1.0 m within the respiratory zone of bedridden patients, velocities diminished by up to 57% near bed railings, leading to the formation of stagnation pockets. Medical apparatus disrupted local airflow, diminishing velocities by 25-40% around ventilators. Turbulence kinetic energy was increased at 1.7 m (standing HCW level), indicating improved mixing. The airlock operated efficiently at 0.71–0.72 m/s. This work identifies airflow velocity and turbulence kinetic energy as verified surrogate indicators for the potential of contaminant dispersion, instead of employing direct virus dispersion modelling. Proposed resilience techniques include hybrid natural-mechanical ventilation, adaptable spatial reconfiguration, real-time indoor air quality monitoring, and sustained airflow maintenance (≥1.5 m/s at patients' bedsides). These findings provide a replicable, data-driven approach for improving airborne infection control in resource-limited hospital settings.