---
title: "imuGAP, Immunity: Geographic & Age-based Projection"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{imuGAP, Immunity: Geographic & Age-based Projection}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, echo = FALSE, message = FALSE, warning=FALSE}
# List all suggested packages needed for this vignette
required_pkgs <- c(
  "imuGAP", "data.table", "bayesplot", "ggplot2",
  "tidyr", "dplyr"
)

# Check if they are available
pkgs_available <- all(sapply(required_pkgs, requireNamespace, quietly = TRUE))

# If not available, hide the rest of the document or knit dynamically
if (!pkgs_available) {
  knitr::opts_chunk$set(eval = FALSE)
  message("Some suggested packages are missing. Vignette code will not be executed.")
} else {
  # Assign the result so the chunk does not auto-print the logical vector
  # sapply() returns (the setup block should render no output).
  chk <- suppressPackageStartupMessages(
    sapply(required_pkgs, require, character.only = TRUE)
  )
}
```

# Introduction

The name `imuGAP` stands for "Immunity: Geographic & Age-based Projection". This package allows the user to synthesize across multiple data sources to make predictions of vaccination coverage for user-defined populations of interest. For example, one could use the package to:

1. Estimate current vaccination coverage by location across different age groups
2. Estimate coverage (or uptake) for a given birth cohort (e.g. people born in 1990) across their life course (i.e. at each age from birth to current age)
3. Fill in gaps in observed coverage data (e.g. a school that doesn't report vaccination coverage in a certain year)

More specifically, the package provides a [stan](https://mc-stan.org/)-based model for estimating vaccination coverage by location, cohort, and age for childhood infectious diseases, such as measles. The core model represents a target population as having a life-long propensity for vaccination; some proportion, $\phi$, of that population is unlikely to vaccinate and the complementary proportion, $1 - \phi$, is likely to vaccinate. That population then experiences a vaccination rate, $\lambda$, over the model time eras, according to the vaccination eligibility schedule, $\nu$. These core parameters can vary over time and location, in a user-specifiable way.

Focusing just on the core model element, imagine a particular population location $i$ and cohort $a$ (where $a$ denotes the start of the time period when that group was born). If that cohort is now age $t$, and the vaccine schedule for the first dose is $\nu(t)$, the expected fraction of that group to have at least one dose is then:

$$
P(\ge\textrm{1 dose}) = \left(1 - \phi_{i, a}\right) \left(1 - \exp\left\{-\int_a^{t} \lambda_{i, a}(s)\nu(s) d\textrm{s}\right\}\right)
$$

Which is to say, we are representing vaccination coverage via a survival-like model. The model generalizes this approach to the first dose out to arbitrary sequential dose coverage, with each subsequent dose conditional on previous dose receipt.

# Walkthrough of Basic Usage

This walkthrough demonstrates the workflow of fitting the model and predicting coverage on simulated data. The package includes several bundled datasets for demonstration, representing a nested geographic hierarchy (State -> Counties -> Schools) for population uptake of a two dose vaccine, like MMR for measles.

## 1. Preparing and Validating the Input Data

First, let's explore the three required inputs that define the location hierarchy, observation metadata, and the actual coverage observations. The package provides a family of `canonicalize_*` functions to validate, clean, and convert these raw structures into the canonical forms required by the sampler. You can use those directly to help troubleshoot your inputs, as we do in the following examples. However, as shown in the next section, the `sampling()` method also automatically canonicalizes the inputs.

### Location Hierarchy (`locations_sim`)

The locations dataset defines the nesting relationship of the locations in the model. In this simulation, we have a State, which contains three Counties, which in turn contain various Schools. We validate and canonicalize it using `canonicalize_locations()`.

```{r locations}
data("locations_sim", package = "imuGAP")
head(locations_sim)

# Canonicalize and validate
canonical_locations <- canonicalize_locations(locations_sim)
head(canonical_locations)
```

### Coverage Observations (`observations_sim`)

The observations dataset contains the counts of individuals who were vaccinated (`positive`) out of the total sampled (`sample_n`) for each observation. It also includes a `censored` column, which is `1` if the observation is right-censored and `NA` otherwise. We validate and canonicalize it using `canonicalize_observations()`.

