analysis/01_test_run.R
In SC-COSMO/sccosmomcma: Stanford-CIDE COronavirus Simulation MOdel (SC-COSMO) for Mexico City Metropolitan Area Analysis
rm(list = ls()) # to clean the workspace
#### 02.1 Load packages and functions ####
#### 02.1.1 Load packages and functions ####
library(deSolve)
library(reshape2)
library(tidyverse)
library(ggplot2)
library(grid) # For watermark
# library(sccosmomcma)
devtools::load_all(".")
library(stringi)
library(data.table)
library(countrycode)
library(dampack)
######## Initial setup ######
choose_country <- "Mexico"
#choose_state <- "Ciudad de Mexico"
choose_state <- "MCMA"
df_density <- get_densities(country = choose_country,
state = choose_state)
l_contact_matrices <- get_contact_matrix(country = choose_country,
state = choose_state,
density = df_density$density)
df_mort_state_cty_age_temp <- get_lifetables(country = choose_country,
state = choose_state)
df_mort_state_cty_age <- df_mort_state_cty_age_temp %>%
dplyr::mutate(state = choose_state,
county = choose_state)
df_pop_state_cty_age_temp <- get_population_ages(country = choose_country,
state = choose_state)
df_pop_state_cty_age <- df_pop_state_cty_age_temp %>%
dplyr::mutate(state = choose_state,
county = choose_state)
df_pop_state_cty_age <- dplyr::left_join(df_pop_state_cty_age,
df_mort_state_cty_age)
df_pop_state_cty_age <- df_pop_state_cty_age %>%
dplyr::filter(!is.na(dx)) %>%
dplyr::mutate(deaths = (dx/lx)*age_pop) #%>%
# dplyr::rename(population = age_pop)
v_params_calib <- c(r_beta = 0.20,
r_soc_dist_factor = 0.75,
r_nu_exp2_dx_lb = 0.100, # 0.900,
r_nu_exp2_dx_ub = 0.005, # 0.900,
r_nu_exp2_dx_rate = 0.250,
n_nu_exp2_dx_mid = 16
)
test_sq = FALSE
if (test_sq == FALSE ) {
i1 <- make_intervention(intervention_type = "StatusQuo",
time_start = 0,
time_stop = 30)
i2 <- make_intervention(intervention_type = "SocialDistancing",
time_start = 30,
time_stop = 40,
intervention_factor = v_params_calib["r_soc_dist_factor"],
intervention_change_rate = 0.5)
i3 <- make_intervention(intervention_type = "SocialDistancingLinear",
time_start = 40,
time_stop = 55,
intervention_factor = v_params_calib["r_soc_dist_factor"],
intervention_factor_end = 0.80)
i4 <- make_intervention(intervention_type = "SocialDistancing",
time_start = 55,
time_stop = 240,
intervention_factor = v_params_calib["r_soc_dist_factor"],
intervention_change_rate = 0.5,
resume_school = TRUE,
school_intervention_factor = 1)
# resume_school = FALSE)
i5 <- make_intervention(intervention_type = "StatusQuo",
time_start = 240,
time_stop = 1000)
l_interventions <- add_intervention(interventions = NULL, intervention = i1)
l_interventions <- add_intervention(interventions = l_interventions, intervention = i2)
l_interventions <- add_intervention(interventions = l_interventions, intervention = i3)
l_interventions <- add_intervention(interventions = l_interventions, intervention = i4)
l_interventions <- add_intervention(interventions = l_interventions, intervention = i5)
} else {
i1 <- make_intervention(intervention_type = "StatusQuo",
time_start = 0,
time_stop = 1000)
l_interventions <- add_intervention(interventions = NULL, intervention = i1)
}
n_t <- 200
l_params_init <- load_params_init(n_t = n_t, # Number of days
ctry = choose_country,
ste = choose_state,
cty = choose_state,
l_contact_info = l_contact_matrices,
# v_omega = rep(1/10, 8),
# r_birth = (18/1000)/365.25,
r_beta = v_params_calib["r_beta"],
l_nu_exp2_dx = add_period(l_period_def = NULL,
l_period_add = make_period(
functional_form = "general logit",
time_start = 0,
time_stop = n_t,
val_start = as.numeric(v_params_calib["r_nu_exp2_dx_lb"]),
val_end = as.numeric(v_params_calib["r_nu_exp2_dx_ub"]),
v_logit_change_rate = as.numeric(v_params_calib["r_nu_exp2_dx_rate"]),
v_logit_change_mid = as.numeric(v_params_calib["n_nu_exp2_dx_mid"]))),
l_nu_inf2_dx = add_period(l_period_def = NULL,
l_period_add = make_period(
functional_form = "general logit",
time_start = 0,
time_stop = n_t,
val_start = as.numeric(v_params_calib["r_nu_exp2_dx_lb"]),
val_end = as.numeric(v_params_calib["r_nu_exp2_dx_ub"]),
v_logit_change_rate = as.numeric(v_params_calib["r_nu_exp2_dx_rate"]),
v_logit_change_mid = as.numeric(v_params_calib["n_nu_exp2_dx_mid"]))),
v_inf_init_ages = c(0, 0, 0, 1, 0, 0, 0, 0),
