fit_SEIQRDP: Fit SEIQRDP function

In genSEIR: Predict Epidemic Curves with Generalized SEIR Modeling

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)

genSEIR documentation built on July 12, 2021, 5:07 p.m.