From 08d79586fa8c41b4c28c34ed73d56399b80c4a42 Mon Sep 17 00:00:00 2001 From: Sebastian Funk Date: Fri, 10 May 2024 13:11:26 +0100 Subject: [PATCH] 1.5.0 release PR (#653) * update version in NEWS * fix typos * update vignettes * clean up imports * donttest in estimate_truncation example * fix links * update namespace * first version of cran comments * reorder news by priority * update package man page * add mantainer to cran comment * re-render README * Increment version number to 1.5.0 * remove relative links closing #659 * streamline vignettes and move figures Closes #660 * backtick escape stanfit object Closes #661. * write executable to temporary directory Closes #662. * reformat for linting * update cran comments * remove test this is causing warnings and is only a deprecation test so easiest to just remove * don't write into user directory * set seed in vignettes * update cran comments * dev version --- .github/workflows/R-CMD-check.yaml | 1 - .github/workflows/synthetic-validation.yaml | 2 - DESCRIPTION | 6 +- NAMESPACE | 1 - NEWS.md | 6 +- R/checks.R | 2 +- R/data.R | 2 +- R/estimate_truncation.R | 4 +- R/extract.R | 4 +- R/stan.R | 1 + README.Rmd | 18 +- README.md | 42 ++- cran-comments.md | 9 +- inst/WORDLIST | 36 ++- inst/dev/recover-synthetic/eval_rt.R | 54 ++-- man/EpiNow2-package.Rd | 4 +- man/check_reports_valid.Rd | 2 +- man/discretise.Rd | 2 +- man/estimate_truncation.Rd | 4 +- man/example_truncated.Rd | 2 +- man/extract_samples.Rd | 4 +- tests/testthat/test-estimate_infections.R | 8 - vignettes/EpiNow2-unnamed-chunk-11-1.png | Bin 0 -> 433586 bytes vignettes/EpiNow2-unnamed-chunk-15-1.png | Bin 0 -> 138321 bytes vignettes/EpiNow2.Rmd | 246 ++++++++--------- vignettes/EpiNow2.Rmd.orig | 10 + vignettes/epinow-epinow-1.png | Bin 0 -> 24760 bytes vignettes/epinow-regional_epinow-1.png | Bin 0 -> 34732 bytes .../epinow-regional_epinow_multiple-1.png | Bin 0 -> 30715 bytes vignettes/epinow.Rmd | 139 +++++----- 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vignettes/estimate_infections_workflow.Rmd | 6 +- .../estimate_infections_workflow.Rmd.orig | 11 +- vignettes/figure/bp-1.png | Bin 66069 -> 0 bytes vignettes/figure/data-1.png | Bin 11336 -> 0 bytes vignettes/figure/default-1.png | Bin 76529 -> 0 bytes vignettes/figure/fixed-1.png | Bin 62004 -> 0 bytes vignettes/figure/gp_projection-1.png | Bin 75922 -> 0 bytes vignettes/figure/lower_accuracy-1.png | Bin 75818 -> 0 bytes vignettes/figure/no_delays-1.png | Bin 71297 -> 0 bytes vignettes/figure/nonparametric-1.png | Bin 70105 -> 0 bytes vignettes/figure/regional_epinow-1.png | Bin 58371 -> 0 bytes .../figure/regional_epinow_multiple-1.png | Bin 52374 -> 0 bytes vignettes/figure/results-1.png | Bin 85776 -> 0 bytes vignettes/figure/susceptible_depletion-1.png | Bin 76524 -> 0 bytes vignettes/figure/truncation-1.png | Bin 76466 -> 0 bytes vignettes/figure/unnamed-chunk-10-1.png | Bin 974063 -> 0 bytes vignettes/figure/unnamed-chunk-14-1.png | Bin 387195 -> 0 bytes vignettes/figure/weekly_rw-1.png | Bin 75060 -> 0 bytes 63 files changed, 444 insertions(+), 457 deletions(-) create mode 100644 vignettes/EpiNow2-unnamed-chunk-11-1.png create mode 100644 vignettes/EpiNow2-unnamed-chunk-15-1.png create mode 100644 vignettes/epinow-epinow-1.png create mode 100644 vignettes/epinow-regional_epinow-1.png create mode 100644 vignettes/epinow-regional_epinow_multiple-1.png create mode 100644 vignettes/estimate_infections_options-bp-1.png create mode 100644 vignettes/estimate_infections_options-data-1.png create mode 100644 vignettes/estimate_infections_options-default-1.png create mode 100644 vignettes/estimate_infections_options-fixed-1.png create mode 100644 vignettes/estimate_infections_options-gp_projection-1.png create mode 100644 vignettes/estimate_infections_options-lower_accuracy-1.png create mode 100644 vignettes/estimate_infections_options-no_delays-1.png create mode 100644 vignettes/estimate_infections_options-nonparametric-1.png create mode 100644 vignettes/estimate_infections_options-susceptible_depletion-1.png create mode 100644 vignettes/estimate_infections_options-truncation-1.png create mode 100644 vignettes/estimate_infections_options-weekly_rw-1.png create mode 100644 vignettes/estimate_infections_workflow-results-1.png delete mode 100644 vignettes/figure/bp-1.png delete mode 100644 vignettes/figure/data-1.png delete mode 100644 vignettes/figure/default-1.png delete mode 100644 vignettes/figure/fixed-1.png delete mode 100644 vignettes/figure/gp_projection-1.png delete mode 100644 vignettes/figure/lower_accuracy-1.png delete mode 100644 vignettes/figure/no_delays-1.png delete mode 100644 vignettes/figure/nonparametric-1.png delete mode 100644 vignettes/figure/regional_epinow-1.png delete mode 100644 vignettes/figure/regional_epinow_multiple-1.png delete mode 100644 vignettes/figure/results-1.png delete mode 100644 vignettes/figure/susceptible_depletion-1.png delete mode 100644 vignettes/figure/truncation-1.png delete mode 100644 vignettes/figure/unnamed-chunk-10-1.png delete mode 100644 vignettes/figure/unnamed-chunk-14-1.png delete mode 100644 vignettes/figure/weekly_rw-1.png diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index a00e21f94..e1012308a 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -60,7 +60,6 @@ jobs: with: dependencies: NA extra-packages: | - dplyr rmarkdown rcmdcheck cmdstanr diff --git a/.github/workflows/synthetic-validation.yaml b/.github/workflows/synthetic-validation.yaml index d505accaf..b42f5c77f 100644 --- a/.github/workflows/synthetic-validation.yaml +++ b/.github/workflows/synthetic-validation.yaml @@ -28,8 +28,6 @@ jobs: install-pandoc: false extra-packages: | here - dplyr - tidyr scoringutils loo matrixStats diff --git a/DESCRIPTION b/DESCRIPTION index a5393d999..bbef6795c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Type: Package Package: EpiNow2 Title: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters -Version: 1.4.9000 +Version: 1.5.0.9000 Authors@R: c(person(given = "Sam", family = "Abbott", @@ -125,16 +125,12 @@ Imports: Suggests: cmdstanr, covr, - dplyr, here, - kableExtra, knitr, - magrittr, precommit, rmarkdown, spelling, testthat, - tidyr, usethis, withr LinkingTo: diff --git a/NAMESPACE b/NAMESPACE index 8c3885e01..b477eeea6 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -58,7 +58,6 @@ export(get_generation_time) export(get_incubation_period) export(get_parameters) export(get_pmf) -export(get_raw_result) export(get_regional_results) export(gp_opts) export(growth_to_R) diff --git a/NEWS.md b/NEWS.md index af6359abd..3c2b3e859 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,12 +1,14 @@ -# EpiNow2 1.4.9000 +# EpiNow2 (development version) + +# EpiNow2 1.5.0 This release comes with a change of maintainer, from @seabbs to @sbfnk. This is to reflect who will handle the upcoming CRAN submission, but is not expected to lead to a change in workflows. ## Major changes -* Delay discretisation is now based on a two-day censoring window (with uniform probability in between), based on recommendations in [Park et al, medRxiv, 2024](https://doi.org/10.1101/2024.01.12.24301247). By @sbfnk in #518 and reviewed by @jamesmbaazam. * The interface to generating delay distributions has been completely overhauled. Instead of calling `dist_spec()` users now specify distributions using functions that represent the available distributions, i.e. `LogNormal()`, `Gamma()` and `Fixed()`. See `?EpiNow2::Distributions`. Uncertainty is specified using calls of the same nature, to `Normal()`. More information on the underlying design can be found in `inst/dev/design_dist.md` By @sbfnk in #504 and reviewed by @seabbs. +* Delay discretisation is now based on a two-day censoring window (with uniform probability in between), based on recommendations in [Park et al, medRxiv, 2024](https://doi.org/10.1101/2024.01.12.24301247). By @sbfnk in #518 and reviewed by @jamesmbaazam. ## Deprecations diff --git a/R/checks.R b/R/checks.R index 01dfb7db8..6d1fe3a06 100644 --- a/R/checks.R +++ b/R/checks.R @@ -3,7 +3,7 @@ #' @description `r lifecycle::badge("stable")` #' `check_reports_valid()` checks that the supplied data is a ``, #' and that it has the right column names and types. In particular, it checks -#' that the date column is in date format and does not contain NA's, and that +#' that the date column is in date format and does not contain NAs, and that #' the other columns are numeric. #' #' @param data A data frame with either: diff --git a/R/data.R b/R/data.R index 610c5d764..9d3fbad8d 100644 --- a/R/data.R +++ b/R/data.R @@ -52,7 +52,7 @@ #' An example dataset of observed cases with truncation applied. #' This data is generated internally for use in the example of #' `estimate_truncation()`. For details on how the data is generated, see -#' #nolint +#' #nolint #' @format A list of `data.table`s containing cases reported on each date until #' a point of truncation. #' Each element of the list is a `data.table` with the following columns: diff --git a/R/estimate_truncation.R b/R/estimate_truncation.R index d628de996..55fd8271c 100644 --- a/R/estimate_truncation.R +++ b/R/estimate_truncation.R @@ -4,7 +4,7 @@ #' Estimates a truncation distribution from multiple snapshots of the same #' data source over time. This distribution can then be used passed to the #' `truncation` argument in [regional_epinow()], [epinow()], and -#' [estimate_infections()] to adjust for truncated data and propagage the +#' [estimate_infections()] to adjust for truncated data and propagate the #' uncertainty associated with data truncation into the estimates. #' #' See [here](https://gist.github.com/seabbs/176b0c7f83eab1a7192a25b28bbd116a) @@ -79,6 +79,7 @@ #' @importFrom checkmate assert_character assert_numeric assert_class #' assert_logical #' @examples +#' \donttest{ #' # set number of cores to use #' old_opts <- options() #' options(mc.cores = ifelse(interactive(), 4, 1)) @@ -109,6 +110,7 @@ #' ) #' plot(out) #' options(old_opts) +#' } estimate_truncation <- function(data, max_truncation, trunc_max = 10, trunc_dist = "lognormal", truncation = trunc_opts( diff --git a/R/extract.R b/R/extract.R index dc6e0d44d..331c100cf 100644 --- a/R/extract.R +++ b/R/extract.R @@ -55,8 +55,8 @@ extract_static_parameter <- function(param, samples) { #' Extract all samples from a stan fit #' -#' If the `object` argument is a object, it simply returns the result -#' of [rstan::extract()]. If it is a `` it returns samples +#' If the `object` argument is a `` object, it simply returns the +#' result of [rstan::extract()]. If it is a `` it returns samples #' in the same format as [rstan::extract()] does for `` objects. #' @param stan_fit A `` or `` object as returned by #' [fit_model()]. diff --git a/R/stan.R b/R/stan.R index e7ff91073..63ad24495 100644 --- a/R/stan.R +++ b/R/stan.R @@ -39,6 +39,7 @@ epinow2_cmdstan_model <- function(model = "estimate_infections", model <- monitor(cmdstanr::cmdstan_model( model_file, include_paths = dir, + dir = tempdir(), ... )) return(model) diff --git a/README.Rmd b/README.Rmd index 1908120bd..df21f97eb 100644 --- a/README.Rmd +++ b/README.Rmd @@ -35,11 +35,11 @@ Uncertainty is propagated from all inputs into the final parameter estimates, he `{EpiNow2}` provides three models: -* [`estimate_infections()`](reference/estimate_infections.html): Reconstruct cases by date of infection from reported cases. +* `estimate_infections()`: Reconstruct cases by date of infection from reported cases. -* [`estimate_secondary()`](reference/estimate_secondary.html): Estimate the relationship between primary and secondary observations, for example, deaths (secondary) based on hospital admissions (primary), or bed occupancy (secondary) based on hospital admissions (primary). +* `estimate_secondary()`: Estimate the relationship between primary and secondary observations, for example, deaths (secondary) based on hospital admissions (primary), or bed occupancy (secondary) based on hospital admissions (primary). -* [`estimate_truncation()`](reference/estimate_truncation.html): Estimate a truncation distribution from multiple snapshots of the same data source over time. For more flexibility, check out the [`{epinowcast}`](https://package.epinowcast.org/) package. +* `estimate_truncation()`: Estimate a truncation distribution from multiple snapshots of the same data source over time. For more flexibility, check out the [`{epinowcast}`](https://package.epinowcast.org/) package. The default model in `estimate_infections()` uses a non-stationary Gaussian process to estimate the time-varying reproduction number and infer infections. Other options, which generally reduce runtimes at the cost of the granularity of estimates or real-time performance, include: @@ -52,17 +52,17 @@ The default model in `estimate_infections()` uses a non-stationary Gaussian proc * A deconvolution/back-calculation method for inferring infections, followed with calculating the time-varying reproduction number. * Adjustment for the remaining susceptible population beyond the forecast horizon. -The documentation for [`estimate_infections`](reference/estimate_infections.html) provides examples of the implementation of the different options available. +The documentation for `estimate_infections` provides examples of the implementation of the different options available. `{EpiNow2}` is designed to be used via a single function call to two functions: -* [`epinow()`](reference/epinow.html): Estimate Rt and cases by date of infection and forecast these infections into the future. +* `epinow()`: