update epinow2 entry and epiparameter version

analyses/estimate_severity/cfr-stratified-severity.qmd

@@ -107,7 +107,7 @@ dat_incidence
 delay_onset_death <-
   epiparameter::epiparameter_db(
     disease = "ebola",
-    epi_dist = "onset to death",
+    epi_name = "onset to death",
     single_epiparameter = TRUE
   )
 

analyses/forecast_cases/projections_incidence_epitrix.qmd

@@ -81,7 +81,7 @@ incidence2_fit <-
 epidist_ebola_si <- 
   epiparameter::epidist_db(
     disease = "Ebola",
-    epi_dist = "serial_interval",
+    epi_name = "serial_interval",
     single_epiparameter = TRUE
     )
 

analyses/quantify_transmission/reproduction_number_timevarying_epiestim_parametric.qmd → analyses/quantify_transmission/reproduction_timevarying_epinow2.qmd

@@ -44,7 +44,7 @@ library(tidyverse)
 dat <- subset(outbreaks::ebola_sim_clean$linelist ,!is.na(hospital)) %>% 
   dplyr::as_tibble()
 
-# View data
+# Print data
 dat
 
 # Get a linelist object
@@ -61,35 +61,38 @@ dat_linelist <- dat %>%
   # keep tagged and validated variables
   linelist::tags_df()
 
+# Print validated linelist
+dat_linelist
+
 # Get incidence object
 dat_incidence <- dat_linelist %>% 
-  # aggregate by date type
+  # aggregate cases by date of onset by days
   incidence2::incidence(
     date_index = "date_onset",
-    interval = "day", # for interoperability with {epinow2}
-    # complete_dates = TRUE, # it does affect the downstream analysis
-    date_names_to = "date", # for interoperability with {epinow2}
-    count_values_to = "confirm", # for interoperability with {epinow2}
+    interval = "day", 
+    # rename column outputs for interoperability with {epinow2}
+    date_names_to = "date",
+    count_values_to = "confirm",
   ) %>%
-  # reduce computation time (only for this demo code)
+  # keep date range between June and November 2014
   dplyr::filter(date>="2014-06-01" & date<"2014-10-01") %>%
-  # for interoperability with {epinow2}
-  dplyr::select(-count_variable) #%>%
-  # convert to tibble format for simpler data frame output
-  # dplyr::as_tibble()
+  # drop column for interoperability with {epinow2}
+  dplyr::select(-count_variable)
 
-# View incidence data
+# Print incidence data
 dat_incidence
 
+# Generation time ---------------------------------------------------------
+
 # Get serial interval delay
 serial_interval <-
   epiparameter::epiparameter_db(
     disease = "ebola",
-    epi_dist = "serial interval",
+    epi_name = "serial interval",
     single_epiparameter = TRUE
   )
 
-# Print serial interval information
+# Print serial interval metadata
 serial_interval
 
 # Get distribution parameters from delay 
@@ -101,22 +104,44 @@ serial_interval_gamma <- EpiNow2::Gamma(
   scale = serial_interval_param["scale"]
 )
 
-# Print EpiNow2 interface
+# Print EpiNow2 output interface
 serial_interval_gamma
 
+# Delays from infection to observed data ----------------------------------
+
+# Get fixed delay from infection to symptom onset
+incubation_period <- epiparameter::epiparameter_db(
+  disease = "ebola",
+  epi_name = "incubation",
+  single_epiparameter = TRUE
+)
+
+# Print incubation period metadata
+incubation_period
+
+# Get distribution parameters from delay 
+incubation_period_param <- epiparameter::get_parameters(incubation_period)
+
+# Adapt {epiparameter} to the {EpiNow2} distribution interface
+incubation_period_gamma <- EpiNow2::Gamma(
+  shape = incubation_period_param["shape"],
+  scale = incubation_period_param["scale"]
+)
+
+# Print EpiNow2 output interface
+incubation_period_gamma
+
+# Estimate transmissibility -----------------------------------------------
+
 # Configure parallel computation
 withr::local_options(base::list(mc.cores = 4))
 
-# Estimate transmissibility
 # WAIT this takes around 5 minutes
 # tictoc::tic()
 estimates <- EpiNow2::epinow(
-  # cases
   data = dat_incidence,
-  # delays
   generation_time = EpiNow2::generation_time_opts(serial_interval_gamma),
-  # reduce computation time (only for this demo code)
-  stan = EpiNow2::stan_opts(samples = 1000, chains = 2)
+  delays = EpiNow2::delay_opts(incubation_period_gamma)
 )
 # tictoc::toc()
 
@@ -134,7 +159,7 @@ plot(estimates)
 <!-- - The `linelist` object contains the first list element from `ebola_sim_clean`. -->
 <!-- - The `incidence()` function from the `incidence` package converts the vector `date_of_onset` from the `linelist` data frame to an `incidence` class object. -->
 
-<!-- - The `epidist_db()` function from the `epiparameter` package extract a parameter by specifying the disease name in the `disease` argument, epidemiological distribution in the `epi_dist` argument, and author name in the `author` argument. -->
+<!-- - The `epidist_db()` function from the `epiparameter` package extract a parameter by specifying the disease name in the `disease` argument, epidemiological distribution in the `epi_name` argument, and author name in the `author` argument. -->
 
 <!-- - The `estimate_R()` function from the `EpiEstim` package estimates the time-varying reproduction number (Rt). We provide the `incidence_data`, specify the method as `"parametric_si"` (parametric with a known serial interval), and pass the serial interval distribution parameters using the `make_config` function. -->
 <!-- - The `plot` function creates three plots from the `estimate_R` class object.  -->
@@ -145,4 +170,4 @@ Additionally, make sure to adjust the serial interval distribution parameters ac
 
