pomp package COVID SEIR model least squares errors traceback - r

Question

I try to model a SEIR for UK to evaluate the implemented containment measures and found some code with the pomp package here: https://kingaa.github.io/clim-dis/parest/parest.html
I tried to transfer this to my case which adds one stage (E) and three more variables. In the end i want to do a least squared estimation to find the optimal beta.
Data_UK_beta0 consists of the variable date (int from 0 to 165) and new_cases (from John Hopkins University dataset).
Data_UK_pomp_beta0 <- pomp(
data= Data_UK_beta0,
times ="date", t0=0,
skeleton = vectorfield(
Csnippet("
DS=-beta1*S*I/N;
DE= beta1*S*I/N-delta1*E;
DI=delta1*E-(gamma1+eta1)*I;
DR=gamma1*I;")),
rinit = Csnippet("
S=S_0;
E=E_0;
I=I_0;
R=N-S_0-E_0-I_0;"),
statenames = c("S","E","I","R"),
paramnames = c("beta1","delta1","gamma1","eta1","N","S_0","E_0","I_0"))
sse_UK_beta0 <- function (params) {
x <- trajectory(Data_UK_pomp_beta0,params=params)
discrep <- x["I",,]-obs(Data_UK_pomp_beta0)
sum(discrep^2)
}
install.packages("apricom")
library(apricom)
beta_reg <- function (beta0) {
params <- c(beta1=beta0, delta1=1/5.1, gamma1=1, eta1=0.012649, N=67886004, S_0=67886004, E_0=5, I_0=2)
sse(params)
}
beta0 <- seq(from=1,to=40,by=1)
SSE <- sapply(beta0, beta_reg)
got the following error traceback (unfortunately in german, but i guess the message should be clear :)
Fehler bei der Auswertung des Argumentes 'x' bei der Methodenauswahl für Funktion 'as.matrix': Argument "dataset" fehlt (ohne Standardwert)
7.
h(simpleError(msg, call))
6.
.handleSimpleError(function (cond)
.Internal(C_tryCatchHelper(addr, 1L, cond)), "Argument \"dataset\" fehlt (ohne Standardwert)",
base::quote(as.matrix(dataset)))
5.
as.matrix(dataset)
4.
sse(params)
3.
FUN(X[[i]], ...)
2.
lapply(X = X, FUN = FUN, ...)
1.
sapply(beta0, beta_reg)
What did i do wrong?

Answer 1

The sse function you've imported from apricom has nothing (as far as I can see) to do with this problem. (This also doesn't have anything to do with C(++) code compilation, so the [compiler-errors] tag in your question is a little misleading.)
You haven't given us a way to get your Data_UK_beta0 data set so I can't reproduce this, but I assume that you actually want something like:
beta_reg <- function (beta0) {
params <- c(beta1=beta0, delta1=1/5.1, gamma1=1,
eta1=0.012649, N=67886004, S_0=67886004, E_0=5, I_0=2)
sse_UK_beta0(params)
}
beta0 <- seq(from=1,to=40,by=1)
SSE <- sapply(beta0, beta_reg)
You should also be aware that are a bunch of other potentially tricky issues in what you're doing. One that jumps out is that you are likely comparing the prevalence of infection in the modeling (the current number in the I compartment) to case report data, which is a (lagged, biased, imperfect) measure of the incidence of disease, i.e. the number of new cases per unit time ...