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UK.R
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# - - - - - - - - - - - - - - - - - - - - - - -
# UK model: load data and analyse scenarios
# - - - - - - - - - - - - - - - - - - - - - - -
library(rlang)
library(stringr)
# Set path
# Set this path to the base directory of the repository.
##covid_uk_path = "~/Dropbox/COVID-UK"
covid_uk_path = "./"
# covidm options
cm_path = paste0(covid_uk_path, "/covidm/");
if (grepl(Sys.info()["user"], pattern = "^adamkuchars(ki)?$")) { cm_path = "~/Documents/GitHub/covid-uk/covidm/" }
source(paste0(cm_path, "/R/covidm.R"))
# build parameters for entire UK, for setting R0.
parametersUK1 = cm_parameters_SEI3R(cm_uk_locations("UK", 0),
dE = cm_delay_gamma(4.0, 4.0, t_max = 60, t_step = 0.25)$p,
dIp = cm_delay_gamma(1.5, 4.0, t_max = 60, t_step = 0.25)$p,
dIs = cm_delay_gamma(3.5, 4.0, t_max = 60, t_step = 0.25)$p,
dIa = cm_delay_gamma(5.0, 4.0, t_max = 60, t_step = 0.25)$p,
deterministic = F);
# build parameters for regions of UK, down to the county level (level 3).
locations = cm_uk_locations("UK", 3);
parameters = cm_parameters_SEI3R(locations, date_start = "2020-01-29", date_end = "2021-12-31",
dE = cm_delay_gamma(4.0, 4.0, t_max = 60, t_step = 0.25)$p, # 6.5 day serial interval.
dIp = cm_delay_gamma(1.5, 4.0, t_max = 60, t_step = 0.25)$p, # 1.5 days w/o symptoms
dIs = cm_delay_gamma(3.5, 4.0, t_max = 60, t_step = 0.25)$p, # 5 days total of infectiousness
dIa = cm_delay_gamma(5.0, 4.0, t_max = 60, t_step = 0.25)$p, # 5 days total of infectiousness here as well.
deterministic = F);
# Split off the elderly (70+, age groups 15 and 16) so their contact matrices can be manipulated separately
parameters = cm_split_matrices_ex_in(parameters, 15);
# Create additional matrix for child-elderly contacts
for (j in seq_along(parameters$pop))
{
# Recover home/other contact matrix
mat_ref = parameters$pop[[j]]$matrices[[1]] + parameters$pop[[j]]$matrices[[4]] +
parameters$pop[[j]]$matrices[[5]] + parameters$pop[[j]]$matrices[[8]];
gran = 5/7; # adjustment for weekdays only.
N = nrow(mat_ref);
popsize = parameters$pop[[j]]$size;
mat = matrix(0, ncol = N, nrow = N);
# Add child-grandparent contacts: under 15s to 55+s
for (a in 1:3) {
dist = c(rep(0, 10 + a), mat_ref[a, (11 + a):N]);
dist = dist/sum(dist);
mat[a, ] = mat[a, ] + gran * dist;
mat[, a] = mat[, a] + (gran * dist) * (popsize[a] / popsize);
}
# Add child-grandparent contact matrix to population
parameters$pop[[j]]$matrices$gran = mat;
parameters$pop[[j]]$contact = c(parameters$pop[[j]]$contact, 0);
}
# Health burden processes
probs = fread(
"Age,Prop_symptomatic,IFR,Prop_inf_hosp,Prop_inf_critical,Prop_critical_fatal,Prop_noncritical_fatal,Prop_symp_hospitalised,Prop_hospitalised_critical
10,0.66,8.59E-05,0.002361009,6.44E-05,0.5,0,0,0.3
20,0.66,0.000122561,0.003370421,9.19E-05,0.5,9.47E-04,0.007615301,0.3
30,0.66,0.000382331,0.010514103,0.000286748,0.5,0.001005803,0.008086654,0.3
40,0.66,0.000851765,0.023423527,0.000638823,0.5,0.001231579,0.009901895,0.3
50,0.66,0.001489873,0.0394717,0.001117404,0.5,0.002305449,0.018535807,0.3
60,0.66,0.006933589,0.098113786,0.005200192,0.5,0.006754596,0.054306954,0.3
70,0.66,0.022120421,0.224965092,0.016590316,0.5,0.018720727,0.150514645,0.3
80,0.66,0.059223786,0.362002579,0.04441784,0.5,0.041408882,0.332927412,0.3
100,0.66,0.087585558,0.437927788,0.065689168,0.5,0.076818182,0.617618182,0.3")
reformat = function(P)
{
