Infection Probability Calculator

Calculator Inputs

Formula Used

This calculator estimates an effective dose from your chosen exposure route, then converts dose into an infection probability using a dose-response model.

How to Use This Calculator

1. Select the transmission route that best matches the situation.
2. Enter exposure duration and route efficiency based on viability or inoculation success.
3. Fill route-specific fields (air concentration, touch events, or inoculum inputs).
4. Set dose-response parameters from literature when possible (N50 and α).
5. Optionally set exposures per day and days to estimate cumulative risk.
6. Press Calculate to view results, then download CSV or PDF.

Exposure Inputs and Biological Meaning

Inputs represent a pathway for pathogens to reach susceptible tissue. Air concentration uses copies per cubic meter, while contact uses copies per square centimeter. Duration in minutes and breathing rate in liters per minute convert to inhaled volume in cubic meters. Route efficiency compresses viability, deposition, and successful entry into a 0–1 factor. Susceptibility scales dose from 0.05 to 5 to reflect immunity, age, or comorbid risk.

Translating Dose Drivers into Effective Dose

For airborne scenarios, effective dose multiplies concentration by inhaled volume, then attenuates with clean-air rate and distance. The calculator applies a smooth ventilation factor using an ACH reference of 6, and a distance factor that drops with the square of meters from the source. Mask efficiencies reduce both emission and inhalation using (1−source)×(1−receiver). For contact, dose grows with touch count, transfer efficiency, self‑inoculation probability, survival, and reduced by hygiene.

Choosing Dose–Response Parameters Responsibly

The dose model converts dose to probability. Exponential uses k and often derives k from N50 through ln(2)/N50, meaning N50 is the median infectious dose. Beta‑Poisson adds an alpha shape parameter that captures heterogeneity; smaller alpha typically increases variability. When literature is uncertain, use ranges: test N50 across one order of magnitude and alpha between 0.1 and 1.0. The uncertainty slider applies ±% to dose to show sensitivity bands.

Interpreting Single vs Cumulative Probability

Single‑exposure probability estimates risk for one event with the chosen duration. Many real settings involve repeated exposures; the calculator uses Pcum = 1 − (1 − P)^n, where n equals exposures per day times days. This grows quickly when P is moderate: for example, P=2% repeated 30 times yields about 45% cumulative risk. Risk buckets provide communication thresholds: under 1% is very low, 1–5% low, 5–20% moderate, 20–50% high.

Scenario Comparison and Mitigation Planning

Use the same dose‑response parameters across scenarios to isolate mitigation effects. Typical levers include raising clean‑air ACH, increasing distance, improving mask efficiency, and shortening duration. For contact, reducing touch events and increasing hygiene effectiveness often produce large gains because they reduce dose linearly. Save each run with a scenario label, then export CSV or PDF for review. If outputs seem extreme, revisit units, confirm the route, and widen uncertainty to stress‑test assumptions when needed.

FAQs

1) Which dose-response model should I choose?

Use the model that best matches published data for your pathogen. Exponential is simpler and depends on N50 via k = ln(2)/N50. Beta‑Poisson adds alpha to capture variability when responses differ across hosts or doses.

2) What does N50 mean in this calculator?

N50 is the median infectious dose: the dose where the model predicts about 50% infection probability. Lower N50 implies higher infectivity. If you set N50 to 0, the calculator uses your k value directly.

3) How should I use the uncertainty setting?

Uncertainty applies a ±% band to the effective dose, then recomputes probability. It helps you see how sensitive outputs are to measurement error, unit assumptions, or parameter uncertainty. Wider bands indicate inputs need better data or tighter controls.

4) Why is my probability high even with a mask?

Probability is driven by dose. High concentration, long duration, close distance, low clean‑air rate, or low mask efficiency can still produce a large dose. Try increasing ACH, distancing, and receiver efficiency together to reduce dose more strongly.

5) What is the difference between single and cumulative probability?

Single probability is for one exposure event. Cumulative probability combines repeated exposures using 1 − (1 − P)^n. Even small single-event probabilities can accumulate across many contacts, which is why frequency and time horizons matter.

6) Can I use this for clinical decisions?

No. This is an educational estimator for comparing scenarios and mitigation choices. Real infection risk depends on pathogen strain, host factors, and context-specific measurements. Use it alongside professional guidance and validated data sources.