This function is originally used by specific disease models in ‘EPIRICE’ to model disease intensity of several rice diseases. Given proper values it can be used with other pathosystems as well.
SEIR(
wth,
emergence,
onset,
duration,
rhlim,
rainlim,
H0,
I0,
RcA,
RcT,
RcOpt,
p,
i,
Sx,
a,
RRS,
RRG
)
a data.frame
of weather on a daily time-step containing data
with the following field names.
Field Name | Value |
YYYYMMDD | Date as Year Month Day (ISO8601) |
DOY | Consecutive day of year, commonly called "Julian date" |
TEMP | Mean daily temperature (°C) |
RHUM | Mean daily relative humidity (%) |
RAIN | Mean daily rainfall (mm) |
LAT | Optional latitude of weather observation, see LAT/LON Note |
LON | Optional longitude of weather observation, see LAT/LON Note |
expected date of plant emergence (or transplanting for rice)
entered in YYYY-MM-DD
format (character). Described in Table 1 of Savary
et al. 2012 and Table 1 of Savary et al. 2015.
expected number of days until the onset of disease after emergence date (day, integer). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.
simulation duration i.e., growing season length (day, integer). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.
relative humidity value threshold to decide whether leaves are wet or not (numeric). Described in Table 1 of Savary et al. 2012. Savary et al. 2012 used 90%.
rainfall amount (mm) threshold to decide whether leaves are wet or not (numeric). Described in Table 1 of Savary et al. 2012. Savary et al. 2012 used 5mm.
initial number of plant's healthy sites (integer). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.
initial number of infective sites (integer). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.
modifier for Rc (the basic infection rate corrected for removals) for crop age (numeric vector). Described in Table 1 of Savary et al. 2012 Table 1 of Savary et al. 2015.
modifier for Rc (the basic infection rate corrected for removals) for temperature (numeric vector). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.
potential basic infection rate corrected for removals (numeric). Derived from Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.
duration of latent period (day, integer). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.
duration of infectious period (day, integer). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.
maximum number of sites (integer). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.
aggregation coefficient, values are from 1 to >1 (numeric). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015. See further details in a - Aggregation section.
relative rate of physiological senescence (numeric). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.
relative rate of growth (numeric). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.
A data.table()
containing the following columns:
Zero indexed day of simulation that was run
Date of simulation
Total number of sites present on day "x"
Number of latent sites present on day "x"
Number of infectious sites present on day "x"
Number of removed sites present on day "x"
Number of senesced sites present on day "x"
Rate of infection
Rate of transfer from latent to infectious sites
Rate of growth of healthy sites
Rate of senescence of healthy sites
Number of diseased (latent + infectious + removed) sites on day "x"
Proportion of diseased (latent + infectious + removed) sites per total sites not including removed sites on day "x"
Area under the disease progress curve AUDPC for the simulation
Latitude value if provided by the wth
object
Longitude value if provided by the wth
object
When a is set to 1
the assumption is that that there is no disease
aggregation with new infections occurring at random among the healthy sites.
When a is greater than 1
there is aggregation in the disease occurrence,
the pathogen is unable to access the entire population of healthy sites,
which results in disease aggregation. Refer to Savary et al. (2012) for
greater detail.
If the wth
object provides LAT and LON columns, these will be included
in the output for mapping purposes. Both values must be present. These
columns are provided by default when using get_wth()
.
Sparks, A.H., P.D. Esker, M. Bates, W. Dall' Acqua, Z. Guo, V. Segovia, S.D. Silwal, S. Tolos, and K.A. Garrett, 2008. Ecology and Epidemiology in R: Disease Progress over Time. The Plant Health Instructor. DOI: doi:10.1094/PHI-A-2008-0129-02 .
Madden, L. V., G. Hughes, and F. van den Bosch. 2007. The Study of Plant Disease Epidemics. American Phytopathological Society, St. Paul, MN. DOI: doi:10.1094/9780890545058 .
Savary, S., Nelson, A., Willocquet, L., Pangga, I., and Aunario, J. Modeling and mapping potential epidemics of rice diseases globally. Crop Protection, Volume 34, 2012, Pages 6-17, ISSN 0261-2194 DOI: doi:10.1016/j.cropro.2011.11.009 .
SEIR()
is called by the following specific disease modelling functions:
if (FALSE) { # interactive()
# get weather for IRRI Zeigler Experiment Station in wet season 2000
wth <- get_wth(
lonlat = c(121.25562, 14.6774),
dates = c("2000-06-30", "2000-12-31")
)
# provide suitable values for brown spot intensity
RcA <-
cbind(c(0L, 20L, 40L, 60L, 80L, 100L, 120L),
c(0.35, 0.35, 0.35, 0.47, 0.59, 0.71, 1.0))
RcT <-
cbind(c(15L, 20L, 25L, 30L, 35L, 40L),
c(0, 0.06, 1.0, 0.85, 0.16, 0))
emergence <- "2000-07-15"
(SEIR(
wth = wth,
emergence = emergence,
onset = 20,
duration = 120,
rhlim = 90,
rainlim = 5,
RcA = RcA,
RcT = RcT,
RcOpt = 0.61,
p = 6,
i = 19,
H0 = 600,
I0 = 1,
a = 1,
Sx = 100000,
RRS = 0.01,
RRG = 0.1
))
}