Estimate Rt and cases by date of infection and forecast these infections into the future. -* [`regional_epinow()`](reference/regional_epinow.html): Efficiently run `epinow()` across multiple regions in an efficient manner. +* `regional_epinow()`: Efficiently run `epinow()` across multiple regions in an efficient manner. -These two functions call [`estimate_infections()`](reference/estimate_infections.html), which works to reconstruct cases by date of infection from reported cases. +These two functions call `estimate_infections()`, which works to reconstruct cases by date of infection from reported cases. -For more details on using each function see the [function documentation](reference/index.html). +For more details on using each function corresponding function documentation. @@ -117,7 +117,7 @@ discusses how to summarise and visualise the results after running the models. The package has two websites: one for [the stable release version on CRAN](https://epiforecasts.io/EpiNow2/), and -another for [the version in development](https://epiforecasts.io/EpiNow2/dev). +another for [the version in development](https://epiforecasts.io/EpiNow2/dev/). These two provide various resources for learning about the package, including the function reference, details about each model (model definition), workflows for each model (usage), and case studies or literature of applications of diff --git a/README.md b/README.md index 1c8baed9e..160208c9a 100644 --- a/README.md +++ b/README.md @@ -59,17 +59,17 @@ Models provided `{EpiNow2}` provides three models: -- [`estimate_infections()`](reference/estimate_infections.html): - Reconstruct cases by date of infection from reported cases. +- `estimate_infections()`: Reconstruct cases by date of infection from + reported cases. -- [`estimate_secondary()`](reference/estimate_secondary.html): Estimate - the relationship between primary and secondary observations, for - example, deaths (secondary) based on hospital admissions (primary), or - bed occupancy (secondary) based on hospital admissions (primary). +- `estimate_secondary()`: Estimate the relationship between primary and + secondary observations, for example, deaths (secondary) based on + hospital admissions (primary), or bed occupancy (secondary) based on + hospital admissions (primary). -- [`estimate_truncation()`](reference/estimate_truncation.html): - Estimate a truncation distribution from multiple snapshots of the same - data source over time. For more flexibility, check out the +- `estimate_truncation()`: Estimate a truncation distribution from + multiple snapshots of the same data source over time. For more + flexibility, check out the [`{epinowcast}`](https://package.epinowcast.org/) package. The default model in `estimate_infections()` uses a non-stationary @@ -90,25 +90,23 @@ cost of the granularity of estimates or real-time performance, include: - Adjustment for the remaining susceptible population beyond the forecast horizon. -The documentation for -[`estimate_infections`](reference/estimate_infections.html) provides -examples of the implementation of the different options available. +The documentation for `estimate_infections` provides examples of the +implementation of the different options available. `{EpiNow2}` is designed to be used via a single function call to two functions: -- [`epinow()`](reference/epinow.html): Estimate Rt and cases by date of - infection and forecast these infections into the future. +- `epinow()`: Estimate Rt and cases by date of infection and forecast + these infections into the future. -- [`regional_epinow()`](reference/regional_epinow.html): Efficiently run - `epinow()` across multiple regions in an efficient manner. +- `regional_epinow()`: Efficiently run `epinow()` across multiple + regions in an efficient manner. -These two functions call -[`estimate_infections()`](reference/estimate_infections.html), which -works to reconstruct cases by date of infection from reported cases. +These two functions call `estimate_infections()`, which works to +reconstruct cases by date of infection from reported cases. -For more details on using each function see the [function -documentation](reference/index.html). +For more details on using each function corresponding function +documentation. @@ -178,7 +176,7 @@ Package website The package has two websites: one for [the stable release version on CRAN](https://epiforecasts.io/EpiNow2/), and another for [the version in -development](https://epiforecasts.io/EpiNow2/dev). These two provide +development](https://epiforecasts.io/EpiNow2/dev/). These two provide various resources for learning about the package, including the function reference, details about each model (model definition), workflows for each model (usage), and case studies or literature of applications of diff --git a/cran-comments.md b/cran-comments.md index e3632a4e9..555be6f49 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,4 +1,11 @@ ## R CMD check results -0 errors ✔ | 0 warnings ✔ | 0 notes ✔ +This issue is submitted by a new maintainer as mentioned in a NOTE. +There is also a new NOTE because an additional repository has been specified in +order to suggest cmdstan. This is the same approach used in brms: +https://cran.r-project.org/web/packages/brms/index.html + +# Resubmission comments + +Size of the tar.gz has been reduced, locally confirmed as 4.7MB. diff --git a/inst/WORDLIST b/inst/WORDLIST index e729c5f35..ae8c83a86 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -4,17 +4,20 @@ CMD CMF CMMID COVID +CSTE Centers CoV Cori Covid Dirichlet +Discretise Dockerfile ECDC +EpiEstim EpiNow Epinow -Gostic Gruson +HHS Habakuk Hain HalfNormal @@ -40,56 +43,66 @@ Nowcasts ORCID PLoS PMF -PRs Poisson Quilty Riutort -Rmarkdown Rstan Rtools Runtimes SACEMA SARS Sanjat +Susswein +VB Wellcome +ast backcalc backcalculation +bp centered +cmdstanr codecov com csv dd defato +detectCores +discretisation +discretise discretised dist div doi dt +dynamicaltruncation epidemia epiforecasts epinow +epinowcast +epiparameter etc filepath frac github gp intial +laplace ldots lengthscale leq lineranges linters -logmean -lognorm -logsd +lintr lt matern mathcal mathrm mc +medRxiv metacran negbin nolint +nonmechanistic nowcast nowcasting nowcasts @@ -99,15 +112,15 @@ pak parallisation parameterisations parameterised +params +pkgdown +pmf pngs poisson -progressr -rds realland -reprex -roxygen rstan runtimes +rw sd se sim @@ -115,9 +128,8 @@ st stan stanfit testland -testthat +th tibbles -tidyverse timelimit underreporting varphi diff --git a/inst/dev/recover-synthetic/eval_rt.R b/inst/dev/recover-synthetic/eval_rt.R index 20ff6215c..819e7708a 100644 --- a/inst/dev/recover-synthetic/eval_rt.R +++ b/inst/dev/recover-synthetic/eval_rt.R @@ -1,25 +1,24 @@ ## to be run following rt.R -library(dplyr) -library(loo) -library(tidyr) +library(data.table) library(scoringutils) library(ggplot2) +library(rstan) source(here::here("inst", "dev", "recover-synthetic", "plot.R")) synthetic <- readRDS("synthetic.rds") looic <- lapply(synthetic$models, function(x) { est <- loo(x$fit, save_psis = TRUE)$estimates - return(as_tibble(est, rownames = "metric")) + dt <- data.table(est, keep.rownames = TRUE) + setnames(dt, "rn", "metric") + return(dt) }) names(looic) <- synthetic$model_names -dl <- bind_rows(looic, .id = "model") %>% - filter(metric == "looic") %>% - mutate( - model = sub("^(.