```{r observations}
data("observations_sim", package = "imuGAP")
head(observations_sim[, .(obs_id, loc_id, positive, sample_n, censored)])

# Canonicalize and validate
canonical_observations <- canonicalize_observations(observations_sim)
head(canonical_observations)
```

### Observation Metadata (`populations_sim`)

The populations dataset acts as observation metadata, mapping each observation ID (`obs_id`) to the corresponding location, birth cohort, age at observation, vaccine dose, and observation weight. We validate and canonicalize it using `canonicalize_populations()`.

```{r populations}
data("populations_sim", package = "imuGAP")
head(populations_sim)

# Canonicalize and validate
canonical_populations <- canonicalize_populations(
  populations_sim, observations_sim, locations_sim
)
head(canonical_populations)
```

### Validation Failure Examples

To ensure data integrity, the `canonicalize_*` functions enforce strict rules on the input data format and constraints. For example, if we modify the observations data so that the number of `positive` cases exceeds the total sample size `sample_n`, the validation function will raise a clear error:

```{r validation-failure-obs}
# Create a copy with an invalid observation (positive > sample_n)
invalid_obs <- copy(observations_sim[, .(obs_id, loc_id, positive, sample_n, censored)])
invalid_obs[1, positive := sample_n + 10]

# This will fail validation and throw an error:
tryCatch(
  canonicalize_observations(invalid_obs),
  error = function(e) message("Caught expected error: ", e$message)
)
```

Similarly, if the locations data contains duplicate location IDs, `canonicalize_locations()` will detect the duplication and throw an error:

```{r validation-failure-locs}
# Create a copy with a duplicate location ID
invalid_locs <- rbind(
  locations_sim,
  data.frame(loc_id = "Scruggs", parent_id = "State")
)

# This will fail validation:
tryCatch(
  canonicalize_locations(invalid_locs),
  error = function(e) message("Caught expected error: ", e$message)
)
```

See the `canonicalize_*` function documentation for more complete validation requirements.

---

## 2. Fitting the Model

Using the prepared input datasets, we can fit the Bayesian model using `sampling()`. The options for the sampler can be configured using `stan_options()`.

Because compiling the Stan model and running the MCMC chain can take some time, we show the code below without executing it.

```{r sampling-code, eval = FALSE}
fit_sim <- sampling(
  observations_sim, populations_sim, locations_sim,
  stan_opts = stan_options(
    iter = 2000, chains = 4, refresh = 0, seed = 1L
  )
)
```

For this walkthrough, we load the pre-computed fit object `fit_sim` bundled with the package:

```{r load-fit}
data("fit_sim", package = "imuGAP")
```

Once the model is fit, we can extract posterior draws of the model parameters using `extract_imugap()`. For example, let's extract the B-spline coefficients representing the state-level vaccine uptake baseline:

```{r extract-params}
beta_draws <- extract_imugap(fit_sim, pars = "beta_bs")
str(beta_draws)
```

We can also look at trace plots for selected parameters to ensure convergence has been reached.

```{r trace-plot}
bayesplot::mcmc_trace(fit_sim$stanfit,
  pars = c(
    "beta_bs[1]", "beta_bs[2]", "beta_bs[3]",
    "beta_bs[4]", "beta_bs[5]"
  )
) + theme_bw() +
  theme(legend.position = c(.9, .1), legend.justification = c(1, 0))
```

---

## 3. Defining a Target for Predictions

To predict vaccine coverage for a target population (which can include locations or cohorts without direct observations, as long as they exist in the locations hierarchy), we first define a target grid using `create_target()`. Note that predictions can only be made for birth cohorts and locations that have at least some observations included in the estimation run. In other words, the model cannot predict coverage for future birth cohorts or unobserved locations. 

For example, we can generate a "snapshot" prediction target for all locations, including the State and County levels, across ages 1 to 18:

```{r target}
target_sim <- create_target(
  fit = fit_sim, location = unique(locations_sim$loc_id), age = 1:18,
  cohort = max(populations_sim$cohort) - 18, dose = c(1, 2), mode = "snapshot"
)
head(target_sim)
```

---

## 4. Predicting Coverage

Finally, we run `predict()` to generate predicted coverage probabilities for each target population combination. By default it uses every posterior draw; here we pass `posterior_size` to predict over a smaller sub-sample taken from the end of each chain.

Generating predictions also runs the Stan model (in generated quantities mode) and can be time-consuming, so we show the code below without executing it:

```{r predict-code, eval = FALSE}
predict_sim <- predict(object = fit_sim, target = target_sim, posterior_size = 100)
```

Instead, we load the pre-computed prediction results `predict_sim` bundled with the package. This is an object of class `imugap_predict` which contains a 3D draws array (`predict_sim$draws`) with the MCMC draws for each prediction target as well as the target information (`predict_sim$target`).