l_idx_scale_factor = get_const_multiage_list(n_t, rep(0, 8)),
l_interventions = l_interventions,
r_tau = v_params_calib["r_beta"]*1.5,
# v_cfr = rep(0, 8),
# v_ifr = rep(0, 8),
n_hhsize = 3,
m_r_exit_hns = rep(1/20, 8),
m_r_exit_icu = rep(1/30, 8),
m_r_exit_hs = 0.5*rep(1/20, 8) + 0.5*rep(1/30, 8),
m_sigma_hns = rep(10, 8),
m_sigma_hs = rep(10, 8),
m_sigma_icu = rep(7, 8)
#,
# v_omega = rep(1/180, 8)
)
# l_params_init$v_birth
# l_params_init$v_omega
# v_init_age_grps <- l_params_init$v_init_age_grps
l_params_all <- load_all_params(l_params_init = l_params_init)
# l_params_all$v_r_mort <- rep(0, 8)
# l_params_all$m_r_exp1_sx <- matrix(0, nrow = 8, ncol = 3)
# l_params_all$m_r_inf1_sx <- matrix(0, nrow = 8, ncol = 2)
# list2env(l_params_init, envir = .GlobalEnv)
# list2env(l_params_all, envir = .GlobalEnv)
sum(l_params_all$v_states_init[1:15])
system.time(
l_out_cosmo <- cosmo(l_params_all = l_params_all)
)
# diagnostics(deSolve::ode(y = l_params_all$v_states_init,
# times = l_params_all$v_times,
# func = ifelse(l_params_all$comp,
# yes = cosmo_dXdt_comp,
# no = cosmo_dxdt),
# parms = l_params_all))
View(l_out_cosmo$df_out_cosmo)
# v_pop <- as.numeric(l_out_cosmo$df_out_cosmo[17, -1])
rowSums(l_out_cosmo$df_out_cosmo[1:n_t, 2:(l_out_cosmo$l_params_all$n_states_ages+1)] < 0)
rowSums(l_out_cosmo$df_out_cosmo[1:n_t, (l_out_cosmo$l_params_all$n_states_ages+2):ncol(l_out_cosmo$df_out_cosmo)]<0.0)
# View(l_out_cosmo$df_out_cosmo[23:26, 2:(l_out_cosmo$l_params_all$n_states_ages+1)])
# View(l_out_cosmo$df_out_cosmo[, 2:(l_out_cosmo$l_params_all$n_states_ages+1)])
# View(l_out_cosmo$df_out_cosmo[1:40, (l_out_cosmo$l_params_all$n_states_ages+2):ncol(l_out_cosmo$df_out_cosmo)])
colnames(l_out_cosmo$df_out_cosmo)[l_out_cosmo$df_out_cosmo[23, ]<0]
apply(l_out_cosmo$df_out_cosmo, 1,
function(x) colnames(l_out_cosmo$df_out_cosmo)[x<0])
### Plot population size over time
plot_popsize_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_popsize_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
# ggsave(filename = "figs/02_cumulative_infections_total_mex_nothing.png", width = 8, height = 6)
### Plot total cumulative number of diagnosed infections over time
# df_InfIDXcumtot <- data.frame(Outcome = "Cumulative diagnosed infections",
# Intervention = "Do Nothing",
# time = calc_infidxcum_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_infidxcum_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_dxcum_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_dxcum_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_dxcum_totals(l_out_cosmo,
proportion = TRUE,
only_all = TRUE, print_plot = FALSE)
plot_dxcum_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_infidxcum_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
### Plot total cumulative number of diagnosed infections over time
# df_InfIDXInctot <- data.frame(Outcome = "Incidental diagnosed infections",
# Intervention = "Do Nothing",
# time = calc_infidxinc_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_infidxinc_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_dxinc_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_dxinc_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_dxinc_totals(l_out_cosmo,
proportion = TRUE,
only_all = TRUE, print_plot = FALSE)
plot_dxinc_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_infidxcum_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
### Plot total cumulative number of infections over time
# df_Infcumtot <- data.frame(Outcome = "Cumulative infections",
# Intervention = "Do Nothing",
# time = calc_infcum_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_infcum_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_infcum_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_infcum_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_infcum_totals(l_out_cosmo,
proportion = TRUE,
only_all = TRUE, print_plot = FALSE)
plot_infcum_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_infcum_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
