User-defined epidemic process

Lets you supply any RTMB-traceable function intensity_fn(theta) that returns the log expected-incidence trajectory log_mean[n_time x n_strata] as the latent epidemic process for the nowcast. This makes the framework epidemic-process agnostic: random walks, ODE models, regression surfaces, and anything else that can be written in AD-safe arithmetic are all valid.

Usage

custom_epidemic(
  intensity_fn,
  priors = list(),
  name = "Custom",
  param_names = character(0),
  inits = numeric(0)
)

Arguments

intensity_fn

A function function(theta) that takes a numeric parameter vector and returns a numeric matrix of dimensions [n_time x n_strata] containing log expected incidence (the full log_mean, not just a trend — include any intercept inside the function). Must use only RTMB-traceable operations: +, -, *, /, exp, log, sqrt, abs, sum, for loops of fixed length (not data-dependent). Never use if/ifelse on parameter values, pmax/pmin on AD types, or external solvers. Call validate_custom_epidemic() to check traceability before fitting.

priors

A list with one element per parameter in theta. Each element is either a prior_class object such as normal_prior() (free parameter, estimated) or a single numeric scalar (fixed parameter, held constant during optimisation). The number of parameters is inferred from the length of this list (or from param_names / inits if priors is omitted). For time-varying processes (e.g. a random walk with one innovation per event-time), get the number of event-times with infer_max_time() first.

name

Character label shown in print and diagnostic output.

param_names

Character vector naming the parameters; its length sets the number of parameters if priors is empty. Defaults to "theta1", …

inits

Numeric vector of starting values; its length sets the number of parameters if priors and param_names are empty. Defaults to zeros.

Value

A custom_epidemic_class object (subclass of epidemic_process_class).

Details

See also

validate_custom_epidemic(), epidemic_process

Examples

# Pure random walk on log-incidence (max_time = 20)
# Uses cumsum() — no [<- assignment needed, so fully AD-safe.
max_time <- 20L
rw_fn <- function(theta) {
  log_mu0  <- theta[1]
  sigma_rw <- exp(theta[2])
  eps      <- theta[3:(2L + max_time)]
  lm       <- log_mu0 + cumsum(sigma_rw * eps)
  matrix(lm, max_time, 1L)
}
# n_params is inferred (here 2 + max_time) from the priors list:
custom_epi <- custom_epidemic(
  rw_fn,
  priors   = c(list(normal_prior(2, 1), normal_prior(-2, 0.5)),
               rep(list(std_normal_prior()), max_time)),
  name     = "RandomWalk",
  param_names = c("log_mu0", "log_sigma", paste0("eps_", seq_len(max_time))),
  inits    = c(2, -2, rep(0, max_time))
)