# 70-74,3388.488 75-79,2442.147 80-84,1736.567 85-89,1077.555 90-94,490.577 95-99,130.083 100+,15.834
x = c(P[1:7], weighted.mean(c(P[8], P[9]), c(3388.488 + 2442.147, 1736.567 + 1077.555 + 490.577 + 130.083 + 15.834)));
return (rep(x, each = 2))
}
P.icu_symp = reformat(probs[, Prop_symp_hospitalised * Prop_hospitalised_critical]);
P.nonicu_symp = reformat(probs[, Prop_symp_hospitalised * (1 - Prop_hospitalised_critical)]);
P.death_icu = reformat(probs[, Prop_critical_fatal]);
P.death_nonicu = reformat(probs[, Prop_noncritical_fatal]);
burden_processes = list(
list(source = "Ip", type = "multinomial", names = c("to_icu", "to_nonicu", "null"), report = c("", "", ""),
prob = matrix(c(P.icu_symp, P.nonicu_symp, 1 - P.icu_symp - P.nonicu_symp), nrow = 3, ncol = 16, byrow = T),
delays = matrix(c(cm_delay_gamma(7, 7, 60, 0.25)$p, cm_delay_gamma(7, 7, 60, 0.25)$p, cm_delay_skip(60, 0.25)$p), nrow = 3, byrow = T)),
list(source = "to_icu", type = "multinomial", names = "icu", report = "p",
prob = matrix(1, nrow = 1, ncol = 16, byrow = T),
delays = matrix(cm_delay_gamma(10, 10, 60, 0.25)$p, nrow = 1, byrow = T)),
list(source = "to_nonicu", type = "multinomial", names = "nonicu", report = "p",
prob = matrix(1, nrow = 1, ncol = 16, byrow = T),
delays = matrix(cm_delay_gamma(8, 8, 60, 0.25)$p, nrow = 1, byrow = T)),
list(source = "Ip", type = "multinomial", names = c("death", "null"), report = c("o", ""),
prob = matrix(c(P.death_nonicu, 1 - P.death_nonicu), nrow = 2, ncol = 16, byrow = T),
delays = matrix(c(cm_delay_gamma(22, 22, 60, 0.25)$p, cm_delay_skip(60, 0.25)$p), nrow = 2, byrow = T))
)
parameters$processes = burden_processes
# Observer for lockdown scenarios
observer_lockdown = function(lockdown_trigger) function(time, dynamics)
{
# Get current icu prevalence
icu_prevalence = dynamics[t == time, sum(icu_p)];
# Determine lockdown trigger
trigger = lockdown_trigger;
# If ICU prevalence exceeds a threshold, turn on lockdown
if (icu_prevalence >= trigger) {
return (list(csv = paste(time, "trace_lockdown", "All", 2, sep = ","),
changes = list(contact_lowerto = c(1, 0.1, 0.1, 0.1, 1, 0.1, 0.1, 0.1, 1))));
} else {
return (list(csv = paste(time, "trace_lockdown", "All", 1, sep = ","),
changes = list(contact_lowerto = c(1, 1, 1, 1, 1, 1, 1, 1, 1))));
}
return (list(csv = paste(time, "trace_lockdown", "All", 1, sep = ",")))
}
# Load age-varying symptomatic rate
covid_scenario = qread(paste0(covid_uk_path, "/data/2-linelist_symp_fit_fIa0.5.qs"));
# Identify London boroughs for early seeding, and regions of each country for time courses
london = cm_structure_UK[match(str_sub(locations, 6), Name), Geography1 %like% "London"]
england = cm_structure_UK[match(str_sub(locations, 6), Name), Code %like% "^E" & !(Geography1 %like% "London")]
wales = cm_structure_UK[match(str_sub(locations, 6), Name), Code %like% "^W"]
scotland = cm_structure_UK[match(str_sub(locations, 6), Name), Code %like% "^S"]
nireland = cm_structure_UK[match(str_sub(locations, 6), Name), Code %like% "^N"]
westmid = cm_structure_UK[match(str_sub(locations, 6), Name), Name == "West Midlands (Met County)"]
cumbria = cm_structure_UK[match(str_sub(locations, 6), Name), Name == "Cumbria"]
save = function(run)
{
# if (analysis == 3) {
# filename = paste0("~/Dropbox/COVID-UK Storage/", run$dynamics$scenario[1], "-", run$dynamics$run[1], ".qs");
# cm_save(run, filename);
# }
}
add_totals = function(run, totals)
{
regions = run$dynamics[, unique(population)];
# totals by age
totals0 = run$dynamics[, .(total = sum(value)), by = .(scenario, run, compartment, group)];