```{r load-predictions}
data("predict_sim", package = "imuGAP")
```

We can summarize these predictions to get the posterior mean and credible intervals across the target location, age, and doses requested:

```{r summarize-predictions}
# Calculate the posterior mean coverage probability for each location and dose at age 5
summary_predict <- summary(predict_sim)
head(summary_predict)
```

Now let's visualize the results. First we will take a look at overall state coverage by cohort. Note that the lower coverage among 5 year olds is due to them only having been eligible for their second dose for one year.   

```{r state-viz}
summary_predict |>
  subset(loc_id == "State" & dose == 2 & age > 4) |>
  ggplot() +
  aes(x = age) +
  geom_line(aes(y = q50)) +
  geom_ribbon(aes(ymin = q2_5, ymax = q97_5), alpha = 0.5) +
  theme_bw() +
  scale_x_continuous(breaks = 5:18, minor_breaks = NULL) +
  labs(x = "Age", y = "Coverage", title = "State-level two dose coverage")
```

We can also look at the trend in coverage by age at the county level. Note that they follow the same trend as the state but with differing magnitude.

```{r county-viz}
summary_predict |>
  subset(loc_id %in% c("Scruggs", "Simone", "Watson") & dose == 2 & age > 4) |>
  ggplot() +
  aes(x = age) +
  geom_line(aes(y = q50, color = loc_id)) +
  geom_ribbon(aes(ymin = q2_5, ymax = q97_5, fill = loc_id), alpha = 0.2) +
  theme_bw() +
  theme(
    legend.position = "inside", legend.position.inside = c(.2, 0.05),
    legend.justification.inside = c(0, 0)
  ) +
  scale_x_continuous(breaks = 5:18, minor_breaks = NULL) +
  scale_color_discrete(NULL, aesthetics = c("color", "fill")) +
  labs(
    x = "Age", y = "Coverage", title = "County-level two dose coverage"
  )
```

Next, we can look at the distribution of school-level coverage estimates by age group. Let's focus on Scruggs County as an example. 

```{r grade-viz}
scruggs_schools <- locations_sim[parent_id == "Scruggs", loc_id]
summary_predict |>
  subset(
    loc_id %in% scruggs_schools & dose == 2 & age > 4
  ) |>
  ggplot() +
  geom_boxplot(aes(x = factor(age), y = q50)) +
  theme_bw() +
  labs(
    x = "Age", y = "Coverage",
    title = "Distribution of School-Level Coverage For Scruggs County"
  )
```

Finally let's look at some selected schools and see how their predicted coverage compared to true underlying coverage from the data simulation process.

```{r}
schools <- c(
  "Towhee Children's Academy", # ~380 per grade
  "Flycatcher Elementary", # ~110 per grade
  "Sparrow School" # ~60 per grade
)

# Subset to targets of interest (all retained posterior draws)
predict_sub <- predict_sim |>
  subset(loc_id %in% schools & dose == 2 & age > 4)

# Get the pre-computed background coverage matching the subsetted target
latent_ref <- copy(predict_sub$target)
latent_ref$coverage <- latent_params_sim$coverage[predict_sub$target$obs_id]


# Convert predictions to a long-format data.frame
draws_df <- as.data.frame(predict_sub)

# Now plot it all
ggplot() +
  aes(age, coverage, color = loc_id) +
  geom_point(
    data = draws_df,
    alpha = 1 / 256, shape = 16,
    position = position_jitterdodge(
      dodge.width = 0.5,
      jitter.width = 0.15
    )
  ) +
  geom_point(
    data = latent_ref,
    mapping = aes(shape = "True value"),
    fill = NA
  ) +
  theme_bw() +
  scale_shape_manual(
    name = "",
    values = c("True value" = 24)
  ) +
  scale_color_discrete(NULL, aesthetics = c("color", "fill")) +
  scale_x_continuous(breaks = 5:18, minor_breaks = NULL) +
  theme(legend.position = "bottom") +
  labs(color = "School", x = "Age", y = "Coverage")
```

