Introduction to {epicrop}
{epicrop} provides an R package of the ‘EPIRICE’ model as described
in (Savary et al. 2012), the modified
EPIRICE model as described in (Kim et al.
2015), the ‘EPIWHEAT’ model as described in (Savary et al. 2015) and a generic SEIR model
function, epicrop_sim(), 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}
package (Sparks 2018) and predict disease
intensity of five rice diseases using a generic SEIR model (Zadoks 1971) function,
epicrop_sim().
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. (2015), (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 epicrop_sim() function. If you are
interested in modelling other pathosystems, please refer to (2012) 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.
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 (Sparks 2018).
# 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## YYYYMMDD DOY TEMP TMIN TMAX RHUM RAIN LAT LON
## <Date> <int> <num> <num> <num> <num> <num> <num> <num>
## 1: 1999-12-31 365 24.41 23.34 26.20 93.41 4.92 14.6774 121.2556
## 2: 2000-01-01 1 24.38 22.85 27.46 91.25 14.93 14.6774 121.2556
## 3: 2000-01-02 2 24.28 22.68 27.42 90.88 6.96 14.6774 121.2556
## 4: 2000-01-03 3 23.82 22.17 26.96 88.36 2.28 14.6774 121.2556
## 5: 2000-01-04 4 23.68 21.90 27.14 88.00 0.87 14.6774 121.2556
## ---
## 363: 2000-12-27 362 24.46 22.90 26.28 92.49 21.15 14.6774 121.2556
## 364: 2000-12-28 363 24.64 23.32 27.28 91.92 6.01 14.6774 121.2556
## 365: 2000-12-29 364 24.58 22.51 27.90 90.79 5.49 14.6774 121.2556
## 366: 2000-12-30 365 25.31 22.84 28.84 86.25 2.07 14.6774 121.2556
## 367: 2000-12-31 366 24.47 21.63 28.63 87.83 3.45 14.6774 121.2556Predict 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 epicrop_sim() for
more on the values returned.
# Predict bacterial blight intensity for the year 2000 wet season at IRRI
bb_wet <- bacterial_blight(wth, emergence = "2000-07-01")
summary(bb_wet)## simday dates sites latent
## Min. : 1.00 Min. :2000-06-30 Min. : 108.7 Min. : 0.000
## 1st Qu.: 30.75 1st Qu.:2000-07-29 1st Qu.: 989.0 1st Qu.: 0.933
## Median : 60.50 Median :2000-08-28 Median :1712.0 Median : 17.979
## Mean : 60.50 Mean :2000-08-28 Mean :1590.1 Mean : 60.023
## 3rd Qu.: 90.25 3rd Qu.:2000-09-27 3rd Qu.:2278.9 3rd Qu.:108.208
## Max. :120.00 Max. :2000-10-27 Max. :2586.3 Max. :234.072
## infectious removed senesced rateinf
## Min. : 0.00 Min. : 0.00 Min. : 1.0 Min. : 0.000
## 1st Qu.: 1.00 1st Qu.: 0.00 1st Qu.: 122.7 1st Qu.: 0.000
## Median : 26.79 Median : 1.00 Median : 665.4 Median : 0.866
## Mean :267.60 Mean : 56.04 Mean : 775.2 Mean :12.262
## 3rd Qu.:595.15 3rd Qu.: 28.87 3rd Qu.:1390.0 3rd Qu.:22.356
## Max. :970.87 Max. :612.71 Max. :1895.1 Max. :56.827
## rlex rtransfer rremoved rgrowth
## Min. :0 Min. : 0.00 Min. : 0.000 Min. : 9.688
## 1st Qu.:0 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.:19.569
## Median :0 Median : 0.00 Median : 0.000 Median :32.024
## Mean :0 Mean :11.83 Mean : 5.106 Mean :38.908
## 3rd Qu.:0 3rd Qu.:22.36 3rd Qu.: 2.517 3rd Qu.:57.391
## Max. :0 Max. :56.83 Max. :48.174 Max. :79.585
## rsenesced diseased intensity lat
## Min. : 1.000 Min. : 0.000 Min. :0.000000 Min. :14.68
## 1st Qu.: 9.323 1st Qu.: 2.429 1st Qu.:0.002382 1st Qu.:14.68
## Median :17.120 Median : 46.853 Median :0.017969 Median :14.68
## Mean :15.793 Mean : 383.668 Mean :0.122956 Mean :14.68
## 3rd Qu.:22.789 3rd Qu.: 846.783 3rd Qu.:0.289563 3rd Qu.:14.68
## Max. :25.863 Max. :1471.442 Max. :0.426437 Max. :14.68
## lon
## Min. :121.3
## 1st Qu.:121.3
## Median :121.3
## Mean :121.3
## 3rd Qu.:121.3
## Max. :121.3Plotting 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.
## Posit Community (formerly RStudio Community) is a great place to
## get help: https://forum.posit.co/c/tidyverseWet season sites
The model records the number of sites for each bin daily; this can be graphed as follows.