return (rbind(totals, totals0))
}
add_dynamics = function(run, dynamics, iv)
{
regions = run$dynamics[, unique(population)];
interv = data.table(scenario = run$dynamics$scenario[1], run = run$dynamics$run[1], t = unique(run$dynamics$t),
compartment = "trace_school", region = "All", value = unlist(iv$trace_school));
if (!is.null(iv$trace_intervention)) {
interv = rbind(interv,
data.table(scenario = run$dynamics$scenario[1], run = run$dynamics$run[1], t = unique(run$dynamics$t),
compartment = "trace_intervention", region = "All", value = unlist(iv$trace_intervention)));
} else {
interv = rbind(interv,
data.table(scenario = run$dynamics$scenario[1], run = run$dynamics$run[1], t = unique(run$dynamics$t),
compartment = "trace_intervention", region = "All", value = 1));
}
csvlines = NULL;
if (nchar(run$csv[[1]]) > 0) {
csvlines = fread(run$csv[[1]], header = F);
csvlines = cbind(run$dynamics$scenario[1], run$dynamics$run[1], csvlines);
names(csvlines) = c("scenario", "run", "t", "compartment", "region", "value");
csvlines = unique(csvlines);
}
# time courses
return (rbind(dynamics,
run$dynamics[population %in% locations[westmid], .(region = "West Midlands", value = sum(value)), by = .(scenario, run, t, compartment)],
run$dynamics[population %in% locations[cumbria], .(region = "Cumbria", value = sum(value)), by = .(scenario, run, t, compartment)],
run$dynamics[population %in% locations[london], .(region = "London", value = sum(value)), by = .(scenario, run, t, compartment)],
run$dynamics[population %in% locations[england], .(region = "England", value = sum(value)), by = .(scenario, run, t, compartment)],
run$dynamics[population %in% locations[wales], .(region = "Wales", value = sum(value)), by = .(scenario, run, t, compartment)],
run$dynamics[population %in% locations[scotland], .(region = "Scotland", value = sum(value)), by = .(scenario, run, t, compartment)],
run$dynamics[population %in% locations[nireland], .(region = "Northern Ireland", value = sum(value)), by = .(scenario, run, t, compartment)],
run$dynamics[, .(region = "United Kingdom", value = sum(value)), by = .(scenario, run, t, compartment)],
interv,
csvlines
))
}
#############
# MAIN CODE #
#############
argv = commandArgs(trailingOnly = T);
argc = length(argv);
if (argc != 2) {
stop("Must provide two arguments: analysis set and number of runs.");
}
analysis = as.numeric(argv[argc-1]);
n_runs = as.numeric(argv[argc]);
if (analysis == 1) {
# Define school terms, base versus intervention (both same here)
school_close_b = c("2020-2-16", "2020-4-05", "2020-5-24", "2020-7-22", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_b = c("2020-2-22", "2020-4-18", "2020-5-30", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
school_close_i = c("2020-2-16", "2020-4-05", "2020-5-24", "2020-7-22", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_i = c("2020-2-22", "2020-4-18", "2020-5-30", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
# Define interventions to be used
interventions = list(
`School Closures` = list(contact = c(1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0)),
`Social Distancing` = list(contact = c(1.0, 0.5, 1.0, 0.5, 1.0, 0.5, 1.0, 0.5, 0)),
`Elderly Shielding` = list(contact = c(1.0, 1.0, 1.0, 1.0, 1.0, 0.25, 1.0, 0.25, 0)),
`Self-Isolation` = list(fIs = rep(0.65, 16)),
`Combination` = list(contact = c(1.0, 0.5, 0.0, 0.5, 1.0, 0.25, 0.0, 0.25, 0), fIs = rep(0.65, 16))
);
# Set options
option.trigger = "national";
option.duration = 7 * 12;
option.lockdown = NA;
