---
title: epicrop
author: Adam H. Sparks
output:
  rmarkdown::html_document:
    theme: journal
bibliography: references.bib 
vignette: >
  %\VignetteIndexEntry{epicrop}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
  %\VignetteDepends{ggplot2}
  %\VignetteDepends{tidyr}
---



<!-- STOP! Do not edit this file. This file is generated from epicrop.Rmd.orig -->

## Introduction to {epicrop}

{epicrop} provides an R package of the 'EPIRICE' model as described in [@Savary2012], the modified EPIRICE model as described in [@Kim2015], the 'EPIWHEAT' model as described in [@Savary2015] and a generic SEIR model function, `seir()`, for modelling crop disease epidemics.
The model uses daily weather data to estimate disease intensity.
A function, `get_wth()`, is provided to simplify downloading weather data via the [{nasapower}](https://cran.r-project.org/package=nasapower) package [@Sparks2018] and predict disease intensity of five rice diseases using a generic SEIR model [@Zadoks1971] function, `seir()`.

For 'EPIRICE', default values derived from the literature suitable for modelling unmanaged disease intensity of five rice diseases, bacterial blight (`bacterial_blight()`); brown spot (`brown_spot()`); leaf blast (`leaf_blast()`); sheath blight (`sheath_blight()`) and tungro (`tungro()`) and two modified by Kim _et al._ [-@Kim2015], (`modified_kim_leaf_blast()` and `helper_modified_kim_sheath_blight()`), are provided.
The modified Kim versions provide leaf blast and sheath blight models that include additional weather parameters and modified equations to better reflect disease progress under certain environmental conditions on the Korean Peninsula.
The 'EPIWHEAT' model includes two wheat diseases, leaf rust (`leaf_rust()`) and septoria tritici blotch (`s_tritici_blotch()`), with default values derived from the literature suitable for modelling unmanaged disease intensity of these diseases.

Using the package functions is designed to be straightforward for modelling rice disease risks, but flexible enough to accommodate other pathosystems using the `seir()` function.
If you are interested in modelling other pathosystems, please refer to [-@Savary2012] for the development of the parameters that were used for the rice diseases as derived from the existing literature and are implemented in the individual disease model functions.

### Getting started

Load the library.


``` r
library(epicrop)
```

### Get weather data

The most simple way to use the model is to download weather data from NASA POWER using `get_wth()`, which provides the data in a format suitable for use in the model and is freely available.
See the help file for `naspower::get_power()` for more details of this functionality and details on the data [@Sparks2018].


``` r
# Fetch weather for year 2000 season at the IRRI Zeigler Experiment Station
wth <- get_wth(
  lonlat = c(121.25562, 14.6774),
  dates = c("2000-01-01", "2000-12-31")
)

wth
```

```
## Key: <YYYYMMDD>
##        YYYYMMDD   DOY  TEMP  TMIN  TMAX  RHUM  RAIN     LAT      LON
##          <IDat> <int> <num> <num> <num> <num> <num>   <num>    <num>
##   1: 2000-01-01     1 24.38 22.85 27.46 91.25 14.93 14.6774 121.2556
##   2: 2000-01-02     2 24.28 22.68 27.42 90.88  6.96 14.6774 121.2556
##   3: 2000-01-03     3 23.82 22.17 26.96 88.36  2.28 14.6774 121.2556
##   4: 2000-01-04     4 23.68 21.90 27.14 88.00  0.87 14.6774 121.2556
##   5: 2000-01-05     5 24.11 21.54 28.18 88.12  0.43 14.6774 121.2556
##  ---                                                                
## 362: 2000-12-27   362 24.46 22.90 26.28 92.49 21.15 14.6774 121.2556
## 363: 2000-12-28   363 24.64 23.32 27.28 91.92  6.01 14.6774 121.2556
## 364: 2000-12-29   364 24.58 22.51 27.90 90.79  5.49 14.6774 121.2556
## 365: 2000-12-30   365 25.31 22.84 28.84 86.25  2.07 14.6774 121.2556
## 366: 2000-12-31   366 24.47 21.63 28.63 87.83  3.45 14.6774 121.2556
```

