Airborne Infection Risk Calculator
Indoor Airborne Infection Risk: Context and Purpose
Airborne infection risk arises when people share indoor air with someone releasing infectious respiratory aerosols while breathing, speaking, coughing, or singing. Enclosed rooms with limited outdoor-air exchange can allow those particles to accumulate, so practical decisions such as opening windows, reducing attendance, or shortening a gathering affect the exposure dose. This calculator uses the Wells-Riley approach to turn room volume, ventilation, time, occupancy, emission, and breathing assumptions into an estimated individual infection probability. It is a scenario-planning model, not a diagnosis or a measurement of whether a particular person is infectious.
The Wells-Riley Equation for Indoor Airborne Exposure
The airborne infection calculation uses the canonical Wells-Riley equation . Here is the probability that one susceptible person becomes infected; is the number of infectious people sharing the room; is the quanta emission rate, an abstract measure of infectious airborne dose; is the susceptible person’s inhaled-air rate; is time spent exposed; and is ventilation flow in volume per unit time. Quanta let the model represent a pathogen and activity with one input: vocalizing or strenuous activity can raise emissions compared with quiet breathing. The exponential form treats inhalation of infectious quanta as a Poisson process, so the result is the chance of inhaling at least one quantum.
Wells-Riley Implementation in This Airborne Risk Calculator
This airborne infection risk calculator converts air changes per hour (ACH) into ventilation flow by multiplying ACH by room volume: . With ACH in changes per hour and volume in cubic metres, is in cubic metres per hour. The calculation therefore uses:
Formula: Risk = 100 × 1 - e^-(I×q×p×t)/(ACH×V)
The calculator reports this individual probability as a percentage. It also multiplies the unrounded probability by the number of susceptible occupants to report expected secondary infections. That figure is an average number across comparable scenarios, not a prediction that a fractional number of people will become ill; linearity of expectation makes the multiplication valid even when individual outcomes are not independent.
Airborne Infection Risk Parameter Guidance
Infectious Individuals. For an airborne infection scenario, this field is the number of people assumed to be actively emitting infectious quanta into the room. A value of one is often used to explore the consequence of one undetected infectious attendee; additional infectious people increase the modeled dose proportionally.
Susceptible Occupants. This is the number of people who could be infected. It does not change the displayed probability for one susceptible person, but it scales the expected-secondary-infections result and helps compare gatherings of different sizes.
Room Volume. Enter the physical air volume in cubic metres. At the same emission and ventilation rate in ACH, a larger room has a larger ventilation flow and more air in which emitted quanta are diluted.
Air Changes per Hour. ACH is the number of room-air volumes supplied, exchanged, or equivalently cleaned each hour under the model’s ventilation assumption. Raising ACH increases the denominator of the dose calculation; the value should represent the ventilation or clean-air rate relevant to the scenario.
Exposure Duration. This is the number of hours occupants share the indoor air. In the calculator’s steady-state Wells-Riley expression, longer exposure increases dose directly, while a shorter event reduces it.
Quanta Emission Rate. Measured in quanta per hour for each infectious person, this input represents the pathogen and the emitting activity. It is often uncertain and can differ substantially between quiet breathing, speaking, singing, coughing, and exercise, so it is useful to examine more than one plausible assumption.
Breathing Rate. This is each susceptible person’s inhalation rate in cubic metres per hour. A higher rate increases the modeled dose because that person inhales more of the shared room air during the exposure.
