improve early calculation

NEWS.md

@@ -19,6 +19,7 @@
 - A bug was fixed where an internal function for applying a default cdf cutoff failed due to a difference a vector length issue. By @jamesmbaazam in #858 and reviewed by @sbfnk.
 - All parameters have been changed to the new parameter interface. By @sbfnk in #871 and reviewed by @seabbs.
 - A bug was fixed where `plot.dist_spec()` wasn't throwing an informative error due to an incomplete check for the max of the specified delay. By @jamesmbaazam in #858 and reviewed by @.
+- Updated the early dynamics calculation to use the full linear model if available. Also changd the prior for initial infections to be approximately Poisson. By @sbfnk in # and reviewed by
 
 ## Package changes
 

R/create.R

@@ -455,28 +455,34 @@ create_obs_model <- function(obs = obs_opts(), dates) {
 #' @return A list containing `prior_infections` and `prior_growth`.
 #' @keywords internal
 estimate_early_dynamics <- function(cases, seeding_time) {
-  first_week <- data.table::data.table(
-    confirm = cases[seq_len(min(7, length(cases)))],
-    t = seq_len(min(7, length(cases)))
+  initial_period <- data.table::data.table(
+    confirm = cases[seq_len(min(7, seeding_time, length(cases)))],
+    t = seq_len(min(7, seeding_time, length(cases))) - 1
   )[!is.na(confirm)]
 
-  # Calculate prior infections
-  prior_infections <- log(mean(first_week$confirm, na.rm = TRUE))
-  prior_infections <- ifelse(
-    is.na(prior_infections) || is.null(prior_infections),
-    0, prior_infections
-  )
-
+  prior_infections <- 0
   # Calculate prior growth
-  if (seeding_time > 1 && nrow(first_week) > 1) {
+  if (seeding_time > 1 && nrow(initial_period) > 1) {
     safe_lm <- purrr::safely(stats::lm)
-    prior_growth <- safe_lm(log(confirm) ~ t, data = first_week)[[1]]
+    prior_growth <- safe_lm(log(confirm) ~ t, data = initial_period)[[1]]
+    prior_infections <- ifelse(
+      is.null(prior_growth), 0, prior_growth$coefficients[1]
+    )
     prior_growth <- ifelse(
       is.null(prior_growth), 0, prior_growth$coefficients[2]
     )
   } else {
     prior_growth <- 0
   }
+
+  # Calculate prior infections
+  if (prior_infections == 0) {
+    prior_infections <- log(mean(initial_period$confirm, na.rm = TRUE))
+    if (is.na(prior_infections) || is.null(prior_infections)) {
+      prior_infections <- 0
+    }
+  }
+
   return(list(
     prior_infections = prior_infections,
     prior_growth = prior_growth

inst/stan/estimate_infections.stan

@@ -95,9 +95,13 @@ transformed parameters {
       );
     }
     profile("infections") {
+      real frac_obs = get_param(
+        frac_obs_id, params_fixed_lookup, params_variable_lookup, params_value,
+        params
+      );
       infections = generate_infections(
         R, seeding_time, gt_rev_pmf, initial_infections, initial_growth, pop,
-        future_time
+        future_time, obs_scale, frac_obs
       );
     }
   } else {

inst/stan/functions/infections.stan

@@ -20,7 +20,7 @@ real update_infectiousness(vector infections, vector gt_rev_pmf,
 // generate infections by using Rt = Rt-1 * sum(reversed generation time pmf * infections)
 vector generate_infections(vector oR, int uot, vector gt_rev_pmf,
                            array[] real initial_infections, array[] real initial_growth,
-                           int pop, int ht) {
+                           int pop, int ht, int obs_scale, real frac_obs) {
   // time indices and storage
   int ot = num_elements(oR);
   int nht = ot - ht;
@@ -32,6 +32,9 @@ vector generate_infections(vector oR, int uot, vector gt_rev_pmf,
   vector[ot] infectiousness;
   // Initialise infections using daily growth
   infections[1] = exp(initial_infections[1]);
+  if (obs_scale) {
+    infections[1] = infections[1] / frac_obs;
+  }
   if (uot > 1) {
     real growth = exp(initial_growth[1]);
     for (s in 2:uot) {