### Predict bacterial blight

All of the helper family of functions work in exactly the same manner.
You provide them with weather data and an emergence date, that falls within the weather data provided, and they will return a data frame of disease intensity over the season and other values associated with the model.
See the help file for `seir()` for more on the values returned.


``` r
# Predict bacterial blight intensity for the year 2000 wet season at IRRI
bb_wet <- bacterial_blight(wth, emergence = "2000-07-01")
```


``` r
summary(bb_wet)
```

```
##      simday           dates                sites            latent      
##  Min.   :  1.00   Min.   :2000-07-01   Min.   :  0.00   Min.   : 0.000  
##  1st Qu.: 30.75   1st Qu.:2000-07-30   1st Qu.: 16.28   1st Qu.: 0.000  
##  Median : 60.50   Median :2000-08-29   Median : 64.21   Median : 1.000  
##  Mean   : 60.50   Mean   :2000-08-29   Mean   : 57.02   Mean   : 3.744  
##  3rd Qu.: 90.25   3rd Qu.:2000-09-28   3rd Qu.: 98.22   3rd Qu.: 5.080  
##  Max.   :120.00   Max.   :2000-10-28   Max.   :100.00   Max.   :18.014  
##    infectious        removed         senesced        rateinf            rlex  
##  Min.   : 0.000   Min.   : 0.00   Min.   : 0.00   Min.   :0.0000   Min.   :0  
##  1st Qu.: 1.000   1st Qu.: 0.00   1st Qu.: 0.00   1st Qu.:0.0000   1st Qu.:0  
##  Median : 6.838   Median : 1.00   Median : 1.00   Median :0.0000   Median :0  
##  Mean   :17.062   Mean   :12.88   Mean   :13.39   Mean   :0.5348   Mean   :0  
##  3rd Qu.:34.146   3rd Qu.:19.56   3rd Qu.:21.82   3rd Qu.:0.6962   3rd Qu.:0  
##  Max.   :53.168   Max.   :61.49   Max.   :62.04   Max.   :4.0939   Max.   :0  
##    rtransfer         rremoved         rgrowth    rsenesced         diseased    
##  Min.   :0.0000   Min.   :0.0000   Min.   :0   Min.   :0.0000   Min.   : 0.00  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0   1st Qu.:0.0000   1st Qu.: 1.78  
##  Median :0.0000   Median :0.0000   Median :0   Median :0.0000   Median :37.14  
##  Mean   :0.5348   Mean   :0.5125   Mean   :0   Mean   :0.5170   Mean   :33.68  
##  3rd Qu.:0.6962   3rd Qu.:0.6962   3rd Qu.:0   3rd Qu.:0.6962   3rd Qu.:64.18  
##  Max.   :4.0939   Max.   :4.0939   Max.   :0   Max.   :4.0939   Max.   :64.69  
##    intensity           lat             lon       
##  Min.   :0.0000   Min.   :14.68   Min.   :121.3  
##  1st Qu.:0.0178   1st Qu.:14.68   1st Qu.:121.3  
##  Median :0.3602   Median :14.68   Median :121.3  
##  Mean   :0.4158   Mean   :14.68   Mean   :121.3  
##  3rd Qu.:0.7351   3rd Qu.:14.68   3rd Qu.:121.3  
##  Max.   :1.0000   Max.   :14.68   Max.   :121.3
```

## Plotting using {ggplot2}

The data are in a wide format by default and need to be converted to long format for use in {ggplot2} if you wish to plot more than one variable at a time.


``` r
library(ggplot2)
library(tidyr)
```

### Wet season sites

The model records the number of sites for each bin daily; this can be graphed as follows.


``` r
dat <- pivot_longer(
  bb_wet,
  cols = c("diseased", "removed", "latent", "infectious"),
  names_to = "site",
  values_to = "value"
)

ggplot(data = dat,
       aes(
         x = dates,
         y = value,
         shape = site,
         linetype = site
       )) +
  labs(y = "Sites",
       x = "Date") +
  geom_line(aes(group = site, colour = site)) +
  geom_point(aes(colour = site)) +
  theme_classic()
```

![Site states over time for bacterial blight. Results for wet season year 2000 at IRRI Zeigler Experiment Station shown. Weather data used to run the model were obtained from the NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program.](wet_plot_sites-1.png)

### Wet season intensity

Plotting intensity over time does not require any data manipulation.


``` r
ggplot(data = bb_wet,
       aes(x = dates,
           y = intensity * 100)) +
  labs(y = "Intensity (%)",
       x = "Date") +
  geom_line() +
  geom_point() +
  theme_classic()
```

![Wet season disease intensity over time for bacterial blight. Results for wet season year 2000 at IRRI Zeigler Experiment Station shown.  Weather data used to run the model were obtained from the NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program.](wet_plot_intensity-1.png)

