fit_SEIQRDP {genSEIR}

Fit SEIQRDP function

Description

Fit SEIQRDP function parameters used in the SEIQRDP function, used to model the time-evolution of an epidemic outbreak.

Usage

fit_SEIQRDP(
  Q,
  R,
  D,
  Npop,
  E0,
  I0,
  time,
  alpha = 0.05,
  dt = 1/24,
  guess,
  ftol = sqrt(.Machine$double.eps),
  ptol = sqrt(.Machine$double.eps),
  gtol = 0,
  diag = list(),
  epsfcn = 0,
  factor = 100,
  maxfev = integer(),
  maxiter = 1000,
  nprint = 1,
  trace = TRUE,
  ...
)

Arguments

Q

time histories of the active cases

R

time histories of the recovered cases

D

time histories of the deceased cases

Npop

total population of the country

E0

initial number of exposed cases

I0

initial number of predicted infectious cases

time

a time vector

alpha

type I error rate, default is 0.05

dt

the time step. This oversamples time to ensure that the algorithm converges

guess

initial guess parameters

ftol

nls.lm.control object. non-negative numeric. Default is 1e-6

ptol

nls.lm.control object. non-negative numeric. Default is 1e-6

gtol

nls.lm.control object. non-negative numeric. Default is 1e-6

diag

nls.lm.control object. a list or numeric vector containing positive entries that serve as multiplicative scale factors for the parameters.

epsfcn

nls.lm.control object. Default is 0.001

factor

nls.lm.control object. Default is 100

maxfev

nls.lm.control object. Default is 1000

maxiter

nls.lm.control object. Default is 100

nprint

nls.lm.control object. Default is 1

trace

set TRUE to trace iteration results

...

further arguments

Value

a list of optimized parameters

Author(s)

Selcuk Korkmaz, [e-mail]

References

Peng, L., Yang, W., Zhang, D., Zhuge, C., Hong, L. 2020. “Epidemic analysis of COVID-19 in China by dynamical modeling”, arXiv preprint arXiv:2002.06563.

https://www.mathworks.com/matlabcentral/fileexchange/74545-generalized-seir-epidemic-model-fitting-and-computation

See Also

SEIQRDP predict_SEIQRDP

Examples

start = "01/01/21"
finish = "04/01/21"
country = "Italy"
dt = 1
f=30

covidData = getDataCOVID(start = start, finish = finish, country = country)
Recovered = covidData$tableRecovered
Deaths = covidData$tableDeaths
Confirmed = covidData$tableConfirmed

if(nrow(Recovered) == 1){
  name = Recovered$CountryRegion
}else{
   name = paste0(Recovered$ProvinceState, " (",Recovered$CountryRegion,")")
}

  recovered = Recovered[ ,5:ncol(covidData$tableRecovered)]
  deaths = Deaths[ ,5:ncol(covidData$tableDeaths)]
  confirmed = Confirmed[ ,5:ncol(covidData$tableConfirmed)]

  Npop = 60000000

  alpha_guess = 0.05
  beta_guess = 0.8
  LT_guess = 7
  Q_guess = 0.8
  lambda_guess = c(0.01,0.001,10)
  kappa_guess = c(0.001,0.001,10)

guess = list(alpha_guess,
            beta_guess,
            1/LT_guess,
            Q_guess,
            lambda_guess[1],
            lambda_guess[2],
            lambda_guess[3],
            kappa_guess[1],
            kappa_guess[2],
            kappa_guess[3])

 Q0 = confirmed[1]-recovered[1]-deaths[1]
 I0 = 0.3*Q0
 E0 = 0.3*Q0
 R0 = recovered[1]
 D0 = deaths[1]

 Active = confirmed-recovered-deaths
 Active[Active<0] <- 0

 Q=Active
 R=recovered
 D = deaths

 time = seq(as.Date(start, format = "%m/%d/%y"), as.Date(finish, format = "%m/%d/%y"), by = "1 day")

 params = fit_SEIQRDP(Q = Active, R = recovered, D = deaths, Npop = Npop, E0 = E0, I0 = I0,
                        time = time, alpha = 0.05, dt = dt, guess = guess, ftol = 1e-6,
                        ptol = 1e-6, gtol = 1e-6, epsfcn = 0.001, factor = 100, maxfev = 1000,
                        maxiter = 100, nprint = 1, trace = TRUE)