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Merge pull request #1 from kkmann/refactor
refactor, 2nd preprint release
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,39 +1,14 @@ | ||
| function evaluate_performance_( | ||
| n_per_bubble, bubbles_per_class, m_classes, pr_meet_class, pr_meet_school, | ||
| gamma, frac_symptomatic, pr_external_infections, pr_noncovid_symptoms, | ||
| pcr, | ||
| eta, slope, intercept, ar_window, ar_coefficient, | ||
| policy_name, a, b | ||
| ) | ||
| lfd = LogRegTest("lfd", eta*slope, intercept, .998; ar_window = ar_window, ar_coefficient = ar_coefficient) | ||
| s = school( | ||
| n_per_bubble, bubbles_per_class, m_classes, pr_meet_class, pr_meet_school, | ||
| gamma, frac_symptomatic, pr_noncovid_symptoms, a, b; | ||
| policy = get_policy(policy_name, lfd) | ||
| ) | ||
| for i in 1:(6*7) | ||
| for indv in s.individuals | ||
| if rand(Bernoulli(pr_external_infections)) | ||
| infect!(indv) | ||
| function f(school, pr_external_infections, days; n = 1L) | ||
| function g(x) | ||
| for i in 1:(days) | ||
| for indv in x.individuals | ||
| if rand(Bernoulli(pr_external_infections)) | ||
| infect!(indv) | ||
| end | ||
| end | ||
| end | ||
| step!(s) | ||
| end | ||
| evaluate(s) | ||
| step!(x) | ||
| end | ||
| return evaluate(x) | ||
| end | ||
| return vcat(pmap(g, [resample(school) for i = 1:n])...) | ||
| end | ||
|
|
||
| function evaluate_performance( | ||
| n_per_bubble, bubbles_per_class, m_classes, pr_meet_class, pr_meet_school, | ||
| gamma, frac_symptomatic, pr_external_infections, pr_noncovid_symptoms, | ||
| pcr, | ||
| eta, slope, intercept, ar_window, ar_coefficient, | ||
| policy_name, a, b | ||
| ) | ||
| vcat(pmap(evaluate_performance_, | ||
| n_per_bubble, bubbles_per_class, m_classes, pr_meet_class, pr_meet_school, | ||
| gamma, frac_symptomatic, pr_external_infections, pr_noncovid_symptoms, | ||
| pcr, | ||
| eta, slope, intercept, ar_window, ar_coefficient, | ||
| policy_name, a, b | ||
| )...) | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,182 @@ | ||
| scenario <- function( | ||
| n_bubble = 9L, | ||
| bubbles_per_class = 3L, | ||
| classes_per_school = 12L, | ||
| expected_class_contacts = 3, | ||
| expected_school_contacts = 1, | ||
| frac_symptomatic = 0.5, | ||
| pr_noncovid_symptoms = 0.01, | ||
| expected_weekly_external_infections = 1, | ||
| a = Inf, | ||
| b = 1, | ||
| lli = 1e6, | ||
| pcr_lod = 300.0, | ||
| pcr_sens = .975, | ||
| pcr_spec = 1.0, | ||
| gamma = 0.0, | ||
| eta = 1.0, | ||
| lfd_spec = 0.998, | ||
| ar_window = 3L, | ||
| ar_coefficient = 0.0, | ||
| days = as.integer(6*7), | ||
| ... | ||
| ) { | ||
| res <- as.list(environment()) | ||
|
|
||
| res$pr_meet_class <- expected_class_contacts/(n_class(res) - 1) | ||
| res$expected_class_contacts <- NULL | ||
| res$pr_meet_school <- expected_school_contacts/(n_school(res) - 1) | ||
| res$expected_school_contacts <- NULL | ||
| res$pr_external_infection <- max(0, min(1, expected_weekly_external_infections / n_school(res) / 7)) | ||
| res$expected_weekly_external_infections <- NULL | ||
|
|
||
| tbl_innova_data <- tibble( | ||
| `viral load` = 10^(2:7), | ||
| sensitivity = 1 - (c(621, 521, 344, 207, 144, 123) - 114) / (684 - 114) | ||
| ) | ||
| sensitivity <- function(vl, slope, intercept) { | ||