## Comparing epidemics

The most common way to compare disease epidemics in botanical epidemiology is to use the area under the disease progress curve (AUDPC) [@Shaner1977].
The AUDPC value for a given simulated season is returned as a part of the output from any of the disease simulations offered in {epicrop}.
You can find the value in the `AUDPC` column.
We can compare the dry season with the wet season by looking at the AUDPC values for both seasons.


``` r
bb_dry <- bacterial_blight(wth = wth, emergence = "2000-01-05")
summary(bb_dry)
```

```
##      simday           dates                sites            latent     
##  Min.   :  1.00   Min.   :2000-01-05   Min.   :  0.00   Min.   : 0.00  
##  1st Qu.: 30.75   1st Qu.:2000-02-03   1st Qu.: 25.50   1st Qu.: 0.00  
##  Median : 60.50   Median :2000-03-04   Median : 64.45   Median : 1.00  
##  Mean   : 60.50   Mean   :2000-03-04   Mean   : 58.57   Mean   : 3.13  
##  3rd Qu.: 90.25   3rd Qu.:2000-04-03   3rd Qu.: 97.56   3rd Qu.: 4.81  
##  Max.   :120.00   Max.   :2000-05-03   Max.   :100.00   Max.   :21.80  
##    infectious        removed         senesced        rateinf            rlex  
##  Min.   : 0.000   Min.   : 0.00   Min.   : 0.00   Min.   :0.0000   Min.   :0  
##  1st Qu.: 1.000   1st Qu.: 0.00   1st Qu.: 0.00   1st Qu.:0.0000   1st Qu.:0  
##  Median : 8.491   Median : 1.00   Median : 1.00   Median :0.0000   Median :0  
##  Mean   :14.181   Mean   :12.42   Mean   :12.85   Mean   :0.4471   Mean   :0  
##  3rd Qu.:28.857   3rd Qu.:21.71   3rd Qu.:21.71   3rd Qu.:0.3144   3rd Qu.:0  
##  Max.   :40.572   Max.   :51.11   Max.   :51.11   Max.   :7.0252   Max.   :0  
##    rtransfer         rremoved        rgrowth    rsenesced        diseased     
##  Min.   :0.0000   Min.   :0.000   Min.   :0   Min.   :0.000   Min.   : 0.000  
##  1st Qu.:0.0000   1st Qu.:0.000   1st Qu.:0   1st Qu.:0.000   1st Qu.: 2.452  
##  Median :0.0000   Median :0.000   Median :0   Median :0.000   Median :34.553  
##  Mean   :0.4471   Mean   :0.426   Mean   :0   Mean   :0.426   Mean   :29.734  
##  3rd Qu.:0.3144   3rd Qu.:0.000   3rd Qu.:0   3rd Qu.:0.000   3rd Qu.:53.122  
##  Max.   :7.0252   Max.   :7.025   Max.   :0   Max.   :7.025   Max.   :53.933  
##    intensity            lat             lon       
##  Min.   :0.00000   Min.   :14.68   Min.   :121.3  
##  1st Qu.:0.02447   1st Qu.:14.68   1st Qu.:121.3  
##  Median :0.34238   Median :14.68   Median :121.3  
##  Mean   :0.36664   Mean   :14.68   Mean   :121.3  
##  3rd Qu.:0.55330   3rd Qu.:14.68   3rd Qu.:121.3  
##  Max.   :1.00000   Max.   :14.68   Max.   :121.3
```

### Dry season intensity

Check the disease progress curve for the dry season.


``` r
ggplot(data = bb_dry,
       aes(x = dates,
           y = intensity * 100)) +
  labs(y = "Intensity (%)",
       x = "Date") +
  geom_line() +
  geom_point() +
  theme_classic()
```

![Dry season site states over time for bacterial blight. Results for dry season year 2000 at IRRI Zeigler Experiment Station shown. Weather data used to run the model were obtained from the NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program.](dry_plot_intensity-1.png)

The AUDPC values can be viewed directly from the attributes of the `data.table` outputs above, but we can also create a small helper function to extract them.


``` r
# Dry season
get_audpc(bb_dry)
```

```
## [1] 43.49663
```

``` r
# Wet season
get_audpc(bb_wet)
```

```
## [1] 49.39145
```

The AUDPC of the wet season is greater than that of the dry season.
Checking the data and referring to the curves, the wet season intensity reaches a peak value of 100% and the dry season tops out at 100%.
So, this meets the expectations that the wet season AUDPC is higher than the dry season, which was predicted to have less disease intensity.

# References

