SEIQRDP: Simulate generalized SEIR model

In genSEIR: Predict Epidemic Curves with Generalized SEIR Modeling

Description

This function simulates the time-histories of an epidemic outbreak using a generalized SEIR model

Usage

SEIQRDP(
  alpha,
  beta,
  gamma,
  delta,
  lambda0,
  kappa0,
  Npop,
  E0,
  I0,
  Q0,
  R0,
  D0,
  lambdaFun,
  kappaFun,
  tstart,
  tfinish,
  dt = 1/24,
  f = 0
)

Arguments

alpha	fitted protection rate
beta	fitted infection rate
gamma	fitted Inverse of the average latent time
delta	fitted rate at which people enter in quarantine
lambda0	fitted cure rate
kappa0	fitted mortality rate
Npop	Total population of the sample
E0	Initial number of exposed cases
I0	Initial number of infectious cases
Q0	Initial number of quarantined cases
R0	Initial number of recovered cases
D0	Initial number of dead cases
lambdaFun	anonymous function giving the time-dependant recovery rate
kappaFun	anonymous function giving the time-dependant death rate
tstart	start date
tfinish	finish date
dt	the time step. This oversamples time to ensure that the algorithm converges
f	number of days for future predictions

Value

a list of predicted cases including susceptible, exposed, infectious, quarantined, recovered, dead and insusceptible.

Author(s)

Selcuk Korkmaz, [e-mail]

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

fit_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, 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)

 res = SEIQRDP(alpha = params$alpha1, beta = params$beta1,
               gamma = params$gamma1, delta = params$delta1,
               lambda0 = c(params$lambda01, params$lambda02, params$lambda03),
               kappa0 = c(params$kappa01, params$kappa02, params$kappa03),
               Npop, E0, I0, Q0, R0, D0,lambdaFun = params$lambdaFun,
               kappaFun = params$kappaFun, tstart = start, tfinish = finish,
               dt = dt, f =f)

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