Transmission risk estimation
Bryan Mayer
2020-02-01
Last updated: 2020-02-01
Checks: 7 0
Knit directory: HHVtransmission/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | a1d2e7b | Bryan | 2020-01-24 | update with documentation |
| Rmd | af2a4c1 | Bryan | 2019-12-30 | all updates after co-author review; edits to tables/figs; ID75 |
| html | af2a4c1 | Bryan | 2019-12-30 | all updates after co-author review; edits to tables/figs; ID75 |
| Rmd | 1ed26ae | Bryan | 2019-11-20 | transmission risk rmd between sensitivity and update |
| Rmd | 8f822dd | Bryan Mayer | 2019-07-26 | mid code update with sensitivity analysis |
| html | 8f822dd | Bryan Mayer | 2019-07-26 | mid code update with sensitivity analysis |
| Rmd | ce0f229 | Bryan Mayer | 2019-07-09 | analysis through first-final draft |
| html | ce0f229 | Bryan Mayer | 2019-07-09 | analysis through first-final draft |
| Rmd | 91ba870 | Bryan Mayer | 2019-07-09 | finished transmission risk |
| html | 91ba870 | Bryan Mayer | 2019-07-09 | finished transmission risk |
| Rmd | b8fb749 | Bryan Mayer | 2019-06-07 | first go at transmission risk |
| html | b8fb749 | Bryan Mayer | 2019-06-07 | first go at transmission risk |
This is the main analysis and results for the manuscript. For pre-processing of the model data and a brief background on the model, see transmission model data setup and background. For full model fitting and sensitivity analysis, see the analysis of model fitting and sensitivity analysis. The sensitivity analysis had downstream effects on the final model analysis presented here.
Data Setup
library(tidyverse)
library(conflicted)
library(kableExtra)
library(cowplot)
library(scales)
library(lhs)
source("code/processing_functions.R")
conflict_prefer("filter", "dplyr")
theme_set(
theme_bw() +
theme(panel.grid.minor = element_blank(),
legend.position = "top")
)
load("output/preprocess-model-data/model_data.RData")
final_model = read_rds("output/final-model/final_model.rds")
# function calculate IDX (e.g., X (prob) = 50 for ID50)
IDX_calc = function(b0, bE, prob){
(-log(1-prob) - b0)/(bE)
}Model results
options(knitr.kable.NA = '')
exposure_summary = model_data_long %>%
group_by(virus, idpar) %>%
summarize(
mean_exposure = 10^mean(count[count > 0]),
mean_exposure2 = 10^mean(count),
max_exposure = max(exposure)
) %>%
mutate(parameter = paste0("b", idpar)) %>%
ungroup()
combined_wide = final_model %>%
subset(model == "Combined") %>%
pivot_wider(names_from = idpar, values_from = betaE) %>%
rename(betaM = M, betaS = S)
final_model_tab = final_model %>%
select(virus, model, idpar, beta0, betaE, null_beta, pvalue, loglik, null_loglik) %>%
arrange(desc(virus), desc(model), idpar) %>%
gather(parameter, estimate, beta0, betaE, null_beta) %>%
select(virus, model, idpar, parameter, estimate, pvalue, null_loglik, loglik) %>%
mutate(
model_cat = case_when(
parameter == "null_beta" ~ "Constant",
model == "Combined" ~ "CM",
TRUE ~ idpar
),
loglik = if_else(parameter == "null_beta", null_loglik, loglik),
parameter = case_when(
parameter == "null_beta" ~ "b0",
parameter == "betaE" ~ str_c("b", idpar),
TRUE ~ str_remove(parameter, "eta")
),
model = factor(
model_cat,