option.intervention_shift = 0;
} else if (analysis == 2.1) {
# Define school terms, base versus intervention (both same here)
school_close_b = c("2020-2-16", "2020-4-05", "2020-5-24", "2020-7-22", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_b = c("2020-2-22", "2020-4-18", "2020-5-30", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
school_close_i = c("2020-2-16", "2020-4-05", "2020-5-24", "2020-7-22", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_i = c("2020-2-22", "2020-4-18", "2020-5-30", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
# Define interventions to be used
interventions = list(
`Combination` = list(contact = c(1.0, 0.5, 0.0, 0.5, 1.0, 0.25, 0.0, 0.25, 0), fIs = rep(0.65, 16))
);
# Set options
option.trigger = "national";
option.duration = 7 * 12;
option.lockdown = NA;
option.intervention_shift = c(0, 14, 28, 56);
} else if (analysis == 2.2) {
# Define school terms, base versus intervention (both same here)
school_close_b = c("2020-2-16", "2020-4-05", "2020-5-24", "2020-7-22", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_b = c("2020-2-22", "2020-4-18", "2020-5-30", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
school_close_i = c("2020-2-16", "2020-4-05", "2020-5-24", "2020-7-22", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_i = c("2020-2-22", "2020-4-18", "2020-5-30", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
# Define interventions to be used
interventions = list(
`Combination` = list(contact = c(1.0, 0.5, 0.0, 0.5, 1.0, 0.25, 0.0, 0.25, 0), fIs = rep(0.65, 16))
);
# Set options
option.trigger = "local";
option.duration = 7 * 12;
option.lockdown = NA;
option.intervention_shift = c(0, 14, 28, 56);
} else if (analysis == 3) {
# Define school terms, base versus intervention (schools close from 23 March)
school_close_b = c("2020-2-16", "2020-4-05", "2020-5-24", "2020-7-22", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_b = c("2020-2-22", "2020-4-18", "2020-5-30", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
school_close_i = c("2020-2-16", "2020-3-23", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_i = c("2020-2-22", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
# Define interventions to be used
interventions = list(
`Intensive Interventions` = list(contact = c(1, 0.655, 1, 0.59155, 1, 0.25, 1, 0.157375, 0), fIs = rep(0.65, 16))
);
# Set options
option.trigger = "2020-03-17";
option.duration = 364;
option.lockdown = c(NA, 1000, 2000, 5000);
option.intervention_shift = 0;
} else if (analysis == 4) {
# Define school terms, base versus intervention (both same here)
school_close_b = c("2020-2-16", "2020-4-05", "2020-5-24", "2020-7-22", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_b = c("2020-2-22", "2020-4-18", "2020-5-30", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
school_close_i = c("2020-2-16", "2020-4-05", "2020-5-24", "2020-7-22", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_i = c("2020-2-22", "2020-4-18", "2020-5-30", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
# Define interventions to be used
interventions = list(
`Intensive` = list(contact = c(1, 0.655, 1, 0.59155, 1, 0.25, 1, 0.157375, 0.0), fIs = rep(0.65, 16)),
`Intensive + School` = list(contact = c(1, 0.655, 0, 0.59155, 1, 0.25, 0, 0.157375, 0.0), fIs = rep(0.65, 16)),
`Intensive + School + G20` = list(contact = c(1, 0.655, 0, 0.59155, 1, 0.25, 0, 0.157375, 0.2), fIs = rep(0.65, 16)),
`Intensive + School + G50` = list(contact = c(1, 0.655, 0, 0.59155, 1, 0.25, 0, 0.157375, 0.5), fIs = rep(0.65, 16)),
