Estimate infection risk using dose-response exposure models. Compare exponential and beta-Poisson assumptions with sensitivity checks. Export clean summaries for audits, teaching, and safety reviews.
| Scenario | Model | Measured Dose | Modifier | Effective Dose | Per Exposure Risk | Cumulative Risk |
|---|---|---|---|---|---|---|
| Lab Surface Exposure | Exponential (r=0.0025) | 250 CFU | 0.4800 | 120.0000 | 25.9182% | 59.3430% (3 events) |
| Food Contact Event | Beta-Poisson (α=0.55, β=1800) | 1000 CFU | 0.4200 | 420.0000 | 10.8942% | 20.6016% (2 events) |
| Low Dose Aerosol | Beta-Poisson (α=0.35, β=950) | 75 virions | 0.5040 | 37.8000 | 1.3564% | 6.6003% (5 events) |
Values above are illustrative examples to demonstrate workflow and output formatting.
1) Effective dose adjustment
D_eff = D × (Viability/100) × (Recovery/100) × Susceptibility
This converts measured dose into an adjusted dose used by the selected dose-response model.
2) Exponential model
P = 1 - e^(-r × D_eff)
ID50 = ln(2) / r
Use when a single-hit assumption is appropriate and parameter r is available from literature or validation data.
3) Approximate Beta-Poisson model
P = 1 - (1 + D_eff/β)^(-α)
ID50 = β × (2^(1/α) - 1)
This model is commonly used for variable host-pathogen interaction assumptions in microbial risk screening.
4) Repeated exposure cumulative risk
P_cum = 1 - (1 - P)^n
The calculator assumes independent exposure events. For dependent events, use a more advanced epidemiological model.
For regulated work, document parameter sources, confidence intervals, and scenario assumptions beside each exported result.
Start by naming the exposure scenario and organism so exported reports remain traceable. Enter the measured dose in the same units used by your sampling protocol, then set viability and recovery percentages to reflect survival and transfer assumptions. These adjustments convert a raw measured quantity into an effective dose used by the selected response model. Clear labels reduce interpretation errors when teams compare multiple scenarios across time, locations, and operators, and reporting periods consistently.
The calculator supports exponential and approximate beta-Poisson dose-response models. Use the exponential option when literature provides a validated single parameter r. Use beta-Poisson when host-pathogen variability is better represented by alpha and beta parameters. Parameter quality drives output quality, so record sources, study design, and confidence limits in notes. This improves auditability and helps analysts explain why two similar doses may produce different estimated risks under alternate assumptions before operational decisions proceed.
After adjustment, the tool calculates effective dose, then estimates per-exposure probability and cumulative probability across repeated events. It also derives an ID50 estimate for the selected model and back-calculates the source dose required to reach a target per-exposure risk. These outputs support screening decisions, training exercises, and scenario comparisons. They do not replace laboratory confirmation, but they provide a consistent framework for discussing thresholds, assumptions, and operational controls during early exposure assessments.
Sensitivity testing is essential because viability, recovery, and susceptibility often vary more than measured dose. Analysts should run low, expected, and high assumptions to create a risk range instead of a single point estimate. Comparing ranges reveals which input most influences the result and where better sampling or literature review is needed. This approach strengthens communication with quality, safety, and research teams by showing uncertainty explicitly rather than hiding it for review and planning.
Use the CSV export for structured logging, trend analysis, or spreadsheet reviews. Use the PDF export for approvals, incident documentation, or teaching packets where a static summary is preferred. Keep each run linked to date, sample method, parameter source, and reviewer notes. A repeatable workflow—calculate, export, verify, and archive—improves consistency and supports defensible biological risk screening across laboratories, classrooms, and operational planning environments with version control.
Effective dose is the measured dose adjusted by viability, recovery, and susceptibility. It is the value passed into the selected dose-response model to estimate infection probability.
Use the exponential model when you have a credible literature or validation estimate for parameter r and a single-hit assumption is appropriate for screening.
Choose Beta-Poisson when host-pathogen response variability matters and you have alpha and beta parameters from published studies, internal validation, or a documented risk model.
No. Cumulative risk is calculated with an independence assumption across events. If exposures are dependent, clustered, or time-correlated, use a more advanced epidemiological approach.
Use it for screening, education, and scenario comparison. Regulatory or clinical decisions should also include validated laboratory data, uncertainty analysis, and expert review.
Save the scenario name, date, sampling method, units, parameter source, assumptions, reviewer notes, and version details so results remain reproducible.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.