# ggsave(filename = "figs/02_cumulative_infections_total_mex_nothing.png", width = 8, height = 6)
### Plot total incidental number of infections over time
# df_InfInctot <- data.frame(Outcome = "Incident infections",
# Intervention = "Do Nothing",
# time = calc_infinc_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_infinc_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_infinc_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_infinc_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_infinc_totals(l_out_cosmo,
proportion = TRUE,
only_all = TRUE, print_plot = FALSE)
plot_infinc_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_infcum_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
# ggsave(filename = "figs/02_cumulative_infections_total_mex_nothing.png", width = 8, height = 6)
### Plot total number of infections over time
# df_Inftot <- data.frame(Outcome = "Infections",
# Intervention = "Do Nothing",
# time = calc_inf_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_inf_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_inf_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_inf_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_inf_totals(l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_inf_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_inf_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
# ggsave(filename = "figs/02_infections_total_mex_nothing.png", width = 8, height = 6)
### Plot total number of diagnosed infections over time
# df_InfIDXtot <- data.frame(Outcome = "Prevalent diagnosed Infections",
# Intervention = "Do Nothing",
# time = calc_infidx_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_infidx_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_dx_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_dx_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_dx_totals(l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_dx_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_inf_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
# ggsave(filename = "figs/02_infections_total_mex_nothing.png", width = 8, height = 6)
### Plot total number of COVID19 deaths over time
# df_Dcov <- data.frame(Outcome = "COVID deaths",
# Intervention = "Do Nothing",
# time = calc_deaths_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_deaths_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_deaths_totals(l_out_cosmo = l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_deaths_totals(l_out_cosmo = l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_deaths_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_deaths_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
### Plot total number of COVID19 deaths from diagnosed infections over time
# df_DcovDX <- data.frame(Outcome = "COVID deaths from diagnosed infections",
# Intervention = "Do Nothing",
# time = calc_deathsdx_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_deathsdx_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_deathsdx_totals(l_out_cosmo = l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_deathsdx_totals(l_out_cosmo = l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_deathsdx_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_deathsdx_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
### Plot symptomatics
plot_sx_totals(l_out_cosmo = l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_sx_totals(l_out_cosmo = l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_sx_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_sx_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
### Plot E and Is
plot_expinf_totals(l_out_cosmo = l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_expinf_totals(l_out_cosmo = l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_expinf_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_expinf_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
#### Reproduction number ####
calc_basic_reproduction_number_kr(l_out_cosmo = l_out_cosmo,
v_time = 2:100)
calc_reproduction_number_wt(l_out_cosmo = l_out_cosmo,
v_time = 2:100,
nsim_chosen = 200)
#calc_reproduction_number_wt(l_out_cosmo = l_out_cosmo,
# v_time = 2:(l_out_cosmo$l_params_all$n_t-3))
#### Hospitalizations ####
l_out_hosp <- prep_dx_hospitalizations(l_out_cosmo = l_out_cosmo)
df_hosp_prev <- calc_dx_hosp(l_hosp = l_out_hosp, l_out_cosmo = l_out_cosmo)
plot(All ~ time, df_hosp_prev)
SC-COSMO/sccosmomcma documentation built on Dec. 18, 2021, 11:56 a.m.
