---
title: "Model definition: estimate_truncation()"
output: rmarkdown::html_vignette
bibliography: library.bib
csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa-numeric-superscript-brackets.csl
vignette: >
  %\VignetteIndexEntry{Model definition: estimate_truncation()}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

This model deals with the problem of _nowcasting_, or adjusting for right-truncation in reported count data.
This occurs when the quantity being observed, for example cases, hospitalisations or deaths, is reported with a delay, resulting in an underestimation of recent counts.
The `estimate_truncation()` model infers parameters of the underlying delay distribution from multiple snapshots of past data.
This can be thought of as a Bayesian form of the chain-ladder nowcasting approach implemented in the [baselinenowcast](https://baselinenowcast.epinowcast.org/) package, with the added benefit of joint uncertainty quantification and delay estimation.
For settings requiring time-varying delays or more detailed reporting structure, see the [epinowcast](https://package.epinowcast.org) package.

Both `estimate_truncation()` and `estimate_dist()` return a delay distribution that downstream functions such as `estimate_infections()`, `estimate_secondary()`, or a further call to `estimate_truncation()` can consume. The main difference is the data they expect: `estimate_truncation()` takes successive snapshots of the same aggregate counts (a reporting triangle), while `estimate_dist()` takes individual-level (linelist) data with primary and secondary event dates.
Because it works from aggregate counts rather than individual records, `estimate_truncation()` also fits an observation model for the counts on top of the delay, whereas `estimate_dist()` estimates the delay distribution alone.
As a rough decision rule, use `estimate_dist()` when you have a linelist and `estimate_truncation()` when you have repeated snapshots of aggregate counts.

## Model

Given snapshots $C^{i}_{t}$ reflecting reported counts for time $t$ where $i=1\ldots S$ is in order of recency (earliest snapshots first) and $S$ is the number of past snapshots used for estimation, we infer the parameters $\boldsymbol{\theta}$ of a discrete truncation distribution with cumulative mass function $Z(\tau | \boldsymbol{\theta})$.
The truncation distribution can be any family supported by `dist_spec` (e.g. log-normal, gamma).

The model assumes that final counts $D_{t}$ are related to observed snapshots via the truncation distribution such that

\begin{equation}
C^{i < S}_{t} \sim F\left(Z(T_i - t | \boldsymbol{\theta}) \cdot D(t) + \sigma\right)
\end{equation}

where $T_i$ is the date of the final observation in snapshot $i$, $Z(\tau)$ is defined to be zero for negative values of $\tau$, $\sigma$ is an additive noise term (controlled via the `noise` argument), and $F$ is the observation model (Poisson or negative binomial, controlled via `obs_opts()`).

The final counts $D_{t}$ are estimated from the most recent snapshot as

\begin{equation}
D_t = \frac{C^{S}_{t}}{Z(T_S - t | \boldsymbol{\theta})}
\end{equation}

### Priors

\begin{align}
\boldsymbol{\theta} &\sim \text{as specified by } \texttt{trunc\_opts()} \\
\varphi &\sim \text{as specified by } \texttt{obs\_opts()} \quad \text{(negative binomial only)} \\
\sigma &\sim \text{as specified by } \texttt{noise}
\end{align}