SEIR: Susceptible-Exposed-Infectious-Removed (SEIR) Model Framework

View source: R/SEIR.R

SEIRR Documentation

Susceptible-Exposed-Infectious-Removed (SEIR) Model Framework

Description

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.

Usage

SEIR(
  wth,
  emergence,
  onset,
  duration,
  rhlim,
  rainlim,
  H0,
  I0,
  RcA,
  RcT,
  RcOpt,
  p,
  i,
  Sx,
  a,
  RRS,
  RRG
)

Arguments

wth

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
emergence

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.

onset

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.

duration

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.

rhlim

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%.

rainlim

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.

H0

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.

I0

initial number of infective sites (integer). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.

RcA

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.

RcT

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.

RcOpt

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.

p

duration of latent period (day, integer). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.

i

duration of infectious period (day, integer). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.

Sx

maximum number of sites (integer). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.

a

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.

RRS

relative rate of physiological senescence (numeric). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.

RRG

relative rate of growth (numeric). Described in Table 1 of Savary et al. 2012 and Table 1 of Savary et al. 2015.

Value

A data.table() containing the following columns:

simday

Zero indexed day of simulation that was run

dates

Date of simulation

sites

Total number of sites present on day "x"

latent

Number of latent sites present on day "x"

infectious

Number of infectious sites present on day "x"

removed

Number of removed sites present on day "x"

senesced

Number of senesced sites present on day "x"

ratinf

Rate of infection

rtransfer

Rate of transfer from latent to infectious sites

rgrowth

Rate of growth of healthy sites

rsenesced

Rate of senescence of healthy sites

diseased

Number of diseased (latent + infectious + removed) sites on day "x"

intensity

Proportion of diseased (latent + infectious + removed) sites per total sites not including removed sites on day "x"

AUDPC

Area under the disease progress curve AUDPC for the simulation

lat

Latitude value if provided by the wth object

lon

Longitude value if provided by the wth object

a - Aggregation

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.

LAT/LON

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().

Author(s)

Adam H. Sparks, adamhsparks@gmail.com

References

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: \Sexpr[results=rd]{tools:::Rd_expr_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: \Sexpr[results=rd]{tools:::Rd_expr_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: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.cropro.2011.11.009")}.

See Also

SEIR() is called by the following specific disease modelling functions:

  • predict_bacterial_blight(),

  • predict_brown_spot(),

  • predict_leaf_blast(),

  • predict_sheath_blight(),

  • predict_tungro()

Examples


# 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
))


adamhsparks/epirice documentation built on Nov. 22, 2023, 5:16 p.m.