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Lam Ho
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Lam Ho
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,47 +1,68 @@ | ||
| library(testthat) | ||
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| test_that("Transition probabilities of SIR model", { | ||
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| data(Eyam) | ||
| data <- Eyam | ||
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| alpha = 3.204 | ||
| beta = 0.019 | ||
| k = 1 | ||
| N <- data$S[1] + data$I[1] + data$R[1] | ||
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| power = list(powS = 0.9, powI_inf = 0.8, powI_rem = 0.9) | ||
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| p1 <- bbd_prob( | ||
| t = data$time[k + 1] - data$time[k], # Time increment | ||
| a0 = data$R[k], b0 = data$I[k], # From: R(t_k), I(t_k) | ||
| brates1 <- function(a,b){ 0 }, | ||
| brates2 <- function(a,b){ beta * max(N - a - b, 0) * b }, | ||
| drates2 <- function(a,b){ 0 }, | ||
| a0 = data$R[k], b0 = data$I[k], # From: R(t_k), I(t_k) | ||
| brates1 <- function(a,b){ 0 }, | ||
| brates2 <- function(a,b){ beta * max(N - a - b, 0) * b }, | ||
| drates2 <- function(a,b){ 0 }, | ||
| trans21 <- function(a,b){ alpha * b }, | ||
| A = data$R[k + 1], B = data$R[k + 1] + data$I[k] - data$R[k], | ||
| computeMode = 4, nblocks = 80 # Compute using 4 threads | ||
| )[data$R[k + 1] - data$R[k] + 1, data$I[k + 1] + 1] | ||
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| p2 <- dbd_prob( # Compute the transition probability matrix | ||
| t = data$time[k + 1] - data$time[k], # Time increment | ||
| a0 = data$S[k], b0 = data$I[k], # From: S(t_k), I(t_k) | ||
| drates1 <- function(a, b) { 0 }, | ||
| brates2 <- function(a, b) { 0 }, | ||
| drates2 <- function(a, b) { alpha * b }, | ||
| a0 = data$S[k], b0 = data$I[k], # From: S(t_k), I(t_k) | ||
| drates1 <- function(a, b) { 0 }, | ||
| brates2 <- function(a, b) { 0 }, | ||
| drates2 <- function(a, b) { alpha * b }, | ||
| trans12 <- function(a, b) { beta * a * b }, | ||
| a = data$S[k + 1], B = data$S[k] + data$I[k] - data$S[k + 1], | ||
| computeMode = 4, nblocks = 80 # Compute using 4 threads | ||
| )[1, data$I[k + 1] + 1] | ||
| p3 <- SIR_prob( | ||
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| p3 <- SIR_prob( | ||
| t = data$time[k + 1] - data$time[k], # Time increment | ||
| alpha = alpha, beta = beta, | ||
| S0 = data$S[k], I0 = data$I[k], # From: R(t_k), I(t_k) | ||
| S0 = data$S[k], I0 = data$I[k], # From: R(t_k), I(t_k) | ||
| nSI = data$S[k] - data$S[k + 1], nIR = data$R[k + 1] - data$R[k], | ||
| direction = "Forward", | ||
| computeMode = 4, nblocks = 80 # Compute using 4 threads | ||
| )[data$S[k] - data$S[k + 1] + 1, data$R[k + 1] - data$R[k] + 1] | ||
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| p4 <- dbd_prob( # Compute the transition probability matrix | ||
| t = data$time[k + 1] - data$time[k], # Time increment | ||
| a0 = data$S[k], b0 = data$I[k], # From: S(t_k), I(t_k) | ||
| drates1 <- function(a, b) { 0 }, | ||
| brates2 <- function(a, b) { 0 }, | ||
| drates2 <- function(a, b) { alpha * b^0.9 }, | ||
| trans12 <- function(a, b) { beta * a^0.9 * b^0.8 }, | ||
| a = data$S[k + 1], B = data$S[k] + data$I[k] - data$S[k + 1], | ||
| computeMode = 4, nblocks = 80 # Compute using 4 threads | ||
| )[1, data$I[k + 1] + 1] | ||
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| p5 <- SIR_prob( | ||
| t = data$time[k + 1] - data$time[k], # Time increment | ||
| alpha = alpha, beta = beta, | ||
| S0 = data$S[k], I0 = data$I[k], # From: R(t_k), I(t_k) | ||
| nSI = data$S[k] - data$S[k + 1], nIR = data$R[k + 1] - data$R[k], | ||
| direction = "Forward", power = power, | ||
| computeMode = 4, nblocks = 80 # Compute using 4 threads | ||
| )[data$S[k] - data$S[k + 1] + 1, data$R[k + 1] - data$R[k] + 1] | ||
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| expect_equal(0.0, abs(p1-p2), 1E-5) | ||
| expect_equal(0.0, abs(p2-p3), 1E-8) | ||
| }) | ||
| expect_equal(0.0, abs(p4-p5), 1E-8) | ||
| }) |