levels = c("Constant", "S", "M", "HH", "CM"),
labels = c(
"Constant risk",
"Secondary children",
"Mother",
"Household sum",
"Combined model"
)
),
pvalue = if_else(model == "Constant risk", NA_real_, pvalue)
) %>%
ungroup() %>%
select(-model_cat, -null_loglik, -idpar) %>%
distinct()
beta0_ests = final_model_tab %>%
subset(model != "Constant risk" & parameter == "b0") %>%
select(virus, model, estimate) %>%
rename(beta0 = estimate)final_model_tab %>%
select(virus, model, parameter, estimate, loglik) %>%
arrange(desc(virus), model) %>%
write_csv("output/results-tables/supp_table4.csv") %>%
mutate_if(is.numeric, format, digits = 3) %>%
kable(digits = 3, caption = "Parameter model estimates") %>%
kable_styling(full_width = F) %>%
collapse_rows(c(1:2, 5), valign = "top")| virus | model | parameter | estimate | loglik |
|---|---|---|---|---|
| HHV-6 | Constant risk | b0 | 3.67e-02 | 91.2 |
| Secondary children | b0 | 2.10e-02 | 84.8 | |
| bS | 9.13e-07 | |||
| Mother | b0 | 2.93e-02 | 87.5 | |
| bM | 7.96e-06 | |||
| Household sum | b0 | 2.03e-02 | 84.6 | |
| bHH | 9.12e-07 | |||
| Combined model | b0 | 1.82e-02 | 84.2 | |
| bM | 6.56e-06 | |||
| bS | 8.03e-07 | |||
| CMV | Constant risk | b0 | 1.98e-02 | 74.1 |
| Secondary children | b0 | 1.94e-02 | ||
| bS | 0.00e+00 | |||
| Mother | b0 | 1.94e-02 | ||
| bM | 0.00e+00 | |||
| Household sum | b0 | 1.94e-02 | ||
| bHH | 0.00e+00 | |||
| Combined model | b0 | 1.94e-02 | ||
| bM | 1.89e-114 | |||
| bS | 0.00e+00 |
exposure_summary %>%
arrange(desc(virus), desc(idpar)) %>%
select(-mean_exposure2, -parameter) %>%
mutate_if(is.numeric, format, digits = 3, scientific = T) %>%
kable(digits = 3, caption = "Parameter model estimates") %>%
kable_styling(full_width = F) %>%
collapse_rows(c(1), valign = "top")| virus | idpar | mean_exposure | max_exposure |
|---|---|---|---|
| HHV-6 | S | 9.52e+03 | 3.18e+06 |
| M | 6.55e+02 | 4.73e+05 | |
| HH | 8.77e+03 | 3.18e+06 | |
| CMV | S | 5.27e+03 | 5.26e+06 |
| M | 3.64e+02 | 7.50e+03 | |
| HH | 5.83e+03 | 5.26e+06 |
constant_risk_res = final_model_tab %>%
subset(model == "Constant risk") %>%
mutate(
model = as.character(model),
constant_risk = 1 - exp(-estimate)
) %>%
select(virus, model, constant_risk)
final_model_tab %>%
subset(model %in% c("Mother", "Secondary children", "Household sum")) %>%
pivot_wider(names_from = parameter, values_from = c(estimate)) %>%
mutate(idpar = if_else(model == "Household sum", "HH",
substr(as.character(model), 1, 1))) %>%
left_join(exposure_summary, by = c("virus", "idpar")) %>%
mutate(
bE = if_else(model == "Household sum", bHH, pmax(bM, bS, na.rm = T)),
constant_risk = 1 - exp(-b0),
exposure_risk_mean = 1 - exp(-b0 - bE * mean_exposure),
exposure_risk_max = 1 - exp(-b0 - bE * max_exposure)
) %>%
select(virus, idpar, constant_risk, mean_exposure, exposure_risk_mean, max_exposure, exposure_risk_max, pvalue) %>%
rename(model = idpar) %>%
bind_rows(constant_risk_res) %>%
mutate_at(vars(mean_exposure, max_exposure), (function(x) round_away_0(log10(x), 2, trailing_zeros = T))) %>%
mutate_at(vars(contains("risk")), (function(x) round_away_0(100*x, 2, trailing_zeros = T))) %>%
mutate(
pvalue = clean_pvalues(pvalue, sig_alpha = 0),