 ## Related
 
-- [Explanation on the time-varying effective reproductive number](https://mrc-ide.github.io/EpiEstim/articles/full_EpiEstim_vignette.html)
+- [Explanation on the Infection model, delays and scaling, and Observation model](https://epiforecasts.io/EpiNow2/articles/estimate_infections.html)

analyses/reconstruct_transmission/estimate_infections.qmd

@@ -76,7 +76,7 @@ incidence_data
 incubation_period_in <-
   epiparameter::epidist_db(
     disease = "covid",
-    epi_dist = "incubation",
+    epi_name = "incubation",
     single_epiparameter = TRUE
   )
 
@@ -99,7 +99,7 @@ incubation_period <- EpiNow2::LogNormal(
 onset_to_death_period_in <-
   epiparameter::epidist_db(
     disease = "covid",
-    epi_dist = "onset to death",
+    epi_name = "onset to death",
     single_epiparameter = TRUE
   )
 
@@ -128,7 +128,7 @@ infection_to_death <- incubation_period + onset_to_death_period
 serial_interval_in <-
   epiparameter::epidist_db(
     disease = "covid",
-    epi_dist = "serial",
+    epi_name = "serial",
     single_epiparameter = TRUE
   )
 
@@ -222,7 +222,7 @@ incidence_data_ebola
 incubation_period_ebola_in <-
   epiparameter::epidist_db(
     disease = "ebola",
-    epi_dist = "incubation",
+    epi_name = "incubation",
     single_epiparameter = TRUE
   )
 
@@ -249,7 +249,7 @@ incubation_period_ebola <- EpiNow2::Gamma(
 serial_interval_ebola_in <-
   epiparameter::epidist_db(
     disease = "ebola",
-    epi_dist = "serial",
+    epi_name = "serial",
     single_epiparameter = TRUE
   )
 

internal/sandbox/reproduction_timevarying_epiestim_parametric.qmd

@@ -0,0 +1,95 @@
+---
+title: "How to quantify the time-varying reproduction number (R~t~)?"
+format: 
+  html:
+    code-link: true
+editor: source
+editor_options: 
+  chunk_output_type: console
+date: last-modified
+toc: true
+toc_float: true
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+## Ingredients
+
+- Simulated outbreak data of Ebola Virus Disease that matches some key properties of the West African Ebola outbreak of 2014-2015 from the package `{outbreaks}`.
+- The `{linelist}` package to keep tagged and validated columns in a line list data set.
+- The `{incidence2}` package to generate aggregated incidence data with the daily number of reported cases.
+- The `{epiparameter}` package to access to the serial interval estimated by the WHO Ebola Response Team in 2015.
+- Assume that the serial interval distribution approximates the generation time.
+- The `{EpiEstim}` package to estimate the time-varying reproduction number.
+
+## Steps in code
+
+```{r}
+#| warning: false
+
+# Load required packages
+library(outbreaks)
+library(incidence)
+library(epiparameter)
+library(EpiEstim)
+library(tidyverse)
+
+# Load the simulated Ebola outbreak data
+data(ebola_sim_clean)
+
+# Extract the first element of the list
+linelist <- ebola_sim_clean$linelist
+
+# Convert the data to an incidence object
+incidence_data <- incidence::incidence(linelist$date_of_onset)
+
+# Extract parameter by disease, distribution, author
+epidist_ebola <- 
+  epiparameter::epidist_db(
+    disease = "Ebola",
+    epi_name = "serial_interval",
+    single_epiparameter = TRUE
+    )
+
+# Estimate the time-varying reproduction number
+epiestim_output <- estimate_R(
+  incid = incidence_data, 
+  method = "parametric_si",
+  config = make_config(
+    list(
+      mean_si = epidist_ebola$summary_stats$mean,
+      std_si = epidist_ebola$summary_stats$sd
+    )
+  )
+)
+
+# Plot the time-varying reproduction number
+plot(epiestim_output)
+```
+
+## Steps in detail
+
+- The `outbreaks` package is loaded to access the simulated Ebola outbreak data.
+- The `epiparameter` package is loaded to access the library of epidemiological parameters.
+
+- The `ebola_sim_clean` object from the package contains the simulated outbreak data.
+- The `linelist` object contains the first list element from `ebola_sim_clean`.
+- The `incidence()` function from the `incidence` package converts the vector `date_of_onset` from the `linelist` data frame to an `incidence` class object.
+
+- The `epidist_db()` function from the `epiparameter` package extract a parameter by specifying the disease name in the `disease` argument, epidemiological distribution in the `epi_name` argument, and author name in the `author` argument.
+
+- The `estimate_R()` function from the `EpiEstim` package estimates the time-varying reproduction number (Rt). We provide the `incidence_data`, specify the method as `"parametric_si"` (parametric with a known serial interval), and pass the serial interval distribution parameters using the `make_config` function.
+- The `plot` function creates three plots from the `estimate_R` class object.
+
+Please note that the code assumes the necessary packages are already installed. If they are not, you can install them using first the `install.packages("pak")` function and then the `pak::pak()` function for both packages in CRAN or GitHub before loading them with `library()`. 
+
+Additionally, make sure to adjust the serial interval distribution parameters according to the specific outbreak you are analyzing.
+
+## Related
+
+- [Explanation on the time-varying effective reproductive number](https://mrc-ide.github.io/EpiEstim/articles/full_EpiEstim_vignette.html)