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rm(list = ls()) # to clean the workspace
#### 02.1 Load packages and functions ####
#### 02.1.1 Load packages and functions ####
library(deSolve)
library(reshape2)
library(tidyverse)
library(ggplot2)
library(grid) # For watermark
# library(sccosmomcma)
devtools::load_all(".")
library(stringi)
library(data.table)
library(countrycode)
library(dampack)
######## Initial setup ######
choose_country <- "Mexico"
#choose_state <- "Ciudad de Mexico"
choose_state <- "MCMA"
df_density <- get_densities(country = choose_country,
state = choose_state)
l_contact_matrices <- get_contact_matrix(country = choose_country,
state = choose_state,
density = df_density$density)
df_mort_state_cty_age_temp <- get_lifetables(country = choose_country,
state = choose_state)
df_mort_state_cty_age <- df_mort_state_cty_age_temp %>%
dplyr::mutate(state = choose_state,
county = choose_state)
df_pop_state_cty_age_temp <- get_population_ages(country = choose_country,
state = choose_state)
df_pop_state_cty_age <- df_pop_state_cty_age_temp %>%
dplyr::mutate(state = choose_state,
county = choose_state)
df_pop_state_cty_age <- dplyr::left_join(df_pop_state_cty_age,
df_mort_state_cty_age)
df_pop_state_cty_age <- df_pop_state_cty_age %>%
dplyr::filter(!is.na(dx)) %>%
dplyr::mutate(deaths = (dx/lx)*age_pop) #%>%
# dplyr::rename(population = age_pop)
v_params_calib <- c(r_beta = 0.20,
r_soc_dist_factor = 0.75,
r_nu_exp2_dx_lb = 0.100, # 0.900,
r_nu_exp2_dx_ub = 0.005, # 0.900,
r_nu_exp2_dx_rate = 0.250,
n_nu_exp2_dx_mid = 16
)
test_sq = FALSE
if (test_sq == FALSE ) {
i1 <- make_intervention(intervention_type = "StatusQuo",
time_start = 0,
time_stop = 30)
i2 <- make_intervention(intervention_type = "SocialDistancing",
time_start = 30,
time_stop = 40,
intervention_factor = v_params_calib["r_soc_dist_factor"],
intervention_change_rate = 0.5)
i3 <- make_intervention(intervention_type = "SocialDistancingLinear",
time_start = 40,
time_stop = 55,
intervention_factor = v_params_calib["r_soc_dist_factor"],
intervention_factor_end = 0.80)
i4 <- make_intervention(intervention_type = "SocialDistancing",
time_start = 55,
time_stop = 240,
intervention_factor = v_params_calib["r_soc_dist_factor"],
intervention_change_rate = 0.5,
resume_school = TRUE,
school_intervention_factor = 1)
# resume_school = FALSE)
i5 <- make_intervention(intervention_type = "StatusQuo",
time_start = 240,
time_stop = 1000)
l_interventions <- add_intervention(interventions = NULL, intervention = i1)
l_interventions <- add_intervention(interventions = l_interventions, intervention = i2)
l_interventions <- add_intervention(interventions = l_interventions, intervention = i3)
l_interventions <- add_intervention(interventions = l_interventions, intervention = i4)
l_interventions <- add_intervention(interventions = l_interventions, intervention = i5)
} else {
i1 <- make_intervention(intervention_type = "StatusQuo",
time_start = 0,
time_stop = 1000)
l_interventions <- add_intervention(interventions = NULL, intervention = i1)
}
n_t <- 200
l_params_init <- load_params_init(n_t = n_t, # Number of days
ctry = choose_country,
ste = choose_state,
cty = choose_state,
l_contact_info = l_contact_matrices,
# v_omega = rep(1/10, 8),
# r_birth = (18/1000)/365.25,
r_beta = v_params_calib["r_beta"],
l_nu_exp2_dx = add_period(l_period_def = NULL,
l_period_add = make_period(
functional_form = "general logit",
time_start = 0,
time_stop = n_t,
val_start = as.numeric(v_params_calib["r_nu_exp2_dx_lb"]),
val_end = as.numeric(v_params_calib["r_nu_exp2_dx_ub"]),
v_logit_change_rate = as.numeric(v_params_calib["r_nu_exp2_dx_rate"]),
v_logit_change_mid = as.numeric(v_params_calib["n_nu_exp2_dx_mid"]))),
l_nu_inf2_dx = add_period(l_period_def = NULL,
l_period_add = make_period(
functional_form = "general logit",
time_start = 0,
time_stop = n_t,
val_start = as.numeric(v_params_calib["r_nu_exp2_dx_lb"]),
val_end = as.numeric(v_params_calib["r_nu_exp2_dx_ub"]),
v_logit_change_rate = as.numeric(v_params_calib["r_nu_exp2_dx_rate"]),
v_logit_change_mid = as.numeric(v_params_calib["n_nu_exp2_dx_mid"]))),
v_inf_init_ages = c(0, 0, 0, 1, 0, 0, 0, 0),
l_idx_scale_factor = get_const_multiage_list(n_t, rep(0, 8)),
l_interventions = l_interventions,
r_tau = v_params_calib["r_beta"]*1.5,