inst/stan/functions/rt.stan

@@ -66,7 +66,7 @@ void rt_lp(array[] real initial_infections, array[] real initial_growth,
     bp_effects ~ normal(0, bp_sd[1]);
   }
   // initial infections
-  initial_infections ~ normal(prior_infections, 0.2);
+  initial_infections ~ normal(prior_infections, sqrt(prior_infections));
 
   if (seeding_time > 1) {
     initial_growth ~ normal(prior_growth, 0.2);

inst/stan/simulate_infections.stan

@@ -62,7 +62,7 @@ generated quantities {
 
       infections[i] = to_row_vector(generate_infections(
         to_vector(R[i]), seeding_time, gt_rev_pmf, initial_infections[i],
-        initial_growth[i], pop, future_time
+        initial_growth[i], pop, future_time, obs_scale, frac_obs[i]
       ));
 
       if (delay_id) {

tests/testthat/test-estimate-early-dynamics.R

@@ -10,7 +10,7 @@ test_that("estimate_early_dynamics works", {
   # Check values
   expect_identical(
     round(prior_estimates$prior_infections, 2),
-    4.53
+    3.21
   )
   expect_identical(
     round(prior_estimates$prior_growth, 2),
@@ -21,29 +21,29 @@ test_that("estimate_early_dynamics works", {
 test_that("estimate_early_dynamics handles NA values correctly", {
   cases <- c(10, 20, NA, 40, 50, NA, 70)
   prior_estimates <- estimate_early_dynamics(cases, 7)
-  expect_equal(
-    prior_estimates$prior_infections,
-    log(mean(c(10, 20, 40, 50, 70), na.rm = TRUE))
+  expect_identical(
+    round(prior_estimates$prior_infections, 2),
+    2.55
   )
   expect_true(!is.na(prior_estimates$prior_growth))
 })
 
 test_that("estimate_early_dynamics handles exponential growth", {
   cases <- 2^(c(0:6)) # Exponential growth
   prior_estimates <- estimate_early_dynamics(cases, 7)
-  expect_equal(prior_estimates$prior_infections, log(mean(cases[1:7])))
+  expect_equal(prior_estimates$prior_infections, log(2^0))
   expect_true(prior_estimates$prior_growth > 0) # Growth should be positive
 })
 
 test_that("estimate_early_dynamics handles exponential decline", {
   cases <- rev(2^(c(0:6))) # Exponential decline
   prior_estimates <- estimate_early_dynamics(cases, 7)
-  expect_equal(prior_estimates$prior_infections, log(mean(cases[1:7])))
+  expect_equal(prior_estimates$prior_infections, log(2^6))
   expect_true(prior_estimates$prior_growth < 0) # Growth should be negative
 })
 
 test_that("estimate_early_dynamics correctly handles seeding time less than 2", {
   cases <- c(5, 10, 20) # Less than 7 days of data
   prior_estimates <- estimate_early_dynamics(cases, 1)
   expect_equal(prior_estimates$prior_growth, 0) # Growth should be 0 if seeding time is <= 1
-})
+})