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()
Wet season intensity
Plotting intensity over time does not require any data manipulation.
ggplot(data = bb_wet,
aes(x = dates,
y = intensity * 100)) +
labs(y = "Intensity (%)",
x = "Date") +
geom_line() +
geom_point() +
theme_classic()
Comparing epidemics
The most common way to compare disease epidemics in botanical
epidemiology is to use the area under the disease progress curve (AUDPC)
(Shaner and Finney 1977). 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.
bb_dry <- bacterial_blight(wth = wth, emergence = "2000-01-05")
summary(bb_dry)## simday dates sites latent
## Min. : 1.00 Min. :2000-01-04 Min. : 108.7 Min. : 0.000
## 1st Qu.: 30.75 1st Qu.:2000-02-02 1st Qu.: 989.5 1st Qu.: 0.000
## Median : 60.50 Median :2000-03-03 Median :2513.1 Median : 2.444
## Mean : 60.50 Mean :2000-03-03 Mean :1865.1 Mean : 14.522
## 3rd Qu.: 90.25 3rd Qu.:2000-04-02 3rd Qu.:2590.0 3rd Qu.: 19.080
## Max. :120.00 Max. :2000-05-02 Max. :2714.2 Max. :148.175
## infectious removed senesced rateinf
## Min. : 0.00 Min. : 0.00 Min. : 1.0 Min. : 0.000
## 1st Qu.: 1.00 1st Qu.: 0.00 1st Qu.: 122.7 1st Qu.: 0.000
## Median : 18.49 Median : 1.00 Median : 665.3 Median : 0.000
## Mean : 74.78 Mean : 21.38 Mean : 824.8 Mean : 2.904
## 3rd Qu.:141.06 3rd Qu.: 19.49 3rd Qu.:1455.2 3rd Qu.: 0.562
## Max. :285.00 Max. :154.75 Max. :2213.8 Max. :43.425
## rlex rtransfer rremoved rgrowth
## Min. :0 Min. : 0.000 Min. : 0.00 Min. : 9.688
## 1st Qu.:0 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.:25.940
## Median :0 Median : 0.000 Median : 0.00 Median :33.960
## Mean :0 Mean : 2.904 Mean : 1.29 Mean :41.597
## 3rd Qu.:0 3rd Qu.: 0.562 3rd Qu.: 0.00 3rd Qu.:57.653
## Max. :0 Max. :43.425 Max. :25.25 Max. :79.439
## rsenesced diseased intensity lat
## Min. : 1.000 Min. : 0.000 Min. :0.000000 Min. :14.68
## 1st Qu.: 9.333 1st Qu.: 2.166 1st Qu.:0.002226 1st Qu.:14.68
## Median :24.958 Median : 33.297 Median :0.012592 Median :14.68
## Mean :18.448 Mean :110.689 Mean :0.032582 Mean :14.68
## 3rd Qu.:25.900 3rd Qu.:200.347 3rd Qu.:0.063808 3rd Qu.:14.68
## Max. :27.142 Max. :348.522 Max. :0.108523 Max. :14.68
## lon
## Min. :121.3
## 1st Qu.:121.3
## Median :121.3
## Mean :121.3
## 3rd Qu.:121.3
## Max. :121.3Dry season intensity
Check the disease progress curve for the dry season.
ggplot(data = bb_dry,
aes(x = dates,
y = intensity * 100)) +
labs(y = "Intensity (%)",
x = "Date") +
geom_line() +
geom_point() +
theme_classic()
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.
# Dry season
get_audpc(bb_dry)## [1] 3.874294
# Wet season
get_audpc(bb_wet)## [1] 14.56478The 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 43% and the dry season tops out at 11%. So, this meets the expectations that the wet season AUDPC is higher than the dry season, which was predicted to have less disease intensity.