model = factor(
model,
levels = c("Constant risk", "S", "M", "HH"),
labels = c("Null", "Secondary children",
"Mother",
"Household sum")
)
) %>%
arrange(desc(virus), model) %>%
write_csv("output/results-tables/individual_risk_tab.csv") %>%
kable(digits = 3, caption = "Parameter model estimates") %>%
kable_styling(full_width = F) %>%
collapse_rows(c(1), valign = "top")| virus | model | constant_risk | mean_exposure | exposure_risk_mean | max_exposure | exposure_risk_max | pvalue |
|---|---|---|---|---|---|---|---|
| HHV-6 | Null | 3.60 | — | ||||
| Secondary children | 2.07 | 3.98 | 2.92 | 6.50 | 94.66 | <0.001 | |
| Mother | 2.88 | 2.82 | 3.39 | 5.68 | 97.75 | 0.007 | |
| Household sum | 2.01 | 3.94 | 2.79 | 6.50 | 94.63 | <0.001 | |
| CMV | Null | 1.96 | — | ||||
| Secondary children | 1.92 | 3.72 | 1.92 | 6.72 | 1.92 | 0.939 | |
| Mother | 1.92 | 2.56 | 1.92 | 3.88 | 1.92 | 0.939 | |
| Household sum | 1.92 | 3.77 | 1.92 | 6.72 | 1.92 | 0.939 |
final_model_tab %>%
subset(model %in% c("Combined model")) %>%
mutate(idpar = substr(parameter, 2, 2)) %>%
select(-parameter) %>%
left_join(exposure_summary, by = c("virus", "idpar")) %>%
group_by(virus, pvalue, idpar) %>%
summarize(
risk_comp_mean = if_else(idpar == "0", estimate, estimate * mean_exposure),
risk_comp_max = if_else(idpar == "0", estimate, estimate * max_exposure)
) %>%
ungroup() %>%
pivot_wider(names_from = idpar, values_from = c(risk_comp_mean, risk_comp_max)) %>%
mutate(
constant_risk = 1 - exp(-risk_comp_mean_0)
) %>%
pivot_longer(cols = c(risk_comp_mean_M, risk_comp_mean_S,
risk_comp_max_M, risk_comp_max_S)) %>%
mutate(
risk = 1 - exp(-risk_comp_mean_0 - value),
parm = str_remove_all(name, "risk_comp_"),
pvalue = clean_pvalues(pvalue, sig_alpha = 0)
) %>%
select(-name, -contains("risk_comp"), -value) %>%
spread(parm, risk) %>%
mutate_if(is.numeric, (function(x) round_away_0(100*x, 2, trailing_zeros = T))) %>%
select(virus, constant_risk, mean_S, max_S, mean_M, max_M, pvalue) %>%
arrange(desc(virus)) %>%
write_csv("output/results-tables/combined_risk_tab.csv") %>%
kable(digits = 3, caption = "Parameter risk estimates (combined model)") %>%
kable_styling(full_width = F) %>%
collapse_rows(c(1), valign = "top")| virus | constant_risk | mean_S | max_S | mean_M | max_M | pvalue |
|---|---|---|---|---|---|---|
| HHV-6 | 1.80 | 2.55 | 92.39 | 2.22 | 95.58 | <0.001 |
| CMV | 1.92 | 1.92 | 1.92 | 1.92 | 1.92 | 0.997 |
Infectious dose calculations
final_model_tab %>%
subset(parameter != "b0" & model != "Household sum" & virus == "HHV-6") %>%
select(-pvalue, -loglik) %>%
group_by(virus, model) %>%
left_join(beta0_ests, by = c("virus", "model")) %>%
mutate(
exposure_source = factor(case_when(
parameter == "bS" ~ "Secondary Children",
parameter == "bM" ~ "Mother"
), levels = c("Secondary Children", "Mother")),
ID25 = IDX_calc(beta0, estimate, 0.25),
ID50 = IDX_calc(beta0, estimate, 0.5),
ID75 = IDX_calc(beta0, estimate, 0.75)
) %>%
ungroup() %>%
mutate_if(is.numeric, (function(x) round_away_0(log10(x), 2, trailing_zeros = T))) %>%
arrange(model, exposure_source) %>%
write_csv("output/results-tables/hhv6_id50_tab.csv") %>%
select(-virus, -estimate, -beta0, -parameter) %>%
