Integrates the deterministic SEIR system
dS/dt = -beta S I / N, dE/dt = beta S I / N - sigma E,
dI/dt = sigma E - gamma I, dR/dt = gamma I.
Value
List with estimate (final size), S, E,
I, R, N, R0, latent_period,
infectious_period, peak_I, peak_time,
final_size, conservation_error, beta,
sigma, gamma, t_max, dt, method.
Details
The exposed class E holds individuals who are infected but not yet
infectious; they progress at rate sigma, so the mean latent period is
1 / sigma. The basic reproduction number is \(R0 = beta /
gamma\): the latent stage delays but does not alter the number of
secondary cases, because every exposed individual eventually becomes
infectious. Population N = S + E + I + R is conserved.
Integrated with classical fourth-order Runge-Kutta at a fixed step, so the result is deterministic and identical in every language arm.
References
Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review 42(4), 599-653. doi:10.1137/S0036144500371907
Examples
Seirep(S = 990, E = 10, I = 0, R = 0, beta = 0.3, sigma = 0.2, gamma = 0.1)
#> $estimate
#> [1] 940.735
#>
#> $S
#> [1] 58.87991
#>
#> $E
#> [1] 0.04684648
#>
#> $I
#> [1] 0.3382567
#>
#> $R
#> [1] 940.735
#>
#> $N
#> [1] 1000
#>
#> $R0
#> [1] 3
#>
#> $latent_period
#> [1] 5
#>
#> $infectious_period
#> [1] 10
#>
#> $peak_I
#> [1] 198.6041
#>
#> $peak_time
#> [1] 55.1
#>
#> $final_size
#> [1] 940.735
#>
#> $conservation_error
#> [1] 1.477929e-12
#>
#> $beta
#> [1] 0.3
#>
#> $sigma
#> [1] 0.2
#>
#> $gamma
#> [1] 0.1
#>
#> $t_max
#> [1] 160
#>
#> $dt
#> [1] 0.1
#>
#> $method
#> [1] "SEIR compartmental model (Hethcote 2000)"
#>
#> attr(,"class")
#> [1] "morie_rich_result" "list"
