---
title: "Statistical Modelling for Infectious Disease Management - Contagious period"
output:
  rmarkdown::html_vignette:
    fig_width: 7 
    fig_height: 5
fontsize: 12pt
geometry: margin=1in
vignette: >
  %\VignetteIndexEntry{Statistical Modelling for Infectious Disease Management - Contagious period}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

knitr::knit_hooks$set(
  source = function(x, options) {
    hook.r <- function(x, options) {
      fence <- "```"
      language <- tolower(options$engine)
      if (language == 'node') language <- 'javascript'
      if (!options$highlight) language <- 'text'
      if(!is.null(options$foldcode)) {
      paste0('\n\n', "<details><summary>Source</summary>\n", fence, language, '\n', x, fence,  '\n\n', "</details>\n")
      } else {
              paste0('\n\n', fence, language, '\n', x, fence,  '\n\n')
      }
    }
    x <- knitr:::hilight_source(x, 'markdown', options)
    hook.r(
      paste(c(
        x, 
        ''
      ), collapse = '\n'), 
      options
    )
  }
)

Sys.setlocale("LC_TIME", "C")

```

```{r setup, echo = FALSE, message = FALSE}
library(smidm)
library(ggplot2)
library(dplyr)
library(hdrcde)

```

## Question

A person begins to show symptoms of COVID-19 on 28.12.2021. When has the person been infectious?

## Generating a data frame with dates and infectiousness probability using \newline `get_infectiousness_density`

If the person shows symptoms, the function `get_infectiousness_density()` can be used to answer the question.
The function `get_infectiousness_density()` creates a dataframe containing the infectiousness at a particular date/time.

### Inputs

The following input arguments are needed for the function `get_infectiousness_density()`:

The `symptom_begin_date` is the date when the person started to have symptoms. 

Then, the `max_infectious_days` is needed, which defines the interval length of the distribution output.  

The other three inputs `infectiousness_shift`, `shape_infectiousness_gamma` and `rate_infectiousness_gamma` are the parameters of the gamma distribution for the infectiousness profile.

```{r get_infectiousness_density_statement}
symptom_begin_date <- as.Date("2021-12-28")
infectiousness_shift <- 12.272481
max_infectious_days <- 24
shape_infectiousness_gamma <- 20.516508
rate_infectiousness_gamma <- 1.592124

infectious_df <- get_infectiousness_density(symptom_begin_date,
                                            infectiousness_shift,
                                            max_infectious_days,
                                            shape_infectiousness_gamma,
                                            rate_infectiousness_gamma)

```

### Methodology

The default values of the gamma distribution are taken from the paper He et al [1]. In this paper an analysis of COVID-19 viral shedding and transmissibility was conducted and a gamma distribution for the infectious period of cases was estimated based on their symptom onset dates. 

### Output

The function call returns the following data frame:

```{r get_infectiousness_density_result, echo = FALSE, message = FALSE}
knitr::kable(infectious_df[100:109, ],
             caption = "values 100 to 109 of resulting data frame")

```

The function `get_infectiousness_density()` creates an interval of length `max_infectious_days` and the resulting data frame shows the resulting density of the gamma distribution for each hour of the period. This density can be used for calculating the most probable period to infect other people. 

## Visualization example of the data frame of \newline `get_infectiousness_density` 

The following code generates a plot with the gamma distribution of the infectiousness profile and the 80% and 95% high density intervals.

```{r .calculate_qstart_qend, foldcode = TRUE}
.calculate_qstart_qend <- function(probability, df) {
    hdr_df <- hdr(den = data.frame(x = 1:length(df$distribution), y = df$distribution), p = probability * 100)$hdr
    qstart <- (hdr_df[1, 1] - 1) / 24
    qend   <- (hdr_df[1, 2] - 1) / 24
  return(list("qstart" = qstart, "qend" = qend))
}

period_80 <- .calculate_qstart_qend(0.8, infectious_df) 
period_95 <- .calculate_qstart_qend(0.95, infectious_df)

```

```{r .shade_curve, foldcode = TRUE}
.shade_curve <- function(df, qstart, qend, fill = "red", alpha = 0.4) {
  subset_df <- df[floor(qstart * 24):ceiling(qend * 24), ]
  geom_area(data = subset_df,
            aes(x = x, y = y), 
            fill = fill, 
            color = NA, 
            alpha = alpha)
}

```

```{r parameters for visualization of get_infection_density, foldcode = TRUE}
  symptom_begin_date <- as.Date("2021-12-28")

  df <- infectious_df
  
  symp_date_posixct_start <- as.POSIXct(format(as.POSIXct(symptom_begin_date, tz = "CET"), "%Y-%m-%d"))
  symp_date_posixct_end <- as.POSIXct(format(as.POSIXct(symptom_begin_date + 1, tz = "CET"), "%Y-%m-%d"))
  symp_date_posixct_mid <- symp_date_posixct_start - as.numeric(difftime(symp_date_posixct_start,
                           symp_date_posixct_end, units = "hours")) / 2 * 3600 
  
```
  
```{r visualization of get_infection_density, foldcode = TRUE}  
  g <- ggplot() +

    scale_x_datetime(breaks = scales::date_breaks("1 days"), labels = scales::date_format("%d-%m-%Y")) +
    theme(axis.text.x = element_text(angle = 90)) +
    # scale_x_continuous(breaks = x_tick,
    #                    labels = x_label) +
    # theme(axis.ticks.x = element_line(color = c(rbind(rep("black", length(x_label)/2), rep(NA, length(x_label)/2))), linetype = 2, size = 1))+
    geom_path(aes(x = df$dates, y = df$distribution, color = "red")) +
    .shade_curve(df = data.frame(x = df$dates, y = df$distribution),
                 period_80$qstart,
                 period_80$qend) +
    .shade_curve(df = data.frame(x = df$dates, y = df$distribution),
                 period_95$qstart,
                 period_95$qend,
                 alpha = 0.2) +
    geom_rect(data = data.frame(xmin = symp_date_posixct_start,
                                xmax = symp_date_posixct_end,
                                ymin = -Inf,
                                ymax = Inf),
              aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
              fill = "brown", alpha = 0.3) +
    geom_label(aes(x = symp_date_posixct_mid, y = 0.9*max(df$distribution), label = "symptom\nonset"),
              colour = "brown", fill = "white", size = 5, label.size = NA) +
    ylab("probability") +
    xlab("timeline") +
    labs(color = 'Verteilung') +
    # ggtitle("Visualization of get_infection_density") +
    theme(legend.position = "none", text = element_text(size = 16*5/5)) +
    theme(axis.text.x = element_text(colour = "black", face = "bold", angle = 30, hjust = 1)) +
    theme(axis.title.x = element_text(colour = "black", face = "bold")) +
    theme(axis.text.y = element_text(colour = "gray50")) +
    theme(axis.title.y = element_text(colour = "gray50"))

  g
  
```

## Literature

[1] He X, Lau EHY, Wu P, Deng X, Wang J, Hao X, Lao YC, Wong JY, Guan Y, Tan X, Mo X, Chen Y, Liao B, Chen W, Hu F, Zhang Q, Zhong M, Wi Y, Zhao L, Zhang F, Cowling BJ, Li F, Leung GM. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nature Medicine. 2020; 26: 672–675. 