Airborne Infection Risk Categories
| Calculated individual risk % | Airborne exposure interpretation |
|---|---|
| 0–10 | Lower modeled probability; review assumptions before treating it as negligible |
| 11–30 | Meaningful modeled probability; consider additional precautions |
| 31–60 | High modeled probability; stronger mitigation may be warranted |
| 61–100 | Very high modeled probability; reconsider the indoor scenario or its controls |
Understanding Airborne Infection Risk Results
An airborne infection result is conditional on every value entered, especially the assumed presence of infectious people and their quanta emission rate. The Wells-Riley model assumes that aerosols mix uniformly throughout the room; real rooms can have local airflow, thermal plumes, and proximity effects that produce uneven concentrations. For example, a 5% result means a modeled individual probability of 0.05, not a guarantee that one person in every group of twenty will be infected. Increasing ventilation or reducing duration lowers the dose in this implementation, while changes close to 100% probability become less proportional because probability cannot exceed 100%.
Mitigation Strategies for Airborne Transmission
Airborne transmission mitigation can be explored by changing the inputs that determine modeled dose. Increasing effective clean-air delivery through ventilation, outdoor air, or filtration is represented here by a higher ACH value. Reducing the number of infectious sources, using a lower emission scenario when the activity is quieter, limiting time indoors, and choosing a larger room all reduce the calculated dose. Masks can reduce emission and inhaled dose in practice, but this calculator has no mask-efficiency input; any attempt to represent masks through quanta or breathing assumptions should be treated as an external modeling choice rather than a built-in mask calculation.
Historical Applications of the Wells-Riley Airborne Model
Wells-Riley airborne infection modeling became widely known through analysis of a tuberculosis outbreak in a university building, where investigators used observed transmission to estimate quanta generation. During the COVID-19 pandemic, researchers also applied related models to indoor settings such as choir rehearsals, call centers, and restaurants. These applications illustrate why occupancy, vocal activity, duration, room size, and ventilation must be considered together rather than as isolated safety measures. They do not make a simple room-level estimate a substitute for outbreak investigation or direct measurements.
Limitations and Extensions for Airborne Infection Risk Estimates
This airborne infection calculator uses a simplified Wells-Riley relationship and therefore omits important details of a particular room. It assumes fixed emission and ventilation rates and homogeneous mixing, although activity, windows, HVAC operation, and air distribution can change during a gathering. It also does not separately model immunity, mask fit, particle deposition, viral decay, or filtration efficiency. The model estimates exposure from the initially infectious people entered; it does not model newly infected occupants becoming infectious during the same event.
Quanta emission is often the most uncertain airborne infection input. Estimates can come from retrospective outbreaks or laboratory work and may vary with the pathogen, variant, behavior, and measurement method. Rather than treating one output as a precise forecast, use a range of defensible quanta, ventilation, and duration assumptions to see which uncertainty moves the result most. In this formula, increasing , , , or increases risk, while increasing ACH or room volume decreases it.
Broader Implications of Indoor Airborne Risk Estimates
Indoor airborne infection estimates can help frame conversations about ventilation and event design without implying that a single percentage settles a health decision. Building operators can compare ventilation scenarios, and organizers can examine how attendance, room selection, duration, and activities alter an assumed exposure. The calculation is especially useful for making the trade-offs explicit: a room with more people may also require more clean air, a shorter schedule, or a lower-emission activity to produce a lower modeled dose.
Airborne risk scenarios can also support education about the connection between respiratory health and building systems. Students and teams can vary room volume, ACH, and exposure time to observe how each term enters the Wells-Riley equation. That exercise emphasizes that indoor-air decisions affect shared exposure and that uncertain inputs deserve careful interpretation.
As evidence and building practices evolve, more detailed models may add time-varying ventilation, filtration, masks, carbon-dioxide-informed ventilation estimates, or stochastic simulation. This calculator intentionally retains a compact Wells-Riley structure: it shows how environmental conditions and behavior combine in a conditional probability, while leaving room-specific assessment to appropriate public-health, engineering, or clinical expertise.
Ventilation Vigil
Keep the room fresh through a 90-second shift. Drag the airflow lane (or tap ↑/↓) to dial ventilation while cough bursts, stuck windows, and crowd surges push the Wells-Riley dose toward danger. Score shielding credits by keeping risk below the baseline forecast.