Cholera QRA Risk Assessment Portal

Source country

Select the exporting/source country you want to assess.

Type and pick from the dropdown list.
If you choose Bangladesh or China, preset values auto-fill the inputs.
For other countries, enter inputs manually using surveillance/literature data.

Presets available: Bangladesh, China. For other countries, enter inputs manually.

Destination

This model is configured for destination country: India.

Iterations

Iterations (Monte Carlo)

Number of simulation runs to estimate uncertainty.

Higher iterations → smoother histogram + more stable CI, but slower.
Recommended: 10,000 (good balance).
Range allowed: 100 to 200,000.

Tip: 10,000 is a good default.

Ready.

Inputs

Probabilities in [0,1]

P1 — Monthly outbreak probability

Outbreaks (a)

Total number of reported cholera outbreaks in the chosen surveillance period.

Use official reports / surveillance summaries / publications.
Used to compute outbreak rate: λ = a / b

Months (b)

Total months in the same period used for a.

Example: 20 years → 240 months.
Used with outbreaks to compute: λ = a / b

$P_1 = 1-e^{-t\lambda}$, $t=1$ month, $\lambda=a/b$

How P1 is calculated

This uses a Poisson process assumption for outbreaks:

λ = a/b (monthly rate), t = 1 month

P1 = 1 − exp(−tλ)

P2 — Infection probability at source (Beta)

Enter α/β directly (advanced)

How to fill P2

P2 models uncertainty in infection probability at source using a Beta distribution.

Recommended: enter s (positive) and n (total).
We convert to Beta using pseudo-counts: α=s+1, β=(n−s)+1.
Advanced: directly enter α and β from literature/prior.

Positive cases (s)

Number of confirmed positive cases at source in your dataset/sample.

Must satisfy: 0 ≤ s ≤ n
Used to compute Beta: α = s + 1

Total tested / at-risk (n)

Total number tested (or total at-risk observations) corresponding to s.

Must be > 0.
Used to compute Beta: β = (n − s) + 1

α=—, β=—, mean(P2)=—

P3 — False negative at source

Sensitivity min

Source sensitivity (min)

Minimum plausible diagnostic sensitivity at source (0–1).

From validation studies / kit insert / literature.
We sample: Sens ~ Uniform(min, max)
Then: P3 = 1 − Sens

Sensitivity max

Source sensitivity (max)

Maximum plausible diagnostic sensitivity at source (0–1).

Ensure max ≥ min.
False negative uses the same sample: P3 = 1 − Uniform(min, max)

$P_3 = 1 - \mathrm{Uniform}(s_{min}, s_{max})$

P4 — False negative in India

Sensitivity min

India sensitivity (min)

Minimum plausible diagnostic sensitivity in India (0–1).

We sample: Sens ~ Uniform(min, max)
Then: P4 = 1 − Sens

Sensitivity max

India sensitivity (max)

Maximum plausible diagnostic sensitivity in India (0–1).

Ensure max ≥ min.
False negative: P4 = 1 − Uniform(min, max)

$P_4 = 1 - \mathrm{Uniform}(s_{min}, s_{max})$

P5 — Exposure (unsafe WASH)

P5a (limited sanitation)

P5a — Limited sanitation

Proportion (0–1) of people with limited sanitation in the risk pathway.

Example: 11% → enter 0.11
Combined exposure: P5 = P5a × P5b

P5b (surface water)

P5b — Surface water use

Proportion (0–1) of people relying on surface water in the pathway.

Example: 1% → enter 0.01
Exposure calculation: P5 = P5a × P5b

$P_5=P_{5a}\times P_{5b}$

P6 — Survival

CFR (Case fatality rate)

Probability (0–1) that an infected case dies.

Example: 3% → enter 0.03
Survival used in risk: P6 = 1 − CFR

$P_6 = 1-\mathrm{CFR}$

Distribution of simulated risk probabilities

Histogram + smooth curve + min/mean/max lines (values shown on plot)

Labels on the dashed lines show Min / Mean / Max values.

Sensitivity Assessment

Spearman rank correlation (ρ) — values shown on bars

Downloads

Enabled after a successful simulation run.

Simulation CSV capped to 50k rows (auto-sampled if more).

Risk Assessment Portal

Risk Assessment Results

Risk from — to India

Distribution of simulated risk probabilities

Sensitivity Assessment