| Metric | Value | Interpretation |
|---|
Summary of effective ACH, needed air changes, and clearance durations.
Airborne infection isolation (AII) rooms protect healthcare workers and subsequent patients by diluting and removing infectious aerosols. During respiratory outbreaks, hospitals often print copies of Centers for Disease Control and Prevention (CDC) tables that list how many minutes are required to reach 99% or 99.9% removal at common air-change rates. Those tables assume a perfectly mixed space with no additional filtration, no deposition to surfaces, and no staff entering to clean or collect specimens. Real rooms differ: volumes vary, portable HEPA units supplement central ventilation, and environmental services teams must open the door repeatedly. Relying on static tables risks either reopening too early or keeping rooms idle for longer than necessary. The calculator above blends physics with operational realism so infection preventionists can document their clearance assumptions.
The form begins with core room characteristics. Volume determines how much air must be cycled to dilute contaminants. The ACH field captures the mechanical ventilation rate supplied by the building system. Mixing efficiency accounts for the fact that stratification, furniture, or poorly aimed diffusers can make the effective ACH lower than the nameplate rating; values under 100% reduce the effective removal rate. Supplemental controls include portable HEPA units whose clean air delivery rate (CADR) adds to the effective ventilation rate when converted to ACH by dividing by room volume. The deposition field approximates natural settling of particles onto surfaces, which acts like an additional air-change rate for larger aerosols. Operational disruptions consider how often staff open the door during turnover and how much clearance time each opening sets back. Finally, the target removal percentage lets the user request 95%, 99%, or 99.9% removal depending on the transmissibility of the pathogen involved.
For a well-mixed space, contaminant concentration follows an exponential decay: , where is the total removal rate in air changes per hour (ACH) and is time in hours. The calculator sets equal to the sum of mechanical ACH, the CADR contribution, and deposition, all scaled by the mixing efficiency percentage. Converting CADR to ACH involves dividing the clean-air delivery rate by the room volume: . Mixing efficiency then multiplies the total by a factor between 0 and 1 to reflect dead zones.
The basic time to reach a target removal fraction is obtained by solving the decay equation for , yielding . Because infection control guidance usually reports minutes, the script multiplies the resulting hours by 60. Door openings complicate matters because they disrupt negative pressure and allow partially contaminated corridor air to mix back into the room. Instead of constructing a full computational fluid dynamics model, the calculator estimates the setback by multiplying the number of door openings per hour by the number of minutes lost per opening and by the duration of the clearance window. This creates an additive penalty that stretches the waiting period. Although simplified, it captures the observation that frequent entries keep a room “dirty” longer.
Beyond the headline figure, the tool builds a minute-by-minute profile. For each minute, it computes a baseline concentration fraction using the exponential decay with the effective ACH. It then calculates how many door events are expected up to that minute by multiplying the door-opening rate by elapsed time, adding the associated penalty minutes, and using the inflated time to compute an adjusted concentration. The downloadable CSV presents four columns: minute mark, baseline concentration, cumulative door penalty, and adjusted concentration. Infection preventionists can import the file into spreadsheets to visualize how quickly the room approaches targets or to document policy rationales during accreditation visits.
Imagine a hospital relying on an older negative-pressure room measuring 65 m³ (roughly 4.5 by 4.8 meters with a 3-meter ceiling). The mechanical system delivers 10 ACH, but diffuser placement and equipment clutter reduce mixing efficiency to 75%. Environmental services deploy a portable HEPA unit that delivers 280 m³/h of clean air. Surface deposition adds another 0.5 ACH, reflecting the behavior of larger droplets. Staff open the door roughly twice per hour while cleaning, and each opening is estimated to rob the room of three minutes of clearance progress. Plugging these numbers into the calculator produces an effective ACH of 8.1 (after mixing), a CADR contribution of 4.3 ACH before mixing, and a combined removal rate of 9.6 ACH. Reaching 99.9% removal would take 43 minutes in the idealized, no-door-opening case. With door penalties applied, the adjusted clearance time stretches to 55 minutes. The summary table also reports that achieving 99% removal requires only 33 minutes, while 95% removal takes 22 minutes.
The CSV timeline reveals that the baseline concentration drops below 1% at minute 33, but the adjusted concentration accounting for door penalties lags behind, crossing the same threshold at minute 41. Administrators weighing policy changes can compare the incremental benefit of adding another HEPA unit: increasing CADR to 500 m³/h would cut the adjusted clearance time to roughly 40 minutes, enabling faster turnover during outbreaks.
The table below compares three common interventions in the scenario above.
| Scenario | Effective ACH | Adjusted time to 99.9% removal | Door events considered |
|---|---|---|---|
| Baseline inputs | 9.6 | 55 minutes | 2 per hour × 3 min penalty |
| Add second HEPA unit (500 m³/h) | 12.2 | 40 minutes | 2 per hour × 3 min penalty |
| Improve mixing to 90% | 11.5 | 43 minutes | 2 per hour × 3 min penalty |
Boosting CADR with an additional HEPA unit yields the fastest clearance because it increases the removal rate without depending on architectural changes. Improving mixing—perhaps by repositioning diffusers or adjusting exhaust grilles—also helps, but not as dramatically. The baseline row illustrates how door openings lengthen waiting times; if staff could consolidate tasks to reduce door events to 0.5 per hour, the adjusted time would fall to about 47 minutes even without hardware changes.
This model remains a simplification. It treats mixing efficiency as a single scalar and assumes that deposition behaves like a constant ACH, even though surface loading depends on particle size, humidity, and airspeed. Door-opening penalties are linear; in reality, a single prolonged door hold can negate minutes of clearance, while quick entries might have negligible effect. The script also ignores temperature stratification, ultraviolet germicidal irradiation (UVGI), and potential leakage around anterooms. Hospitals should continue to validate actual performance by measuring pressure differentials, deploying aerosol tracers, or using particle counters during mock turnovers. Nevertheless, the calculator provides a transparent, documented starting point. By capturing how each parameter shifts the clearance timeline, it helps infection prevention teams justify staffing levels, portable filtration budgets, and patient-placement policies when resources are constrained.