# v_cfr = rep(0, 8),
# v_ifr = rep(0, 8),
n_hhsize = 3,
m_r_exit_hns = rep(1/20, 8),
m_r_exit_icu = rep(1/30, 8),
m_r_exit_hs = 0.5*rep(1/20, 8) + 0.5*rep(1/30, 8),
m_sigma_hns = rep(10, 8),
m_sigma_hs = rep(10, 8),
m_sigma_icu = rep(7, 8)
#,
# v_omega = rep(1/180, 8)
)
# l_params_init$v_birth
# l_params_init$v_omega
# v_init_age_grps <- l_params_init$v_init_age_grps
l_params_all <- load_all_params(l_params_init = l_params_init)
# l_params_all$v_r_mort <- rep(0, 8)
# l_params_all$m_r_exp1_sx <- matrix(0, nrow = 8, ncol = 3)
# l_params_all$m_r_inf1_sx <- matrix(0, nrow = 8, ncol = 2)
# list2env(l_params_init, envir = .GlobalEnv)
# list2env(l_params_all, envir = .GlobalEnv)
sum(l_params_all$v_states_init[1:15])
system.time(
l_out_cosmo <- cosmo(l_params_all = l_params_all)
)
# diagnostics(deSolve::ode(y = l_params_all$v_states_init,
# times = l_params_all$v_times,
# func = ifelse(l_params_all$comp,
# yes = cosmo_dXdt_comp,
# no = cosmo_dxdt),
# parms = l_params_all))
View(l_out_cosmo$df_out_cosmo)
# v_pop <- as.numeric(l_out_cosmo$df_out_cosmo[17, -1])
rowSums(l_out_cosmo$df_out_cosmo[1:n_t, 2:(l_out_cosmo$l_params_all$n_states_ages+1)] < 0)
rowSums(l_out_cosmo$df_out_cosmo[1:n_t, (l_out_cosmo$l_params_all$n_states_ages+2):ncol(l_out_cosmo$df_out_cosmo)]<0.0)
# View(l_out_cosmo$df_out_cosmo[23:26, 2:(l_out_cosmo$l_params_all$n_states_ages+1)])
# View(l_out_cosmo$df_out_cosmo[, 2:(l_out_cosmo$l_params_all$n_states_ages+1)])
# View(l_out_cosmo$df_out_cosmo[1:40, (l_out_cosmo$l_params_all$n_states_ages+2):ncol(l_out_cosmo$df_out_cosmo)])
colnames(l_out_cosmo$df_out_cosmo)[l_out_cosmo$df_out_cosmo[23, ]<0]
apply(l_out_cosmo$df_out_cosmo, 1,
function(x) colnames(l_out_cosmo$df_out_cosmo)[x<0])
### Plot population size over time
plot_popsize_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_popsize_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
# ggsave(filename = "figs/02_cumulative_infections_total_mex_nothing.png", width = 8, height = 6)
### Plot total cumulative number of diagnosed infections over time
# df_InfIDXcumtot <- data.frame(Outcome = "Cumulative diagnosed infections",
# Intervention = "Do Nothing",
# time = calc_infidxcum_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_infidxcum_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_dxcum_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_dxcum_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_dxcum_totals(l_out_cosmo,
proportion = TRUE,
only_all = TRUE, print_plot = FALSE)
plot_dxcum_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_infidxcum_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
### Plot total cumulative number of diagnosed infections over time
# df_InfIDXInctot <- data.frame(Outcome = "Incidental diagnosed infections",
# Intervention = "Do Nothing",
# time = calc_infidxinc_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_infidxinc_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_dxinc_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_dxinc_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_dxinc_totals(l_out_cosmo,
proportion = TRUE,
only_all = TRUE, print_plot = FALSE)
plot_dxinc_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_infidxcum_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
### Plot total cumulative number of infections over time
# df_Infcumtot <- data.frame(Outcome = "Cumulative infections",
# Intervention = "Do Nothing",
# time = calc_infcum_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_infcum_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_infcum_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_infcum_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_infcum_totals(l_out_cosmo,
proportion = TRUE,
only_all = TRUE, print_plot = FALSE)
plot_infcum_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_infcum_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
# ggsave(filename = "figs/02_cumulative_infections_total_mex_nothing.png", width = 8, height = 6)
### Plot total incidental number of infections over time
# df_InfInctot <- data.frame(Outcome = "Incident infections",
# Intervention = "Do Nothing",
# time = calc_infinc_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_infinc_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_infinc_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_infinc_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_infinc_totals(l_out_cosmo,
proportion = TRUE,