`Intensive + School + G100` = list(contact = c(1, 0.655, 0, 0.59155, 1, 0.25, 0, 0.157375, 1.0), fIs = rep(0.65, 16))
);
# Set options
option.trigger = "2020-03-17";
option.duration = 125;
option.lockdown = NA;
option.intervention_shift = 0;
parameters$time1 = "2020-07-20";
} else if (analysis == 6) {
# Define school terms, base versus intervention (both same here)
school_close_b = c("2020-2-16", "2020-4-05", "2020-5-24", "2020-7-22", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_b = c("2020-2-22", "2020-4-18", "2020-5-30", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
school_close_i = c("2020-2-16", "2020-4-05", "2020-5-24", "2020-7-22", "2020-10-25", "2020-12-20", "2021-02-14", "2021-04-01", "2021-05-30", "2021-07-25");
school_reopen_i = c("2020-2-22", "2020-4-18", "2020-5-30", "2020-9-01", "2020-10-31", "2021-01-02", "2021-02-20", "2021-04-17", "2021-06-05", "2021-09-01");
# Define interventions to be used
interventions = list(
`Background` = list(contact = c(1, 1, 0, 1, 1, 0.25, 0, 0.25, 0), fIs = rep(0.65, 16)),
`Background + 0% Sports` = list(contact = c(1, 1, 0, 1 - 0.0041, 1, 0.25, 0, 0.25, 0), fIs = rep(0.65, 16)),
`Background + 25% Leisure` = list(contact = c(1, 1, 0, 1 - 0.362, 1, 0.25, 0, 0.25, 0), fIs = rep(0.65, 16))
);
# Set options
option.trigger = "2020-03-17";
option.duration = 168;
option.lockdown = NA;
option.intervention_shift = 0;
parameters$time1 = "2020-09-01";
}
# Pick R0s
set.seed(9876);
R0s = rnorm(n_runs, mean = 2.675739, sd = 0.5719293)
# Do runs
dynamics = data.table()
totals = data.table()
print(Sys.time())
set.seed(1234567);
for (r in 1:n_runs) {
cat(paste0(r, ": R0 = ", R0s[r], "\n"));
# 1. Pick age-varying symptomatic rate
covy = unname(unlist(covid_scenario[sample.int(nrow(covid_scenario), 1), f_00:f_70]));
covy = rep(covy, each = 2);
# 2. Calculate R0 adjustment needed
parametersUK1$pop[[1]]$y = covy;
u_adj = R0s[r] / cm_calc_R0(parametersUK1, 1);
# 3. Pick seeding times
seed_start = ifelse(london, sample(0:6, length(london), replace = T), sample(0:20, length(london), replace = T));
# 4. Do base model
# 4a. Set parameters
params = duplicate(parameters);
for (j in seq_along(params$pop)) {
params$pop[[j]]$u = params$pop[[j]]$u * u_adj;
params$pop[[j]]$y = covy;
params$pop[[j]]$seed_times = rep(seed_start[j] + 0:27, each = 2);
params$pop[[j]]$dist_seed_ages = cm_age_coefficients(25, 50, 5 * 0:16);
}
# CALCULATE IMPACT ON R0
if (analysis == 5) {
interventions = list(
`Base` = list(),
`School Closures` = list(contact = c(1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0)),
`Social Distancing` = list(contact = c(1.0, 0.5, 1.0, 0.5, 1.0, 0.5, 1.0, 0.5, 0)),
`Elderly Shielding` = list(contact = c(1.0, 1.0, 1.0, 1.0, 1.0, 0.25, 1.0, 0.25, 0)),
`Self-Isolation` = list(fIs = rep(0.65, 16)),
`Combination` = list(contact = c(1.0, 0.5, 0.0, 0.5, 1.0, 0.25, 0.0, 0.25, 0), fIs = rep(0.65, 16)),
`Intensive, schools open` = list(contact = c(1, 0.655, 1, 0.59155, 1, 0.25, 1, 0.157375, 0), fIs = rep(0.65, 16)),
`Intensive, schools closed` = list(contact = c(1, 0.655, 0, 0.59155, 1, 0.25, 0, 0.157375, 0), fIs = rep(0.65, 16)),
`Lockdown` = list(contact = c(1, 0.1, 0.1, 0.1, 1, 0.1, 0.1, 0.1, 0), fIs = rep(0.65, 16))
);
for (i in seq_along(interventions))
{
iR0s = rep(0, length(params$pop));
iweights = rep(0, length(params$pop));
for (j in seq_along(params$pop))
{
for (k in seq_along(interventions[[i]]))
{
params$pop[[j]][[names(interventions[[i]])[k]]] = interventions[[i]][[k]];
