Variable Name	Dimension	Description
$\mathit{d}$	$T\times 1$	$d_i$ is the number of deaths that occurred on the day i.
$\mathit{r}$	$T\times T$	$r_{ij}$ is number of death recorded for day i at day j. Note that $r_{ij}$ for $i<j$ is not defined.
$\mathit{p}$	$T\times T$	$p_{ij}$ is the probability of that a death for day i not yet recorded is recorded at day j. Note that $p_{ij}$ for $i<j$ is not defined.
$\mathit{\alpha }$	$K\times 1$	latent prior parameter for $\mathit{p}$
$\mathit{\beta }$	$K\times 1$	latent prior parameter for $\mathit{p}$
${\mathit{\alpha }}^H$	$2\times 1$	parameter for the probability, $\mathit{p}$ for holiday adjustment
${\mathit{\beta }}^H$	$2\times 1$	parameter for the probability, $\mathit{p}$ for holiday adjustment
$\mu$	$T\times 1$	${\mu }_i$ is the intensity of the expected number of deaths at day i
${\sigma }^2$	$1\times 1$	variation of the random walk prior of the log intensity
$\varphi$	$1\times 1$	overdispersion parameter for the negative binomial distribution
$p_0$	$1\times 1$	probability of reporting for a low reporting event
$\pi$	$1\times 1$	probability of a low reporting event