diff --git a/R/estimate_infections.R b/R/estimate_infections.R
index f386310f0..b2e67efd6 100644
--- a/R/estimate_infections.R
+++ b/R/estimate_infections.R
@@ -20,6 +20,11 @@
 #' @param reported_cases A data frame of confirmed cases (confirm) by date
 #' (date). confirm must be integer and date must be in date format.
 #'
+#' @param process_model A character string that defines what is being
+#' modelled: "infections", "growth" or "R" (default). If ' set to "R",
+#' a generation time distribution needs to be defined via the `generation_time`
+#' argument.
+#'
 #' @param generation_time A call to `generation_time_opts()` defining the
 #' generation time distribution used. For backwards compatibility a list of
 #' summary parameters can also be passed.
@@ -218,7 +223,7 @@
 #' options(old_opts)
 #' }
 estimate_infections <- function(reported_cases,
-                                model = "R",
+                                process_opts = process_opts(),
                                 generation_time = generation_time_opts(),
                                 delays = delay_opts(),
                                 truncation = trunc_opts(),
@@ -288,6 +293,7 @@ estimate_infections <- function(reported_cases,
   # Define stan model parameters
   data <- create_stan_data(
     reported_cases = reported_cases,
+    process_opts = process_opts,
     generation_time = generation_time,
     delays = delays,
     truncation = truncation,
@@ -296,8 +302,7 @@ estimate_infections <- function(reported_cases,
     obs = obs,
     backcalc = backcalc,
     shifted_cases = shifted_cases$confirm,
-    horizon = horizon,
-    process_model = process_model
+    horizon = horizon
   )
 
   # Set up default settings
diff --git a/R/opts.R b/R/opts.R
index 59376949e..f7af12f72 100644
--- a/R/opts.R
+++ b/R/opts.R
@@ -90,7 +90,6 @@ generation_time_opts <- function(..., disease, source, max = 15L,
   names(gt) <- paste0("gt_", names(gt))
 
   return(gt)
-}
 
 #' Delay Distribution Options
 #'
@@ -211,7 +210,7 @@ trunc_opts <- function(dist = list()) {
 
 #' Time-Varying Reproduction Number Options
 #'
-#' @description `r lifecycle::badge("stable")`
+#' @description `r lifecycle::badge("deprecated")`
 #' Defines a list specifying the optional arguments for the time-varying
 #' reproduction number. Custom settings can be supplied which override the
 #' defaults.
@@ -249,6 +248,7 @@ trunc_opts <- function(dist = list()) {
 #'
 #' @return A list of settings defining the time-varying reproduction number.
 #' @author Sam Abbott
+
 #' @inheritParams create_future_rt
 #' @export
 #' @examples
@@ -267,6 +267,7 @@ rt_opts <- function(prior = list(mean = 1, sd = 1),
                     future = "latest",
                     gp_on = "R_t-1",
                     pop = 0) {
+  stop("rt_opts is deprecated - use process_opts instead")
   rt <- list(
     prior = prior,
     use_rt = use_rt,
@@ -288,9 +289,93 @@ rt_opts <- function(prior = list(mean = 1, sd = 1),
   return(rt)
 }
 
-#' Back Calculation Options
+#' Process model optionss
 #'
 #' @description `r lifecycle::badge("stable")`
+#' Defines a list specifying the optional arguments for the process mode.
+#' Custom settings can be supplied which override the defaults.
+#' @param prior List containing named numeric elements "mean" and "sd". The mean and
+#'  standard deviation of the log normal Rt prior. Defaults to mean of 1 and standard
+#'  deviation of 1.
+#' @param use_rt Logical, defaults to `TRUE`. Should Rt be used to generate infections
+#'  and hence reported cases.
+#' @param rw Numeric step size of the random walk, defaults to 0. To specify a weekly random
+#'   walk set `rw = 7`. For more custom break point settings consider passing in a `breakpoints`
+#'   variable as outlined in the next section.
+#' @param use_breakpoints Logical, defaults to `TRUE`. Should break points be used if present
+#'  as a `breakpoint` variable in the input data. Break points should be defined as 1 if present
+#'  and otherwise 0. By default breakpoints are fit jointly with a global non-parametric effect
+#'  and so represent a conservative estimate of break point changes (alter this by setting `gp = NULL`).
+#' @param pop Integer, defaults to 0. Susceptible population initially present. Used to adjust
+#' Rt estimates when otherwise fixed based on the proportion of the population that is
+#' susceptible. When set to 0 no population adjustment is done.
+#' @param gp_on Character string, defaulting to  "R_t-1". Indicates how the Gaussian process,
+#' if in use, should be applied to Rt.  Currently supported options are applying the Gaussian
+#' process to the last estimated Rt (i.e Rt = Rt-1 * GP), and applying the Gaussian process to
+#'  a global mean (i.e Rt = R0 * GP). Both should produced comparable results when data is not
+#'  sparse but the method relying on a global mean will revert to this for real time estimates,
+#'  which may not be desirable.
+#' @return A list of settings defining the time-varying reproduction number
+#' @inheritParams create_future_rt
+#' @export
+#' @examples
+#' # default settings
+#' rt_opts()
+#'
+#' # add a custom length scale
+#' rt_opts(prior = list(mean = 2, sd = 1))
+#'
+#' # add a weekly random walk
+#' rt_opts(rw = 7)
+#' @importFrom data.table fcase
+process_opts <- function(model = "R",
+                         prior_mean = data.table::fcase(
+                           model == "R", list(mean = 1, sd = 1),
+                           model == "growth", list(mean = 0, sd = 1),
+                           model == "infections", NULL
+                         ),
+                         prior_t = NULL,
+                         rw = 0,
+                         use_breakpoints = TRUE,
+                         future = "latest",
+                         stationary = FALSE
+                         pop = 0) {
+
+  ## check
+  model_choices <- c("infections", "growth", "R")
+  process_model <- match.arg(process_model, choices = model_choices)
+  process_model <- which(process_model == model_choices) - 1
+
+  if (!(xor(is.null(prior_mean), is.null(prior_t)))) {
+    stop("Either 'prior_mean' or 'prior_t' must be set to NULL")
+  }
+  process <- list(
+    process_model = process_model,
+    prior_mean = prior_mean,
+    prior_t = prior_t,
+    rw = rw,
+    use_breakpoints = use_breakpoints,
+    future = future,
+    stationary = stationary,
+    pop = pop,
+  )
+
+  # replace default settings with those specified by user
+  if (process$rw > 0) {
+    process$use_breakpoints <- TRUE
+  }
+
+  if (!is.null(prior_mean) &&
+      !("mean" %in% names(process$prior) &&
+        "sd" %in% names(process$prior))) {
+    stop("prior must have both a mean and sd specified")
+  }
+  return(process)
+}
+
+#' Back Calculation Options
+#'
+#' @description `r lifecycle::badge("deprecated")`
 #' Defines a list specifying the optional arguments for the back calculation
 #' of cases. Only used if `rt = NULL`.
 #'
@@ -323,7 +408,8 @@ rt_opts <- function(prior = list(mean = 1, sd = 1),
 #' # default settings
 #' backcalc_opts()
 backcalc_opts <- function(prior = "reports", prior_window = 14, rt_window = 1) {
-  backcalc <- list(
+  stop("backcalc_opts is deprecated - use process_opts instead")
+   backcalc <- list(
     prior = match.arg(prior, choices = c("reports", "none", "infections")),
     prior_window = prior_window,
     rt_window = as.integer(rt_window)