tests/testthat/test-stan-infections.R

@@ -25,35 +25,35 @@ gt_rev_pmf <- get_delay_rev_pmf(
 # test generate infections
 test_that("generate_infections works as expected", {
   expect_equal(
-    round(generate_infections(c(1, rep(1, 9)), 10, gt_rev_pmf, log(1000), 0, 0, 0), 0),
+    round(generate_infections(c(1, rep(1, 9)), 10, gt_rev_pmf, log(1000), 0, 0, 0, 0, 0), 0),
     c(rep(1000, 10), 995, 996, rep(997, 8))
   )
   expect_equal(
-    round(generate_infections(c(1, rep(1.1, 9)), 10, gt_rev_pmf, log(20), 0.03, 0, 0), 0),
+    round(generate_infections(c(1, rep(1.1, 9)), 10, gt_rev_pmf, log(20), 0.03, 0, 0, 0, 0), 0),
     c(20, 21, 21, 22, 23, 23, 24, 25, 25, 26, 24, 27, 28, 29, 30, 30, 31, 32, 33, 34)
   )
   expect_equal(
-    round(generate_infections(c(1, rep(1.1, 9)), 10, gt_rev_pmf, log(100), 0, 0, 0), 0),
+    round(generate_infections(c(1, rep(1.1, 9)), 10, gt_rev_pmf, log(100), 0, 0, 0, 0, 0), 0),
     c(rep(100, 10), 99, 110, 112, 115, 119, 122, 126, 130, 134, 138)
   )
   expect_equal(
-    round(generate_infections(c(1, rep(1, 9)), 4, gt_rev_pmf, log(500), -0.02, 0, 0), 0),
+    round(generate_infections(c(1, rep(1, 9)), 4, gt_rev_pmf, log(500), -0.02, 0, 0, 0, 0), 0),
     c(500, 490, 480, 471, 382, 403, 408, rep(409, 7))
   )
   expect_equal(
-    round(generate_infections(c(1, rep(1.1, 9)), 4, gt_rev_pmf, log(500), 0, 0, 0), 0),
+    round(generate_infections(c(1, rep(1.1, 9)), 4, gt_rev_pmf, log(500), 0, 0, 0, 0, 0), 0),
     c(rep(500, 4), 394, 460, 475, 489, 505, 520, 536, 553, 570, 588)
   )
   expect_equal(
-    round(generate_infections(c(1, rep(1, 9)), 1, gt_rev_pmf, log(40), numeric(0), 0, 0), 0),
+    round(generate_infections(c(1, rep(1, 9)), 1, gt_rev_pmf, log(40), numeric(0), 0, 0, 0, 0), 0),
     c(40, 8, 11, 12, 12, rep(13, 6))
   )
   expect_equal(
-    round(generate_infections(c(1, rep(1.1, 9)), 1, gt_rev_pmf, log(100), 0.01, 0, 0), 0),
+    round(generate_infections(c(1, rep(1.1, 9)), 1, gt_rev_pmf, log(100), 0.01, 0, 0, 0, 0), 0),
     c(100, 20, 31, 35, 36, 37, 38, 39, 41, 42, 43)
   )
   expect_equal(
-    round(generate_infections(c(1, rep(1, 9)), 10, gt_rev_pmf, log(1000), 0, 100000, 4), 0),
+    round(generate_infections(c(1, rep(1, 9)), 10, gt_rev_pmf, log(1000), 0, 100000, 4, 0, 0), 0),
     c(rep(1000, 10), 995, 996, rep(997, 4), 980, 965, 947, 926)
   )
 })

vignettes/estimate_infections.Rmd

@@ -36,12 +36,12 @@ These infections are then mapped to observations via discrete convolutions with
 The model is initialised before the first observed data point by assuming constant exponential growth for the mean of modelled delays from infection to case report (called `seeding_time` $t_\mathrm{seed}$ in the model):
 
 \begin{align}
-  I_0  &\sim \mathrm{LogNormal}(I_\mathrm{obs}, 0.2) \\
+  I_0  &\sim \mathrm{LogNormal}(I_\mathrm{obs}, \sqrt(I_\mathrm{obs})) \\
   r &\sim \mathrm{Normal}(r_\mathrm{obs}, 0.2)\\
   I_{0 < t \leq t_\mathrm{seed}} &= I_0 \exp  \left(r t \right)
 \end{align}
 
-where $I_{t}$ is the number of latent infections on day $t$, $r$ is the estimate of the initial growth rate, and $I_\mathrm{obs}$ and $r_\mathrm{obs}$ are estimated from the first week of observed data: $I_\mathrm{obs}$ as the mean of reported cases in the first 7 days (or the mean of all cases if fewer than 7 days of data are given), divided by the prior mean reporting fraction if less than 1 (see [Delays and scaling]); and $r_\mathrm{obs}$ as the point estimate from fitting a linear regression model to the first 7 days of data (or all data if fewer than 7 days of data are given),
+where $I_{t}$ is the number of latent infections on day $t$, $r$ is the estimate of the initial growth rate, and $I_\mathrm{obs}$ and $r_\mathrm{obs}$ are estimated from the first week of observed data, respectively, as as the point estimates of intercept and slope from fitting a linear regression model to the first 7 days of data (or all data if fewer than 7 days of data are given),
 
 \begin{equation}
 log(C_t) = a + r_\mathrm{obs} t + \epsilon_t