The following is an R code for an SEIR model. Please translate this code to PYTHON language so that it produces the same (or very similar results) in PYTHON thank you! R code: m <- 4 #number of age classes mu <- c(0,0,0,1/(55*365)) #death rate in each age group; it is assumed that only adults die nu <- c(1/(55*365),0,0,0) #is the birth rate into the childhood class; it is assumed only adults give birth. n <- c(6,4,10,55)/75 # fraction in each age class (assumption that life expectancy is 75 years) S0 <- c(0.05,0.01,0.01,0.008) # inital value for number of susceptible E0 <- c(0.0001,0.0001,0.0001,0.0001) # inital value for number of exposed I0 <- c(0.0001,0.0001,0.0001,0.0001) # inital value for number of infectious R0 <- c(0.0298, 0.04313333, 0.12313333, 0.72513333) # inital value for number of recovered ND <- 365 # time to simulate beta <- matrix(c(2.089, 2.089, 2.086, 2.037, 2.089, 9.336, 2.086, 2.037, 2.086, 2.086, 2.086, 2.037, 2.037, 2.037, 2.037, 2.037), nrow=4, ncol=4) # matrix of transmission rates gamma <- 1/5 # recovery rate sigma <- 1/8 # rate at which individuals move from the exposed to the infectious classes TS <- 1 # time step to simualte is days # combining parameter and initial values parms <- list(nu=nu, beta=beta, mu=mu, sigma=sigma, gamma=gamma) INPUT <- c(S0, E0, I0, R0) # constructing time vector t_start <- 0 # starting time t_end <- ND - 1 # ending time t_inc <- TS #time increment t_range <- seq(from= t_start, to=t_end+t_inc, by=t_inc) # vector with time steps # differential equations diff_eqs <- function(times, Y, parms){ dY <- numeric(length(Y)) with(parms,{ # creates an empty matrix for(i in 1:m){ dY[i] <- nu[i]*n[4] - beta[,i]%*%Y[2*m + seq(1:m)] * Y[i] - mu[i] * Y[i] # S_i dY[m+i] <- beta[,i] %*% Y[2*m + seq(1:m)] *Y[i] - mu[i] * Y[m+i] - sigma * Y[m+i] #E_i dY[2*m+i] <- sigma * Y[m+i] - gamma * Y[2*m + i] - mu[i] * Y[2*m+i] #I_i dY[3*m+i] <- gamma * Y[2*m+i] - mu[i] * Y[3*m + i] #R_i } list(dY) }) } RES2=rep(0,17) #initalizing the result vector number_years <- 100 #set the number of years to simulate # initialize the loop k=1 # yearly ageing for(k in 1:number_years) { RES = lsoda(INPUT, t_range, diff_eqs, parms) #taking the last entry as the the new input that then is propagated accoring to the aging INPUT=RES[366,-1] INPUT[16]=INPUT[16]+INPUT[15]/10 INPUT[15]=INPUT[15]+INPUT[14]/4-INPUT[15]/10 INPUT[14]=INPUT[14]+INPUT[13]/6-INPUT[14]/4 INPUT[13]=INPUT[13]-INPUT[13]/6 INPUT[12]=INPUT[12]+INPUT[11]/10 INPUT[11]=INPUT[11]+INPUT[10]/4-INPUT[11]/10 INPUT[10]=INPUT[10]+INPUT[9]/6-INPUT[10]/4 INPUT[9]=INPUT[9]-INPUT[9]/6 INPUT[8]=INPUT[8]+INPUT[7]/10 INPUT[7]=INPUT[7]+INPUT[6]/4-INPUT[7]/10 INPUT[6]=INPUT[6]+INPUT[5]/6-INPUT[6]/4 INPUT[5]=INPUT[5]-INPUT[5]/6 INPUT[4]=INPUT[4]+INPUT[3]/10 INPUT[3]=INPUT[3]+INPUT[2]/4-INPUT[3]/10 INPUT[2]=INPUT[2]+INPUT[1]/6-INPUT[2]/4 INPUT[1]=INPUT[1]-INPUT[1]/6 RES2 <- rbind(RES2,RES) k=k+1 } 

#rescaling time to years time <- seq(from=0, to=100*(ND+1))/(ND+1) #changing time to the rescaled time RES2[ ,"time"] <- time #labeling of the output from ODE solver label <- c("S1", "S2", "S3", "S4","E1", "E2", "E3", "E4", "I1", "I2", "I3", "I4", "R1", "R2", "R3", "R4") label1 <- substr(label, 1, 1) Age <- substr(label, 2, 2) df <- data.frame(time = RES2[, 1], label1 = rep(label1, each = nrow(RES2)), Age = rep(Age, each = nrow(RES2)), value = c(RES2[, -1])) #plotting the data df$label1 <- factor(df$label1, levels = c("S","E","I","R")) df$Age <- factor(df$Age) df %>% mutate(label1 = recode(label1, S = "Susceptible")) %>% mutate(label1 = recode(label1, E = "Exposed")) %>% mutate(label1 = recode(label1, I = "Infectious")) %>% mutate(label1 = recode(label1, R = "Recovered")) %>% mutate(Age = recode(Age, "1" = "0-6 years ")) %>% mutate(Age = recode(Age, "2" = "6-10 years ")) %>% mutate(Age = recode(Age, "3" = "10-20 years ")) %>% mutate(Age = recode(Age, "4" = "20+ years ")) %>% ggplot() + geom_line(aes(x = time, y = value, color = Age)) + facet_wrap( ~label1, ncol=1, scales = "free_y")+ xlab("Time (years)") + ylab(" Individuals")