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BayesSurveillance

BayesSurveillance is an R package for Bayesian adaptive surveillance and intervention learning.

Core workflow

library(BayesSurveillance)

dat <- simulate_surveillance_data(seed = 1)
fit <- fit_bewrs(dat)
risk <- compute_dynamic_bewrs(fit)
peib <- estimate_peib(risk)
policy <- recommend_action(peib)
evaluate_policy(policy)

Adaptive update workflow

old_dat <- simulate_surveillance_data(seed = 1)
new_dat <- simulate_surveillance_data(seed = 2)

updated_policy <- update_policy(old_dat, new_dat)
evaluate_policy(updated_policy)

# Or run the full pipeline directly
policy2 <- adaptive_update(new_dat)

Core methodological idea

The package extends BEWRS from early-warning risk prediction to adaptive intervention learning:

1. Estimate posterior underperformance risk.
2. Convert risk into Dynamic BEWRS using persistence and deterioration.
3. Estimate Provider-specific Expected Intervention Benefit (PEIB).
4. Recommend Bayes-optimal action by expected-loss minimisation.
5. Update the policy as new surveillance data arrive.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.