| lfd <- julia_call("LogRegTest", "lfd", slope, intercept, lfd_spec, need_return = "Julia") | ||
| julia_call("sensitivity.", lfd, vl, need_return = "R") | ||
| } | ||
| innova <- optim( | ||
| c(.76, -5), | ||
| function(x) { | ||
| sum(( | ||
| tbl_innova_data$sensitivity - | ||
| sensitivity(tbl_innova_data$`viral load`, slope = x[1], intercept = x[2]) | ||
| )^2) | ||
| }, | ||
| method = "L-BFGS-B", | ||
| lower = c(.1, -10), | ||
| upper = c(2.5, 5), | ||
| control = list(maxit = 1e4) | ||
| ) | ||
| res$lfd_slope <- innova$par[1] | ||
| res$lfd_intercept <- innova$par[2] | ||
|
|
||
| res | ||
| } | ||
|
|
||
| sensitivity <- function(vl, params) { | ||
| lfd <- julia_call("LogRegTest", "lfd", params$lfd_slope, params$lfd_intercept, params$lfd_spec, need_return = "Julia") | ||
| julia_call("sensitivity.", lfd, vl, need_return = "R") | ||
| } | ||
|
|
||
| n_class <- function(params) with(params, n_bubble * bubbles_per_class) | ||
| n_school <- function(params) with(params, classes_per_school * n_class(params)) | ||
| n_weekly_infections <- function(params) with(params, 7*n_school(params)*pr_external_infection) | ||
|
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||
| schooldays <- function(params) with(params, sum(((1:days) %% 7) %in% 1:5)) | ||
|
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||
| pcr <- function(params) with(params, julia_call("FixedTest", "pcr", pcr_sens, pcr_spec, lod = pcr_lod, need_return = "Julia")) | ||
|
|
||
| lfd <- function(params) { | ||
| with(params, | ||
| julia_call("LogRegTest", "lfd", | ||
| eta*params$lfd_slope, params$lfd_intercept, lfd_spec, | ||
| ar_window = ar_window, ar_coefficient = ar_coefficient | ||
| ) | ||
| ) | ||
| } | ||
|
|
||
| fit_rzero_ <- function(params, gamma_min = 0, gamma_max = .1) { | ||
| tbl_rzero <- tibble( | ||
| gamma = seq(gamma_min, gamma_max, length.out = 100), | ||
| R = map(gamma, ~{ | ||
| params$gamma <- . | ||
| sample_rzero(do.call(school, params), n = 10L) | ||
| }) | ||
| ) %>% | ||
| unnest(R) | ||
| fit <- lm(formula = R ~ gamma - 1, data = tbl_rzero) | ||
| list(fit = fit, data = tbl_rzero) | ||
| } | ||
| fit_rzero <- memoise::memoise(fit_rzero_) | ||
| rzero <- function(gamma, params, gamma_min = 0, gamma_max = .1) { | ||
| fit <- fit_rzero(params, gamma_min, gamma_max)$fit | ||
| as.numeric(predict(fit, newdata = tibble(gamma = gamma), type = "response")) | ||
| } | ||
| gamma <- function(R, params, gamma_min = 0, gamma_max = .1) { | ||
| as.numeric(uniroot(function(x) rzero(x, params, gamma_min, gamma_max) - R, interval = c(gamma_min, gamma_max))$root) | ||
| } | ||
|
|
||
| sample_presymptomatic_vl <- function(params, n = 1e5) { | ||
| dm <- julia_call("LarremoreModel", params$gamma, frac_symptomatic = params$frac_symptomatic, need_return = "Julia") | ||
| individuals <- julia_call("Individual.", dm, rep(params$pr_noncovid_symptoms, n), need_return = "Julia") | ||
| julia_call("infect!.", individuals, need_return = "Julia") | ||
| julia_call("steps!.", individuals, 21L, need_return = "Julia") | ||
| julia_call("get_status_logs", individuals) %>% | ||
| as_tibble() %>% | ||
| arrange(uuid, day) %>% | ||
| group_by(uuid) %>% | ||
| filter( | ||
| row_number() >= which(viral_load > params$pcr_lod)[1], | ||