only_all = TRUE, print_plot = FALSE)
plot_infinc_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_infcum_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
# ggsave(filename = "figs/02_cumulative_infections_total_mex_nothing.png", width = 8, height = 6)
### Plot total number of infections over time
# df_Inftot <- data.frame(Outcome = "Infections",
# Intervention = "Do Nothing",
# time = calc_inf_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_inf_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_inf_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_inf_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_inf_totals(l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_inf_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_inf_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
# ggsave(filename = "figs/02_infections_total_mex_nothing.png", width = 8, height = 6)
### Plot total number of diagnosed infections over time
# df_InfIDXtot <- data.frame(Outcome = "Prevalent diagnosed Infections",
# Intervention = "Do Nothing",
# time = calc_infidx_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_infidx_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_dx_totals(l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_dx_totals(l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_dx_totals(l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_dx_totals(l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
# plot_inf_totals(l_out_cosmo,
# proportion = TRUE, age_proportion = TRUE,
# only_all = TRUE, print_plot = FALSE)
# ggsave(filename = "figs/02_infections_total_mex_nothing.png", width = 8, height = 6)
### Plot total number of COVID19 deaths over time
# df_Dcov <- data.frame(Outcome = "COVID deaths",
# Intervention = "Do Nothing",
# time = calc_deaths_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_deaths_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_deaths_totals(l_out_cosmo = l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_deaths_totals(l_out_cosmo = l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_deaths_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_deaths_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
### Plot total number of COVID19 deaths from diagnosed infections over time
# df_DcovDX <- data.frame(Outcome = "COVID deaths from diagnosed infections",
# Intervention = "Do Nothing",
# time = calc_deathsdx_totals(l_out_cosmo = l_out_cosmo)[, 1],
# value = calc_deathsdx_totals(l_out_cosmo = l_out_cosmo)[, 10],
# check.names = F)
plot_deathsdx_totals(l_out_cosmo = l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_deathsdx_totals(l_out_cosmo = l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_deathsdx_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_deathsdx_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
### Plot symptomatics
plot_sx_totals(l_out_cosmo = l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_sx_totals(l_out_cosmo = l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_sx_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_sx_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
### Plot E and Is
plot_expinf_totals(l_out_cosmo = l_out_cosmo,
only_all = TRUE, print_plot = FALSE)
plot_expinf_totals(l_out_cosmo = l_out_cosmo,
only_all = FALSE, print_plot = FALSE)
plot_expinf_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
plot_expinf_totals(l_out_cosmo = l_out_cosmo,
proportion = TRUE, age_proportion = TRUE,
only_all = FALSE, print_plot = FALSE)
#### Reproduction number ####
calc_basic_reproduction_number_kr(l_out_cosmo = l_out_cosmo,
v_time = 2:100)
calc_reproduction_number_wt(l_out_cosmo = l_out_cosmo,
v_time = 2:100,
nsim_chosen = 200)
#calc_reproduction_number_wt(l_out_cosmo = l_out_cosmo,
# v_time = 2:(l_out_cosmo$l_params_all$n_t-3))
#### Hospitalizations ####
l_out_hosp <- prep_dx_hospitalizations(l_out_cosmo = l_out_cosmo)
df_hosp_prev <- calc_dx_hosp(l_hosp = l_out_hosp, l_out_cosmo = l_out_cosmo)
plot(All ~ time, df_hosp_prev)
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