dr Ridha Wahyutomo, M.Arch
Description
The core hypothesis of the study is integrating Computational Fluid Dynamics (CFD) simulation with microbiological data enhances the predictive accuracy of airflow-based infection risk assessment in hospital isolation rooms. There are two data sources a. Environmental & Architectural Data • Location: A retrofitted COVID-19 isolation room in Pati Islamic Hospital, Central Java, Indonesia. • Measurements: Room geometry, temperature (28.6°C), humidity (68.8%), pressure (-6 Pa), air velocity (11.8 m/s), and ACH (18.87) using calibrated equipment (hot wire anemometer, magnehelic gauge, etc.). • CFD Modeling: Autodesk CFD was used to simulate airflow based on room design and ventilation layout. b. Microbiological Data • Sampling: Passive air sampling via settle plates at five strategic points (e.g., patient bed, entrances, inner corners). • Analysis: Colony Forming Units (CFU) were counted after 24 hour incubation at 37°C. • Total Contamination: 177 CFU/m³ across the room, with highest values near inner corners (75 CFU). The Data Show a. Airflow-Microbe Correlation • Low velocity zones (<0.025 m/s), especially near floor and inner corners, correlated with higher microbial counts (e.g., 75 CFU). • Higher velocity zones (>0.05 m/s) at head level (160 cm) correlated with lower microbial presence, showing effective contaminant removal. b. Statistical Analysis • Power law regression showed a strong inverse relationship CFU density=185.4×(Velocity)−0.67 The model demonstrated strong predictive capability (R² = 0.79, RMSE = 12.3 CFU/m³; F(1,3) = 18.7, p = 0.005), confirming that incremental increases in airflow velocity significantly reduce microbial contamination. Interpretation and Use of the Data a. Validation of CFD for Infection Control • CFD effectively predicted infection risk zones by mapping stagnation areas where microbes settled, validating its use as a preventive design tool in healthcare ventilation planning. b. Practical Implications • Redesign recommendations: ceiling mounted HVAC systems, dynamic airflow targeting, and HEPA filters. • Policy alignment: Findings show gaps between existing hospital infrastructure and Indonesia’s Ministry of Health standards (Permenkes 40/2022). • Cross-application: Useful for rapid retrofitting of spaces during pandemics, e.g., hotels or emergency departments into temporary isolation rooms.
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Steps to reproduce
1. Research Data Outline a. Environmental and Architectural Data Room geometry: Measured dimensions of isolation room, bathroom, doorways, fan positions, etc. Environmental parameters: Air pressure, temperature, humidity, airflow velocity, and air change rate (ACH). b. Microbiological Data Airborne microbial contamination quantified as CFU/m³ at five key room locations using blood agar settle plates. c. CFD Simulation Data 3D transient CFD simulations to model airflow dynamics at various room elevations and cross-sections. Simulated velocity fields correlated spatially and statistically with microbiological data. 2. Data Collection Methods and Protocols a. Architectural & Environmental Measurement Tools/Software: AutoCAD 2023: Room geometry modeling (2D and 3D). Benetech GM 8903 Hot Wire Anemometer: Air velocity. Lutron YK-2001TM: Temperature and relative humidity. Dwyer 460 Magnehelic Gauge: Pressure differentials. Protocols: On-site measurement of distances, fan placement, and boundary conditions. Cross-verification of layout with architectural plans. b. Microbiological Sampling Reagents & Equipment: 90 mm Blood Agar Plates (Becton Dickinson). Incubation at 37°C for 24 hours. Procedure: Passive air sampling using settle plate method, per ISO 14644-1 and SNI 9099-2022 standards. Placement: 5 strategic locations near bed, entrances, and corners. Sampling Time: 15 minutes to 1 hour. Manual CFU counting post incubation. c. CFD Simulation Software: Autodesk CFD 2023 Procedure: Developed 3D CFD model using room geometry and measured boundary conditions. Simulated air velocity at 3 elevations (floor, patient bed height, and respiratory height). Analyzed airflow stagnation, turbulent kinetic energy, and directional movement. 3. Data Analysis and Integration Workflow a. Correlation and Statistical Analysis Software: SPSS 28.0 Methods: Linear regression and Spearman’s rank correlation between air velocity and CFU density. Chi-square test for spatial distribution validity.
Institutions
- Universitas Katolik Soegijapranata Program Pascasarjana