}
iR0s[j] = cm_calc_R0(params, j);
iweights[j] = sum(params$pop[[j]]$size);
}
weighted_R0 = weighted.mean(iR0s, iweights);
dynamics = rbind(dynamics, data.table(run = r, scenario = names(interventions)[i], R0 = weighted_R0));
}
next;
}
# 4b. Set school terms
iv = cm_iv_build(params)
cm_iv_set(iv, school_close_b, school_reopen_b, contact = c(1, 1, 0, 1, 1, 1, 0, 1, 1), trace_school = 2);
params = cm_iv_apply(params, iv);
# 4c. Run model
run = cm_simulate(params, 1, r);
run$dynamics[, run := r];
run$dynamics[, scenario := "Base"];
run$dynamics[, R0 := R0s[r]];
save(run);
totals = add_totals(run, totals);
dynamics = add_dynamics(run, dynamics, iv);
peak_t = run$dynamics[compartment == "cases", .(total_cases = sum(value)), by = t][, t[which.max(total_cases)]];
peak_t_bypop = run$dynamics[compartment == "cases", .(total_cases = sum(value)), by = .(t, population)][, t[which.max(total_cases)], by = population]$V1;
rm(run)
gc()
# 5. Run interventions
for (i in seq_along(interventions)) {
for (duration in option.duration) {
for (trigger in option.trigger) {
for (intervention_shift in option.intervention_shift) {
for (lockdown in option.lockdown) {
cat(paste0(names(interventions)[i], "...\n"))
# 5a. Make parameters and adjust R0
params = duplicate(parameters);
for (j in seq_along(params$pop)) {
params$pop[[j]]$u = params$pop[[j]]$u * u_adj;
params$pop[[j]]$y = covy;
if (!is.na(lockdown)) {
params$pop[[j]]$observer = observer_lockdown(lockdown);
}
}
# 5b. Set interventions
if (trigger == "national") {
intervention_start = peak_t - duration / 2 + intervention_shift;
} else if (trigger == "local") {
intervention_start = peak_t_bypop - duration / 2 + intervention_shift;
} else {
intervention_start = as.numeric(ymd(trigger) - ymd(params$date0));
}
if (trigger == "local") {
# Apply interventions to one population at a time.
for (pi in seq_along(params$pop)) {
ymd_start = ymd(params$date0) + intervention_start[pi];
ymd_end = ymd_start + duration - 1;
iv = cm_iv_build(params)
cm_iv_set(iv, school_close_i, school_reopen_i, contact = c(1, 1, 0, 1, 1, 1, 0, 1, 1), trace_school = 2);
cm_iv_set(iv, ymd_start, ymd_end, interventions[[i]]);
cm_iv_set(iv, ymd_start, ymd_end, trace_intervention = 2);
params = cm_iv_apply(params, iv, pi);
}
} else {
# Apply interventions to entire population.
ymd_start = ymd(params$date0) + intervention_start;
ymd_end = ymd_start + duration - 1;
iv = cm_iv_build(params)
cm_iv_set(iv, school_close_i, school_reopen_i, contact = c(1, 1, 0, 1, 1, 1, 0, 1, 1), trace_school = 2);
cm_iv_set(iv, ymd_start, ymd_end, interventions[[i]]);
cm_iv_set(iv, ymd_start, ymd_end, trace_intervention = 2);
params = cm_iv_apply(params, iv);
}
# 5c. Run model
run = cm_simulate(params, 1, r);
tag = "";
if (length(option.duration) > 1) { tag = paste0(tag, " ", duration + 1, " day"); }
if (length(option.lockdown) > 1) { tag = paste0(tag, " ", ifelse(lockdown >= 0, lockdown, "variable"), " lockdown"); }
if (length(option.trigger) > 1) { tag = paste0(tag, " ", trigger, " trigger"); }
if (length(option.intervention_shift) > 1) { tag = paste0(tag, " ", intervention_shift, " shift"); }
run$dynamics[, run := r];
run$dynamics[, scenario := paste0(names(interventions)[i], tag)];
run$dynamics[, R0 := R0s[r]];
save(run);
totals = add_totals(run, totals);
dynamics = add_dynamics(run, dynamics, iv);
rm(run)
gc()
}
}
}
}
}
}
cm_save(totals, paste0(covid_uk_path, analysis, "-totals.qs"));
cm_save(dynamics, paste0(covid_uk_path, analysis, "-dynamics.qs"));
print(Sys.time())