kable(digits = 2) %>%
kable_styling(full_width = F) %>%
collapse_rows(1:2, valign = "top") %>%
add_header_above(c(" " = 2, "HHV-6 Infectious dose (ID)" = 3))| model | exposure_source | ID25 | ID50 | ID75 |
|---|---|---|---|---|
| Secondary children | Secondary Children | 5.47 | 5.87 | 6.17 |
| Mother | Mother | 4.51 | 4.92 | 5.23 |
| Combined model | Secondary Children | 5.53 | 5.92 | 6.23 |
| Mother | 4.61 | 5.01 | 5.32 |
Dose-response results
risk_data = model_data_long %>%
subset(idpar != "HH") %>%
mutate(
exposure_cat = floor(count) + 0.5
) %>%
group_by(idpar, virus, exposure_cat) %>%
summarize(
total_exposures = n(),
total_infected = sum(infectious_1wk),
risk = mean(infectious_1wk)
)
risk_grid = model_data %>%
mutate(
exposure_S = floor(S) + 0.5,
exposure_M = floor(M) + 0.5
) %>%
group_by(virus, exposure_S, exposure_M) %>%
summarize(
total_exposures = n(),
total_infected = sum(infectious_1wk),
risk = mean(infectious_1wk)
)
risk_prediction_both = final_model %>%
subset(model == "Combined") %>%
left_join(exposure_summary, by = c("virus", "idpar")) %>%
ungroup() %>%
mutate(idpar = factor(idpar, levels = c("S", "M"))) %>%
group_by(virus, idpar) %>%
nest() %>%
mutate(
pred_res = map(data, ~with(.x, tibble(
exposure = seq(0, log10(max_exposure), length = 100),
risk = 1 - exp(-beta0 - betaE * 10^exposure)
)))
) %>%
unnest(pred_res)
dr_plots = map(c("HHV-6", "CMV"), function(i){
if(i == "CMV") {
xlab = expression(paste("Log"[10], " CMV VL (DNA copies/mL)"))
ybreaks = pretty_breaks()
}
if(i == "HHV-6") {
xlab = expression(paste("Log"[10], " HHV-6 VL (DNA copies/mL)"))
ybreaks = 0:4/4
}
risk_data %>%
subset(virus == i) %>%
ungroup() %>%
mutate(idpar = factor(idpar, levels = c("S", "M"))) %>%
ggplot(aes(x = exposure_cat, y = risk)) +
geom_bar(stat = "identity", fill = "grey50") +
geom_line(data = subset(risk_prediction_both, virus == i),
aes(x = exposure), size = 1.25) +
scale_y_continuous(paste("Estimated weekly risk of", i, "infection"),
breaks = ybreaks) +
scale_x_continuous(xlab,
breaks = 0:6+0.5,
labels = paste(0:6, 1:7, sep = "-")) +
facet_grid(.~idpar, labeller = as_labeller(c('S' = "Secondary Children", 'M' = "Mother"))) +
theme(legend.position = "top")
})
exposure_heats = map(c("HHV-6", "CMV"), function(i){
risk_grid %>%
subset(virus == i) %>%
ggplot(aes(x = exposure_S, y = exposure_M, fill = total_exposures,
label = paste(total_infected, total_exposures, sep = "/"))) +
geom_tile() +
#geom_label(label.padding = unit(0.1, "lines"), label.size = 0, fill = "white") +
geom_text(aes(colour = factor(total_exposures > 100)),
fontface = "bold", size = 3.5) +
scale_color_manual(guide=F, values = c("white", "black")) +
viridis::scale_fill_viridis(paste("Total", i, "exposures"), option = "viridis") +
scale_y_continuous(expression(paste("Mother log"[10], " VL (DNA copies/mL)")),
breaks = 0:6+0.5,
labels = paste(0:6, 1:7, sep = "-")) +
scale_x_continuous(expression(paste("Secondary children log"[10], " VL (DNA copies/mL)")),
breaks = 0:6+0.5,
labels = paste(0:6, 1:7, sep = "-")) +
theme_classic() +
theme(legend.position = "bottom",
axis.title.x = element_text(size = 10.5),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8),