| row_number() < which(symptomatic)[1] | ||
| ) %>% | ||
| sample_n(1) %>% | ||
| ungroup() %>% | ||
| select(uuid, day, viral_load) | ||
| } | ||
| sample_presymptomatic_vl_ <- memoise::memoise(sample_presymptomatic_vl) | ||
| mean_sensitivity <- function(params, eta, ...) { | ||
| sample_presymptomatic_vl_(params, ...) %>% | ||
| mutate( | ||
| sensitivity = sensitivity(viral_load^eta, params) | ||
| ) %>% | ||
| pull(sensitivity) %>% | ||
| summary() %>% | ||
| mean() | ||
| } | ||
| eta <- function(params, target, ...) { | ||
| as.numeric(uniroot(function(x) mean_sensitivity(params, x, ...) - target, interval = c(0, 5))$root) | ||
| } | ||
|
|
||
| expand_scenario <- function(params = scenario(), ...) { | ||
| tbl <- if (length(list(...)) == 0) { | ||
| as_tibble(params) | ||
| } else { | ||
| for (name in names(list(...))) { | ||
| params[[name]] <- NULL | ||
| } | ||
| expand_grid(do.call(expand_grid, list(...)), as_tibble(params)) | ||
| } | ||
| tbl %>% | ||
| group_by(row_number()) %>% | ||
| nest() %>% | ||
| mutate(data = map(data, as.list)) %>% | ||
| pull(data) %>% | ||
| enframe(value = "params") %>% | ||
| select(params) %>% | ||
| mutate( | ||
| tmp = map(params, ~as_tibble(.)) | ||
| ) %>% | ||
| unnest(tmp) | ||
| } | ||
|
|
||
| julia_eval('@everywhere include("code/evaluate_performance.jl")') | ||
| evaluate_performance_ <- function(params, policy, n = 1L) { | ||
| school <- do.call(school, c(params, list(policy = policy(params)))) | ||
| julia_call("f", school, params$pr_external_infection, params$days, n = n) %>% | ||
| as_tibble() %>% | ||
| mutate( | ||
| `% infected (cumulative)` = n_infected/n_school(params), | ||
| `% schooldays missed (cumulative)` = workdays_missed/n_school(params)/schooldays(params) | ||
| ) | ||
| } | ||
| evaluate_performance <- function(policies, params = scenario(), n = if (!is.null(n_resample)) {n_resample} else {25L}, ...) { | ||
| gamma2rs <- memoise::memoise(function(gamma) round(rzero(gamma, params), 1) ) | ||
| eta2mean_sensitivity <- memoise::memoise(function(eta) round(mean_sensitivity(params, eta), 2) ) | ||
| expand_scenario(params, ...) %>% | ||
| expand_grid(tibble(policy = policies)) %>% | ||
| mutate( | ||
| policy_name = names(policy), | ||
| results = map2(params, policy, evaluate_performance_, n) | ||
| ) %>% | ||
| unnest(results) %>% | ||
| select(-params) %>% | ||
| mutate( | ||
| policy_name = factor(policy_name, levels = names(lst_policies)), | ||
| Rs = map_dbl(.data$gamma, gamma2rs), | ||
| `mean sensitivity` = map_dbl(.data$eta, eta2mean_sensitivity) | ||
| ) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,16 @@ | ||
| julia_eval('@everywhere include("code/school.jl")') | ||
|
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||
| school <- function(policy = julia_call("DoNothing", need_return = "Julia"), ...){ | ||
| params <- scenario(...) | ||
| with(params, | ||
| julia_call("school", | ||
| n_bubble, bubbles_per_class, classes_per_school, pr_meet_class, pr_meet_school, | ||
| gamma, | ||
| frac_symptomatic, pr_noncovid_symptoms, | ||
| a, b, | ||
| lli, | ||
| policy | ||
| )) | ||
| } | ||
|
|
||
| sample_rzero <- function(school, days = 21L, n = 1L) julia_call("sample_rzero", school, days, n) |
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