legend.key.width = unit(0.75, "cm"))
})
risk_heats = map(c("HHV-6", "CMV"), function(i){
if(i == "CMV") {
kbreaks = pretty_breaks()
klabels = function(x)identity(x)
}
if(i == "HHV-6") {
klabels = c(0:4/20, " >0.25")
kbreaks = c(0:5/20)
}
crossing(
virus = i,
exposure_S = seq(0,
log10(subset(exposure_summary, virus == i & idpar == "S")$max_exposure)+0.1,
length = 100),
exposure_M = seq(0,
log10(subset(exposure_summary, virus == i & idpar == "M")$max_exposure)+0.1,
length = 100)
) %>%
left_join(combined_wide, by = "virus") %>%
mutate(
risk = 1 - exp(-beta0 - betaS * 10^exposure_S - betaM * 10^exposure_M)
) %>%
ggplot(aes(x = exposure_S, y = exposure_M, fill = pmin(risk, 0.25))) +
geom_tile() +
scale_y_continuous(expression(paste("Mother log"[10], " VL (DNA copies/mL)")),
breaks = 1:7) +
scale_x_continuous(expression(paste("Secondary children log"[10], " VL (DNA copies/mL)")),
breaks = 1:7) +
scale_fill_distiller(paste(i, "weekly infection prob."),
palette = "RdYlBu", breaks= kbreaks,
labels = klabels) +
geom_text(data = subset(risk_grid, virus == i), aes(label = round(risk, 2)),
fontface = "bold", colour = "black") +
theme_classic() +
theme(legend.position = "bottom",
axis.title.x = element_text(size = 10.5),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8),
legend.key.width = unit(0.68, "cm"))
})plot_grid(dr_plots[[1]],
plot_grid(exposure_heats[[1]], risk_heats[[1]], nrow = 1, labels = c("(b)", "(c)"),
hjust = -0.2, vjust = 0.5),
nrow = 2, labels = "(a)")
| Version | Author | Date |
|---|---|---|
| af2a4c1 | Bryan | 2019-12-30 |
plot_grid(dr_plots[[2]],
plot_grid(exposure_heats[[2]], risk_heats[[2]], nrow = 1, labels = c("(b)", "(c)"),
hjust = -0.2, vjust = 0.5),
nrow = 2, labels = "(a)")
| Version | Author | Date |
|---|---|---|
| af2a4c1 | Bryan | 2019-12-30 |
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.2
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lhs_1.0.1 scales_1.1.0 cowplot_1.0.0 kableExtra_1.1.0
[5] conflicted_1.0.4 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3
[9] purrr_0.3.3 readr_1.3.1 tidyr_1.0.0 tibble_2.1.3
[13] ggplot2_3.2.1 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 lubridate_1.7.4 lattice_0.20-38
[4] assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.23
[7] plyr_1.8.4 R6_2.4.1 cellranger_1.1.0
[10] backports_1.1.5 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.1 highr_0.8 pillar_1.4.2
[16] rlang_0.4.2 lazyeval_0.2.2 readxl_1.3.1
[19] rstudioapi_0.10 whisker_0.4 rmarkdown_1.17
[22] labeling_0.3 webshot_0.5.1 selectr_0.4-1
[25] munsell_0.5.0 broom_0.5.2 compiler_3.6.1
[28] modelr_0.1.5 xfun_0.10 pkgconfig_2.0.3
[31] htmltools_0.4.0 tidyselect_0.2.5 gridExtra_2.3
[34] workflowr_1.4.0 viridisLite_0.3.0 crayon_1.3.4
[37] dbplyr_1.4.2 withr_2.1.2 grid_3.6.1
[40] nlme_3.1-142 jsonlite_1.6 gtable_0.3.0
[43] lifecycle_0.1.0 DBI_1.0.0 git2r_0.26.1
[46] magrittr_1.5 cli_1.1.0 stringi_1.4.3
[49] reshape2_1.4.3 farver_2.0.1 viridis_0.5.1
[52] fs_1.3.1 xml2_1.2.2 ellipsis_0.3.0
[55] generics_0.0.2 vctrs_0.2.2 RColorBrewer_1.1-2
[58] tools_3.6.1 glue_1.3.1 hms_0.5.3
[61] yaml_2.2.0 colorspace_1.4-1 rvest_0.3.5
[64] memoise_1.1.0 knitr_1.25 haven_2.2.0