*)_([^_]+)$", "\\1|\\2", model) - ) %>% - separate(model, c("model", "method"), sep = "\\|") +dl <- rbindlist(looic, idcol = "model") +dl <- dl[metric == "looic"] +dl[, model := sub("^(.*)_([^_]+)$", "\\1|\\2", model)] +dl[, c("model", "moethod") := tstrsplit(model, "\\|")] R_samples <- lapply(synthetic$models, function(x) { if ("R[1]" %in% names(x$fit)) { @@ -44,10 +43,10 @@ names(rcrps) <- synthetic$model_names icrps <- lapply(inf_samples, calc_crps, truth = synthetic$truth$sim_inf) names(icrps) <- synthetic$model_names -rdf <- bind_rows(rcrps) %>% - mutate(metric = "CRPS") %>% - mutate(time = 1:n()) %>% - pivot_longer(names_to = "model", c(-time, -metric)) +rdf <- as.data.table(rcrps) +rdf[, metric := "CRPS"] +rdf[, time := 1:.N] +rdf <- melt(rdf, id.vars = c("metric", "time"), variable.name = "model") p <- ggplot(rdf, aes(x = time, y = value, colour = model)) + geom_line() + @@ -58,17 +57,14 @@ p <- ggplot(rdf, aes(x = time, y = value, colour = model)) + ggtitle("Reconstructing R") save_ggplot(p, "CRPS", "rt") -rdf %>% - select(-time) %>% - group_by(metric, model) %>% - summarise_all(mean) %>% - pivot_wider(names_from = "metric") %>% - arrange(CRPS) +rmeans <- rdf[, list(mean_crps = mean(value)), by = c("metric", "model")] +setorder(rmeans, mean_crps) +rmeans -idf <- bind_rows(icrps) %>% - mutate(metric = "CRPS") %>% - mutate(time = 1:n()) %>% - pivot_longer(names_to = "model", c(-time, -metric)) +idf <- as.data.table(icrps) +idf[, metric := "CRPS"] +idf[, time := 1:.N] +idf <- melt(idf, id.vars = c("metric", "time"), variable.name = "model") p <- ggplot(idf, aes(x = time, y = value, colour = model)) + geom_line() + @@ -79,10 +75,6 @@ p <- ggplot(idf, aes(x = time, y = value, colour = model)) + ggtitle("Reconstructing infections") save_ggplot(p, "CRPS", "inf") -idf %>% - select(-time) %>% - group_by(metric, model) %>% - summarise_all(mean) %>% - pivot_wider(names_from = "metric") %>% - arrange(CRPS) - +imeans <- idf[, list(mean_crps = mean(value)), by = c("metric", "model")] +setorder(imeans, mean_crps) +imeans diff --git a/man/EpiNow2-package.Rd b/man/EpiNow2-package.Rd index d54cc974f..3b1d4e286 100644 --- a/man/EpiNow2-package.Rd +++ b/man/EpiNow2-package.Rd @@ -19,10 +19,11 @@ Useful links: } \author{ -\strong{Maintainer}: Sam Abbott \email{sam.abbott@lshtm.ac.uk} (\href{https://orcid.org/0000-0001-8057-8037}{ORCID}) +\strong{Maintainer}: Sebastian Funk \email{sebastian.funk@lshtm.ac.uk} (\href{https://orcid.org/0000-0002-2842-3406}{ORCID}) Authors: \itemize{ + \item Sam Abbott \email{sam.abbott@lshtm.ac.uk} (\href{https://orcid.org/0000-0001-8057-8037}{ORCID}) \item Joel Hellewell \email{joel.hellewell@lshtm.ac.uk} (\href{https://orcid.org/0000-0003-2683-0849}{ORCID}) \item Katharine Sherratt \email{katharine.sherratt@lshtm.ac.uk} \item Katelyn Gostic \email{kgostic@uchicago.edu} @@ -31,7 +32,6 @@ Authors: \item Michael DeWitt \email{me.dewitt.jr@gmail.com} (\href{https://orcid.org/0000-0001-8940-1967}{ORCID}) \item James M. Azam \email{james.azam@lshtm.ac.uk} (\href{https://orcid.org/0000-0001-5782-7330}{ORCID}) \item EpiForecasts - \item Sebastian Funk \email{sebastian.funk@lshtm.ac.uk} (\href{https://orcid.org/0000-0002-2842-3406}{ORCID}) } Other contributors: diff --git a/man/check_reports_valid.Rd b/man/check_reports_valid.Rd index 665019568..de18dc20d 100644 --- a/man/check_reports_valid.Rd +++ b/man/check_reports_valid.Rd @@ -29,7 +29,7 @@ Called for its side effects. \ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#stable}{\figure{lifecycle-stable.svg}{options: alt='[Stable]'}}}{\strong{[Stable]}} \code{check_reports_valid()} checks that the supplied data is a \verb{}, and that it has the right column names and types. In particular, it checks -that the date column is in date format and does not contain NA's, and that +that the date column is in date format and does not contain NAs, and that the other columns are numeric. } \keyword{internal} diff --git a/man/discretise.Rd b/man/discretise.Rd index cbf2cbc45..dff1ddc2a 100644 --- a/man/discretise.Rd +++ b/man/discretise.Rd @@ -14,7 +14,7 @@ discretize(x, strict = TRUE) \item{strict}{Logical; If \code{TRUE} (default) an error will be thrown if a distribution cannot be discretised (e.g., because no finite maximum has been -specified or parameters are uncertain). If if \code{FALSE} then any distribution +specified or parameters are uncertain). If \code{FALSE} then any distribution that cannot be discretised will be returned as is.} } \value{ diff --git a/man/estimate_truncation.Rd b/man/estimate_truncation.Rd index 22b90a2ad..d0c5dc323 100644 --- a/man/estimate_truncation.Rd +++ b/man/estimate_truncation.Rd @@ -83,7 +83,7 @@ used for fitting (\code{data}) and the fit object (\code{fit}). Estimates a truncation distribution from multiple snapshots of the same data source over time. This distribution can then be used passed to the \code{truncation} argument in \code{\link[=regional_epinow]{regional_epinow()}}, \code{\link[=epinow]{epinow()}}, and -\code{\link[=estimate_infections]{estimate_infections()}} to adjust for truncated data and propagage the +\code{\link[=estimate_infections]{estimate_infections()}} to adjust for truncated data and propagate the uncertainty associated with data truncation into the estimates. See \href{https://gist.github.com/seabbs/176b0c7f83eab1a7192a25b28bbd116a}{here} @@ -116,6 +116,7 @@ This approach assumes that: } } \examples{ +\donttest{ # set number of cores to use old_opts <- options() options(mc.cores = ifelse(interactive(), 4, 1)) @@ -147,3 +148,4 @@ out <- epinow( plot(out) options(old_opts) } +} diff --git a/man/example_truncated.Rd b/man/example_truncated.Rd index b23032f4f..997b8a182 100644 --- a/man/example_truncated.Rd +++ b/man/example_truncated.Rd @@ -21,6 +21,6 @@ example_truncated An example dataset of observed cases with truncation applied. This data is generated internally for use in the example of \code{estimate_truncation()}. For details on how the data is generated, see -\url{https://github.com/epiforecasts/EpiNow2/blob/main/data-raw/estimate-truncation.R} #nolint +\url{https://github.com/epiforecasts/EpiNow2/blob/main/data-raw/truncated.R} #nolint } \keyword{datasets} diff --git a/man/extract_samples.Rd b/man/extract_samples.Rd index dc6f4653f..910e171e3 100644 --- a/man/extract_samples.Rd +++ b/man/extract_samples.Rd @@ -19,7 +19,7 @@ extract_samples(stan_fit, pars = NULL, include = TRUE) List of data.tables with samples } \description{ -If the \code{object} argument is a \if{html}{\out{}} object, it simply returns the result -of \code{\link[rstan:stanfit-method-extract]{rstan::extract()}}. If it is a \verb{} it returns samples +If the \code{object} argument is a \verb{} object, it simply returns the +result of \code{\link[rstan:stanfit-method-extract]{rstan::extract()}}. If it is a \verb{} it returns samples in the same format as \code{\link[rstan:stanfit-method-extract]{rstan::extract()}} does for \verb{} objects. } diff --git a/tests/testthat/test-estimate_infections.R b/tests/testthat/test-estimate_infections.R index c3ced462f..64dafd376 100644 --- a/tests/testthat/test-estimate_infections.R +++ b/tests/testthat/test-estimate_infections.R @@ -143,11 +143,3 @@ test_that("estimate_infections works as expected with failing chains", { ) )) }) - -test_that("deprecated arguments are recognised", { - expect_deprecated( - estimate_infections( - reported_cases = reported_cases, - generation_time = generation_time_opts(Fixed(1)) - )) -}) diff --git a/vignettes/EpiNow2-unnamed-chunk-11-1.png b/vignettes/EpiNow2-unnamed-chunk-11-1.png new file mode 100644 index 0000000000000000000000000000000000000000..d56747ba4a520f4c08672781f8b6a776303b0ca4 GIT binary patch literal 433586 zcmeFZbx_n_6gRx07@(k1Qo;|A7LZtEX{A%TLs}Z?Mv+z!5RjIZ7LZP*+oii3q&wfc 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Ol->*jo!Dthef}TX;?m^+ literal 0 HcmV?d00001 diff --git a/vignettes/EpiNow2.Rmd b/vignettes/EpiNow2.Rmd index 84a12d52d..2fa8edbc3 100644 --- a/vignettes/EpiNow2.Rmd +++ b/vignettes/EpiNow2.Rmd @@ -12,6 +12,7 @@ vignette: > %\VignetteEncoding{UTF-8} --- + ## Quick start In the following section we give an overview of the simple use case for `epinow()` and `regional_epinow()`. @@ -45,14 +46,11 @@ To demonstrate, we choose a lognormal distribution with mean 2, standard deviati ```r reporting_delay <- LogNormal(mean = 2, sd = 1, max = 10) reporting_delay -``` - -``` -## - lognormal distribution (max: 10): -## meanlog: -## 0.58 -## sdlog: -## 0.47 +#> - lognormal distribution (max: 10): +#> meanlog: +#> 0.58 +#> sdlog: +#> 0.47 ``` For the rest of this vignette, we will use inbuilt example literature estimates for the incubation period and generation time of Covid-19 (see [here](https://github.com/epiforecasts/EpiNow2/tree/main/data-raw) for the code that generates these estimates). *These distributions are unlikely to be applicable for your use case. We strongly recommend investigating what might be the best distributions to use in any given use case.* @@ -60,42 +58,33 @@ For the rest of this vignette, we will use inbuilt example literature estimates ```r example_generation_time -``` - -``` -## - gamma distribution (max: 14): -## shape: -## - normal distribution: -## mean: -## 1.4 -## sd: -## 0.48 -## rate: -## - normal distribution: -## mean: -## 0.38 -## sd: -## 0.25 -``` - -```r +#> - gamma distribution (max: 14): +#> shape: +#> - normal distribution: +#> mean: +#> 1.4 +#> sd: +#> 0.48 +#> rate: +#> - normal distribution: +#> mean: +#> 0.38 +#> sd: +#> 0.25 example_incubation_period -``` - -``` -## - lognormal distribution (max: 14): -## meanlog: -## - normal distribution: -## mean: -## 1.6 -## sd: -## 0.064 -## sdlog: -## - normal distribution: -## mean: -## 0.42 -## sd: -## 0.069 +#> - lognormal distribution (max: 14): +#> meanlog: +#> - normal distribution: +#> mean: +#> 1.6 +#> sd: +#> 0.064 +#> sdlog: +#> - normal distribution: +#> mean: +#> 0.42 +#> sd: +#> 0.069 ``` Now, to the functions. @@ -110,17 +99,14 @@ Load example case data from `{EpiNow2}`. ```r reported_cases <- example_confirmed[1:60] head(reported_cases) -``` - -``` -## date confirm -## -## 1: 2020-02-22 14 -## 2: 2020-02-23 62 -## 3: 2020-02-24 53 -## 4: 2020-02-25 97 -## 5: 2020-02-26 93 -## 6: 2020-02-27 78 +#> date confirm +#> +#> 1: 2020-02-22 14 +#> 2: 2020-02-23 62 +#> 3: 2020-02-24 53 +#> 4: 2020-02-25 97 +#> 5: 2020-02-26 93 +#> 6: 2020-02-27 78 ``` Estimate cases by date of infection, the time-varying reproduction number, the rate of growth, and forecast these estimates into the future by 7 days. Summarise the posterior and return a summary table and plots for reporting purposes. If a `target_folder` is supplied results can be internally saved (with the option to also turn off explicit returning of results). Here we use the default model parameterisation that prioritises real-time performance over run-time or other considerations. For other formulations see the documentation for `estimate_infections()`. @@ -135,24 +121,10 @@ estimates <- epinow( stan = stan_opts(cores = 4, control = list(adapt_delta = 0.99)), verbose = interactive() ) -``` - -``` -## DEBUG [2024-03-06 10:56:48] epinow: Running in exact mode for 2000 samples (across 4 chains each with a warm up of 250 iterations each) and 81 time steps of which 7 are a forecast -## WARN [2024-04-23 16:58:18] epinow: There were 1 divergent transitions after warmup. See -## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup -## to find out why this is a problem and how to eliminate them. - -## WARN [2024-04-23 16:58:18] epinow: Examine the pairs() plot to diagnose sampling problems -## - -``` - -```r names(estimates) -``` - -``` -## [1] "estimates" "estimated_reported_cases" "summary" -## [4] "plots" "timing" +#> [1] "estimates" "estimated_reported_cases" +#> [3] "summary" "plots" +#> [5] "timing" ``` Both summary measures and posterior samples are returned for all parameters in an easily explored format which can be accessed using `summary`. The default is to return a summary table of estimates for key parameters at the latest date partially supported by data. @@ -166,36 +138,35 @@ knitr::kable(summary(estimates)) |measure |estimate | |:--------------------------------|:-----------------------| -|New infections per day |2200 (951 -- 4666) | +|New infections per day |2199 (962 -- 4757) | |Expected change in daily reports |Likely decreasing | -|Effective reproduction no. |0.87 (0.57 -- 1.2) | -|Rate of growth |-0.035 (-0.16 -- 0.067) | -|Doubling/halving time (days) |-20 (10 -- -4.4) | +|Effective reproduction no. |0.88 (0.58 -- 1.2) | +|Rate of growth |-0.034 (-0.15 -- 0.076) | +|Doubling/halving time (days) |-20 (9.1 -- -4.6) | + + Summarised parameter estimates can also easily be returned, either filtered for a single parameter or for all parameters. ```r head(summary(estimates, type = "parameters", params = "R")) -``` - -``` -## date variable strat type median mean sd lower_90 lower_50 lower_20 upper_20 -## -## 1: 2020-02-22 R estimate 2.291017 2.295305 0.1622730 2.038582 2.185297 2.246205 2.333835 -## 2: 2020-02-23 R estimate 2.257591 2.260566 0.1377286 2.045927 2.162193 2.218197 2.296625 -## 3: 2020-02-24 R estimate 2.221472 2.223189 0.1196990 2.038071 2.137169 2.187203 2.250281 -## 4: 2020-02-25 R estimate 2.177615 2.183177 0.1076970 2.014396 2.106603 2.152258 2.206708 -## 5: 2020-02-26 R estimate 2.135769 2.140651 0.1005877 1.983849 2.069109 2.110227 2.160111 -## 6: 2020-02-27 R estimate 2.091666 2.095831 0.0968505 1.942662 2.027977 2.068433 2.115299 -## upper_50 upper_90 -## -## 1: 2.409435 2.570970 -## 2: 2.356789 2.490317 -## 3: 2.305457 2.422693 -## 4: 2.256158 2.361948 -## 5: 2.206701 2.312967 -## 6: 2.158071 2.260933 +#> date variable strat type median mean sd lower_90 +#> +#> 1: 2020-02-22 R estimate 2.288484 2.293707 0.1613209 2.044132 +#> 2: 2020-02-23 R estimate 2.254878 2.259642 0.1403568 2.040359 +#> 3: 2020-02-24 R estimate 2.217611 2.222709 0.1251234 2.026980 +#> 4: 2020-02-25 R estimate 2.174854 2.182984 0.1150322 2.002758 +#> 5: 2020-02-26 R estimate 2.132196 2.140643 0.1089983 1.974475 +#> 6: 2020-02-27 R estimate 2.088958 2.095945 0.1056741 1.933753 +#> lower_50 lower_20 upper_20 upper_50 upper_90 +#> +#> 1: 2.181696 2.246458 2.328439 2.398968 2.574407 +#> 2: 2.162459 2.217716 2.289158 2.349965 2.508728 +#> 3: 2.136181 2.184912 2.247679 2.303535 2.440930 +#> 4: 2.101307 2.148035 2.204825 2.256175 2.381675 +#> 5: 2.064946 2.108764 2.160005 2.207901 2.331636 +#> 6: 2.023783 2.062603 2.114298 2.159626 2.285821 ``` Reported cases are returned in a separate data frame in order to streamline the reporting of forecasts and for model evaluation. @@ -203,17 +174,22 @@ Reported cases are returned in a separate data frame in order to streamline the ```r head(summary(estimates, output = "estimated_reported_cases")) -``` - -``` -## date type median mean sd lower_90 lower_50 lower_20 upper_20 upper_50 upper_90 -## -## 1: 2020-02-22 gp_rt 77 77.9745 22.03483 46 61 71 82 92 116.00 -## 2: 2020-02-23 gp_rt 87 90.1460 25.20663 54 73 82 93 105 135.00 -## 3: 2020-02-24 gp_rt 87 89.2040 25.05848 53 72 81 93 104 135.05 -## 4: 2020-02-25 gp_rt 80 82.0715 22.13329 49 67 75 86 95 120.00 -## 5: 2020-02-26 gp_rt 79 82.0965 22.81924 50 66 74 85 96 123.00 -## 6: 2020-02-27 gp_rt 106 109.0665 30.32398 66 88 99 114 127 164.00 +#> date type median mean sd lower_90 lower_50 lower_20 +#> +#> 1: 2020-02-22 gp_rt 74.0 76.9430 21.47422 46 62 70.0 +#> 2: 2020-02-23 gp_rt 87.0 89.5820 24.71017 54 72 82.0 +#> 3: 2020-02-24 gp_rt 86.0 88.6670 24.76764 53 71 80.6 +#> 4: 2020-02-25 gp_rt 81.0 82.6625 22.56520 49 66 75.0 +#> 5: 2020-02-26 gp_rt 79.0 81.8240 23.31905 48 65 75.0 +#> 6: 2020-02-27 gp_rt 107.5 111.2375 30.27595 69 89 101.0 +#> upper_20 upper_50 upper_90 +#> +#> 1: 79 89 116.00 +#> 2: 92 104 135.00 +#> 3: 92 103 133.00 +#> 4: 87 96 121.00 +#> 5: 85 96 121.05 +#> 6: 115 129 166.00 ``` A range of plots are returned (with the single summary plot shown below). These plots can also be generated using the following `plot` method. @@ -223,7 +199,7 @@ A range of plots are returned (with the single summary plot shown below). These plot(estimates) ``` -![plot of chunk unnamed-chunk-10](figure/unnamed-chunk-10-1.png) +![plot of chunk unnamed-chunk-11](EpiNow2-unnamed-chunk-11-1.png) ### [regional_epinow()](https://epiforecasts.io/EpiNow2/reference/regional_epinow.html) @@ -240,17 +216,14 @@ reported_cases <- data.table::rbindlist(list( reported_cases[, region := "realland"] )) head(reported_cases) -``` - -``` -## date confirm region -## -## 1: 2020-02-22 14 testland -## 2: 2020-02-23 62 testland -## 3: 2020-02-24 53 testland -## 4: 2020-02-25 97 testland -## 5: 2020-02-26 93 testland -## 6: 2020-02-27 78 testland +#> date confirm region +#> +#> 1: 2020-02-22 14 testland +#> 2: 2020-02-23 62 testland +#> 3: 2020-02-24 53 testland +#> 4: 2020-02-25 97 testland +#> 5: 2020-02-26 93 testland +#> 6: 2020-02-27 78 testland ``` Calling `regional_epinow()` runs the `epinow()` on each region in turn (or in parallel depending on the settings used). Here we switch to using a weekly random walk rather than the full Gaussian process model giving us piecewise constant estimates by week. @@ -265,24 +238,19 @@ estimates <- regional_epinow( gp = NULL, stan = stan_opts(cores = 4, warmup = 250, samples = 1000) ) -``` - -``` -## INFO [2024-04-23 16:58:21] Producing following optional outputs: regions, summary, samples, plots, latest -## INFO [2024-04-23 16:58:21] Reporting estimates using data up to: 2020-04-21 -## INFO [2024-04-23 16:58:21] No target directory specified so returning output -## INFO [2024-04-23 16:58:21] Producing estimates for: testland, realland -## INFO [2024-04-23 16:58:21] Regions excluded: none -## DEBUG [2024-04-23 16:58:21] testland: Running in exact mode for 1000 samples (across 4 chains each with a warm up of 250 iterations each) and 81 time steps of which 7 are a forecast -## INFO [2024-04-23 16:58:33] Completed estimates for: testland -## DEBUG [2024-04-23 16:58:33] realland: Running in exact mode for 1000 samples (across 4 chains each with a warm up of 250 iterations each) and 81 time steps of which 7 are a forecast -## INFO [2024-04-23 16:58:44] Completed estimates for: realland -## INFO [2024-04-23 16:58:44] Completed regional estimates -## INFO [2024-04-23 16:58:44] Regions with estimates: 2 -## INFO [2024-04-23 16:58:44] Regions with runtime errors: 0 -## INFO [2024-04-23 16:58:44] Producing summary -## INFO [2024-04-23 16:58:44] No summary directory specified so returning summary output -## INFO [2024-04-23 16:58:44] No target directory specified so returning timings +#> INFO [2024-05-10 07:58:14] Producing following optional outputs: regions, summary, samples, plots, latest +#> INFO [2024-05-10 07:58:14] Reporting estimates using data up to: 2020-04-21 +#> INFO [2024-05-10 07:58:14] No target directory specified so returning output +#> INFO [2024-05-10 07:58:14] Producing estimates for: testland, realland +#> INFO [2024-05-10 07:58:14] Regions excluded: none +#> INFO [2024-05-10 07:58:29] Completed estimates for: testland +#> INFO [2024-05-10 07:58:41] Completed estimates for: realland +#> INFO [2024-05-10 07:58:41] Completed regional estimates +#> INFO [2024-05-10 07:58:41] Regions with estimates: 2 +#> INFO [2024-05-10 07:58:41] Regions with runtime errors: 0 +#> INFO [2024-05-10 07:58:41] Producing summary +#> INFO [2024-05-10 07:58:41] No summary directory specified so returning summary output +#> INFO [2024-05-10 07:58:41] No target directory specified so returning timings ``` Results from each region are stored in a `regional` list with across region summary measures and plots stored in a `summary` list. All results can be set to be internally saved by setting the `target_folder` and `summary_dir` arguments. Each region can be estimated in parallel using the `{future}` package (when in most scenarios `cores` should be set to 1). For routine use each MCMC chain can also be run in parallel (with `future` = TRUE) with a time out (`max_execution_time`) allowing for partial results to be returned if a subset of chains is running longer than expected. See the documentation for the `{future}` package for details on nested futures. @@ -298,8 +266,10 @@ knitr::kable(estimates$summary$summarised_results$table) |Region |New infections per day |Expected change in daily reports |Effective reproduction no. |Rate of growth |Doubling/halving time (days) | |:--------|:----------------------|:--------------------------------|:--------------------------|:-----------------------|:----------------------------| -|realland |2107 (1055 -- 4430) |Likely decreasing |0.86 (0.62 -- 1.2) |-0.038 (-0.11 -- 0.051) |-18 (14 -- -6.3) | -|testland |2121 (1035 -- 4452) |Likely decreasing |0.87 (0.61 -- 1.2) |-0.037 (-0.11 -- 0.05) |-19 (14 -- -6.2) | +|realland |2060 (1007 -- 4579) |Likely decreasing |0.86 (0.59 -- 1.2) |-0.039 (-0.12 -- 0.056) |-18 (12 -- -5.9) | +|testland |2136 (1050 -- 4356) |Likely decreasing |0.86 (0.62 -- 1.2) |-0.036 (-0.11 -- 0.048) |-19 (14 -- -6.4) | + + A range of plots are again returned (with the single summary plot shown below). @@ -308,4 +278,4 @@ A range of plots are again returned (with the single summary plot shown below). estimates$summary$summary_plot ``` -![plot of chunk unnamed-chunk-14](figure/unnamed-chunk-14-1.png) +![plot of chunk unnamed-chunk-15](EpiNow2-unnamed-chunk-15-1.png) diff --git a/vignettes/EpiNow2.Rmd.orig b/vignettes/EpiNow2.Rmd.orig index fb18d2f0e..615c4b5fa 100644 --- a/vignettes/EpiNow2.Rmd.orig +++ b/vignettes/EpiNow2.Rmd.orig @@ -12,6 +12,16 @@ vignette: > %\VignetteEncoding{UTF-8} --- +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + fig.width = 6.5, + fig.height = 6.5, + fig.path = "EpiNow2-" +) +set.seed(9876) +``` ## Quick start In the following section we give an overview of the simple use case for `epinow()` and `regional_epinow()`. diff --git a/vignettes/epinow-epinow-1.png b/vignettes/epinow-epinow-1.png new file mode 100644 index 0000000000000000000000000000000000000000..17e748c785053d533d42ebd742be366c419616d7 GIT binary patch literal 24760 zcmZs@1yoc~`z}6+gwle9v>>9?AR?Vg35bAncc*j>3Ib9JNDBx^cOx-$NlQr%$k5#k zHFwYV-QQjJ|6g|&42zjF=j_@0-ETb4^PbOd-^dZ&eQ*~7fe5+w@@b?r=iGq6%}xvonNXxypghNHS}HBo%kib6d2XL zh>1opNa#JOLmUW%Uh@BX-uKaB8cB76f954Vd)l3}EEqm^Pa4`Ss)7nQ2`?&q+W+V3 z<2XlYA$V-wOC*&(%>dcvMM>Ncxvm!r`e-1EvvY&(6B6#yR>IHhV?Tycei$eMO@!2Dy;;I)InJ)Emex_ zQF+zL9S^g}5={;n9T`<kwuTjlTsNa zCe$s6+HjUk#Od~QwkMbAtv)IU#0~O)KQH@XK?F6k2!S% zJp>|&*TIT zk-8hbUJ~s;kBp>99~LK2nsD(~UH+RfvNS(sa-EzvupNGOAf3|2+5fCN$&tRc*M((l z(m9)h_VR(_xQm&7M-;c?wq5;bSCl`Iz-B#f>5?p!z$yP+{*)?RJO7+^0)Mj7=s2sw zq%(oxHQz?;9_h0q>4>7KM~V!bM(IMf=f~v`=)ZaA4k95Tq5n*n5X$yd8`Is804_J#gu1cw72>7R)2KqRxGijiB{Co*pJ^!x@Tw_`ph<9KI9NkuN4 z+=9HysjJhXklFFj41cf@1t*M4vZ;J7;#)hJVefW&+-KNqcN;?ex5@j+WBYF=u%2gs z;>toSlIW^x=I1?|vMahKW?6Dh4_P6B$i()jQ?GT#wA|k2SYMyrbx!h?dSeJAj8GTO z9M<%eXU48UuO(lEn4Sz0=oOgYq-|*kJ!-FrJ~G>$mQ9f}#DW}D+KlD?`)As0M;5mf z=XX~7ed2}XY7|ZTSa?$=xYEdkcoaQ0i8bUaE1Po4PtW_N{C=@zDx#In34MQ%+d?zb zf+DbDjUV3Z3=qf_(V94-naj%mv!I7)|7x-W-t4BZ=r{FGSdhSB=Qr>NTsV7nCG@dX z(?qN4^Cg2&3lT_z&-KaF@OL51K6!Q(S2ipmycznFne$LOJG-&AEQsme|T%t0Sx%Pi$D9(pD0oHoGoC74Z8XbP|(#^!%h+{hQk-u$2VImFj;>; 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+#> +#> Attaching package: 'EpiNow2' +#> The following object is masked from 'package:stats': +#> +#> Gamma options(mc.cores = 4) reported_cases <- example_confirmed[1:60] reporting_delay <- LogNormal(mean = 2, sd = 1, max = 10) @@ -42,23 +47,24 @@ We can then run the `epinow()` function with the same arguments as `estimate_inf res <- epinow(reported_cases, generation_time = generation_time_opts(example_generation_time), delays = delay_opts(delay), - rt = rt_opts(prior = rt_prior), - target_folder = "results" + rt = rt_opts(prior = rt_prior) ) #> Logging threshold set at INFO for the EpiNow2 logger -#> Writing EpiNow2 logs to the console and: /var/folders/n9/h_419gjj2mg3d208nplgvbg40000gp/T//RtmpCLEtE0/regional-epinow/2020-04-21.log +#> Writing EpiNow2 logs to the console and: /tmp/RtmpJDlhZu/regional-epinow/2020-04-21.log #> Logging threshold set at INFO for the EpiNow2.epinow logger -#> Writing EpiNow2.epinow logs to the console and: /var/folders/n9/h_419gjj2mg3d208nplgvbg40000gp/T//RtmpCLEtE0/epinow/2020-04-21.log -#> WARN [2024-03-06 10:52:09] epinow: There were 9 divergent transitions after warmup. See +#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpJDlhZu/epinow/2020-04-21.log +#> WARN [2024-05-10 07:52:36] epinow: There were 11 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. - -#> WARN [2024-03-06 10:52:09] epinow: Examine the pairs() plot to diagnose sampling problems +#> WARN [2024-05-10 07:52:36] epinow: Examine the pairs() plot to diagnose sampling problems #> - res$plots$R -#> NULL ``` -The initial messages here indicate where log files can be found, and summarised results and plots are in the folder given by `target_folder` (here: `results/`). +![plot of chunk epinow](epinow-epinow-1.png) + +The initial messages here indicate where log files can be found. +If you want summarised results and plots to be written out where they can be accessed later you can use the `target_folder` argument. # Running the model simultaneously on multiple regions @@ -86,42 +92,47 @@ region_rt <- regional_epinow( delays = delay_opts(delay), rt = rt_opts(prior = rt_prior), ) -#> INFO [2024-03-06 10:52:13] Producing following optional outputs: regions, summary, samples, plots, latest +#> Warning: The `reported_cases` argument of `regional_epinow()` is deprecated as of EpiNow2 1.5.0. +#> ℹ Please use the `data` argument instead. +#> ℹ The argument will be removed completely in the next version. +#> This warning is displayed once every 8 hours. +#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated. +#> INFO [2024-05-10 07:52:38] Producing following optional outputs: regions, summary, samples, plots, latest #> Logging threshold set at INFO for the EpiNow2 logger -#> Writing EpiNow2 logs to the console and: /var/folders/n9/h_419gjj2mg3d208nplgvbg40000gp/T//RtmpCLEtE0/regional-epinow/2020-04-21.log +#> Writing EpiNow2 logs to the console and: /tmp/RtmpJDlhZu/regional-epinow/2020-04-21.log #> Logging threshold set at INFO for the EpiNow2.epinow logger -#> Writing EpiNow2.epinow logs to: /var/folders/n9/h_419gjj2mg3d208nplgvbg40000gp/T//RtmpCLEtE0/epinow/2020-04-21.log -#> INFO [2024-03-06 10:52:13] Reporting estimates using data up to: 2020-04-21 -#> INFO [2024-03-06 10:52:13] No target directory specified so returning output -#> INFO [2024-03-06 10:52:13] Producing estimates for: testland, realland -#> INFO [2024-03-06 10:52:13] Regions excluded: none -#> INFO [2024-03-06 10:52:48] Completed estimates for: testland -#> INFO [2024-03-06 10:53:17] Completed estimates for: realland -#> INFO [2024-03-06 10:53:17] Completed regional estimates -#> INFO [2024-03-06 10:53:17] Regions with estimates: 2 -#> INFO [2024-03-06 10:53:17] Regions with runtime errors: 0 -#> INFO [2024-03-06 10:53:17] Producing summary -#> INFO [2024-03-06 10:53:17] No summary directory specified so returning summary output -#> INFO [2024-03-06 10:53:17] No target directory specified so returning timings +#> Writing EpiNow2.epinow logs to: /tmp/RtmpJDlhZu/epinow/2020-04-21.log +#> INFO [2024-05-10 07:52:38] Reporting estimates using data up to: 2020-04-21 +#> INFO [2024-05-10 07:52:38] No target directory specified so returning output +#> INFO [2024-05-10 07:52:38] Producing estimates for: testland, realland +#> INFO [2024-05-10 07:52:38] Regions excluded: none +#> INFO [2024-05-10 07:53:40] Completed estimates for: testland +#> INFO [2024-05-10 07:54:29] Completed estimates for: realland +#> INFO [2024-05-10 07:54:29] Completed regional estimates +#> INFO [2024-05-10 07:54:29] Regions with estimates: 2 +#> INFO [2024-05-10 07:54:29] Regions with runtime errors: 0 +#> INFO [2024-05-10 07:54:29] Producing summary +#> INFO [2024-05-10 07:54:29] No summary directory specified so returning summary output +#> INFO [2024-05-10 07:54:29] No target directory specified so returning timings ## summary region_rt$summary$summarised_results$table -#> Region New confirmed cases by infection date -#> -#> 1: realland 2254 (1153 -- 4243) -#> 2: testland 2261 (1059 -- 4347) -#> Expected change in daily cases Effective reproduction no. -#> -#> 1: Likely decreasing 0.87 (0.6 -- 1.2) -#> 2: Likely decreasing 0.88 (0.6 -- 1.2) -#> Rate of growth Doubling/halving time (days) -#> -#> 1: -0.028 (-0.1 -- 0.034) -24 (20 -- -6.9) -#> 2: -0.027 (-0.1 -- 0.037) -25 (19 -- -6.8) +#> Region New infections per day Expected change in daily reports +#> +#> 1: realland 2211 (921 -- 4610) Likely decreasing +#> 2: testland 2238 (980 -- 4646) Likely decreasing +#> Effective reproduction no. Rate of growth +#> +#> 1: 0.87 (0.57 -- 1.2) -0.036 (-0.16 -- 0.076) +#> 2: 0.88 (0.58 -- 1.2) -0.034 (-0.16 -- 0.076) +#> Doubling/halving time (days) +#> +#> 1: -19 (9.2 -- -4.3) +#> 2: -20 (9.1 -- -4.4) ## plot region_rt$summary$plots$R ``` -![plot of chunk regional_epinow](figure/regional_epinow-1.png) +![plot of chunk regional_epinow](epinow-regional_epinow-1.png) If instead, we wanted to use the Gaussian Process for `testland` and a weekly random walk for `realland` we could specify these separately using the `opts_list()` function from the package and `modifyList()` from `R`. @@ -136,39 +147,39 @@ region_separate_rt <- regional_epinow( delays = delay_opts(delay), rt = rt, gp = gp, ) -#> INFO [2024-03-06 10:53:17] Producing following optional outputs: regions, summary, samples, plots, latest +#> INFO [2024-05-10 07:54:30] Producing following optional outputs: regions, summary, samples, plots, latest #> Logging threshold set at INFO for the EpiNow2 logger -#> Writing EpiNow2 logs to the console and: /var/folders/n9/h_419gjj2mg3d208nplgvbg40000gp/T//RtmpCLEtE0/regional-epinow/2020-04-21.log +#> Writing EpiNow2 logs to the console and: /tmp/RtmpJDlhZu/regional-epinow/2020-04-21.log #> Logging threshold set at INFO for the EpiNow2.epinow logger -#> Writing EpiNow2.epinow logs to: /var/folders/n9/h_419gjj2mg3d208nplgvbg40000gp/T//RtmpCLEtE0/epinow/2020-04-21.log -#> INFO [2024-03-06 10:53:17] Reporting estimates using data up to: 2020-04-21 -#> INFO [2024-03-06 10:53:17] No target directory specified so returning output -#> INFO [2024-03-06 10:53:17] Producing estimates for: testland, realland -#> INFO [2024-03-06 10:53:17] Regions excluded: none -#> INFO [2024-03-06 10:53:50] Completed estimates for: testland -#> INFO [2024-03-06 10:53:59] Completed estimates for: realland -#> INFO [2024-03-06 10:53:59] Completed regional estimates -#> INFO [2024-03-06 10:53:59] Regions with estimates: 2 -#> INFO [2024-03-06 10:53:59] Regions with runtime errors: 0 -#> INFO [2024-03-06 10:53:59] Producing summary -#> INFO [2024-03-06 10:53:59] No summary directory specified so returning summary output -#> INFO [2024-03-06 10:54:00] No target directory specified so returning timings +#> Writing EpiNow2.epinow logs to: /tmp/RtmpJDlhZu/epinow/2020-04-21.log +#> INFO [2024-05-10 07:54:30] Reporting estimates using data up to: 2020-04-21 +#> INFO [2024-05-10 07:54:30] No target directory specified so returning output +#> INFO [2024-05-10 07:54:30] Producing estimates for: testland, realland +#> INFO [2024-05-10 07:54:30] Regions excluded: none +#> INFO [2024-05-10 07:56:06] Completed estimates for: testland +#> INFO [2024-05-10 07:56:24] Completed estimates for: realland +#> INFO [2024-05-10 07:56:24] Completed regional estimates +#> INFO [2024-05-10 07:56:24] Regions with estimates: 2 +#> INFO [2024-05-10 07:56:24] Regions with runtime errors: 0 +#> INFO [2024-05-10 07:56:24] Producing summary +#> INFO [2024-05-10 07:56:24] No summary directory specified so returning summary output +#> INFO [2024-05-10 07:56:24] No target directory specified so returning timings ## summary region_separate_rt$summary$summarised_results$table -#> Region New confirmed cases by infection date -#> -#> 1: realland 2122 (1144 -- 3976) -#> 2: testland 2177 (992 -- 4216) -#> Expected change in daily cases Effective reproduction no. -#> -#> 1: Likely decreasing 0.85 (0.61 -- 1.2) -#> 2: Likely decreasing 0.86 (0.54 -- 1.2) -#> Rate of growth Doubling/halving time (days) -#> -#> 1: -0.033 (-0.094 -- 0.031) -21 (22 -- -7.3) -#> 2: -0.031 (-0.11 -- 0.034) -22 (20 -- -6.1) +#> Region New infections per day Expected change in daily reports +#> +#> 1: realland 2106 (1042 -- 4307) Likely decreasing +#> 2: testland 2128 (843 -- 4774) Likely decreasing +#> Effective reproduction no. Rate of growth +#> +#> 1: 0.86 (0.6 -- 1.2) -0.038 (-0.11 -- 0.046) +#> 2: 0.86 (0.52 -- 1.3) -0.038 (-0.18 -- 0.081) +#> Doubling/halving time (days) +#> +#> 1: -18 (15 -- -6.3) +#> 2: -18 (8.6 -- -3.8) ## plot region_separate_rt$summary$plots$R ``` -![plot of chunk regional_epinow_multiple](figure/regional_epinow_multiple-1.png) +![plot of chunk regional_epinow_multiple](epinow-regional_epinow_multiple-1.png) diff --git a/vignettes/epinow.Rmd.orig b/vignettes/epinow.Rmd.orig index b0b2fb782..f569bf952 100644 --- a/vignettes/epinow.Rmd.orig +++ b/vignettes/epinow.Rmd.orig @@ -17,8 +17,10 @@ knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 6.5, - fig.height = 6.5 + fig.height = 6.5, + fig.path = "epinow-" ) +set.seed(9876) ``` The _EpiNow2_ package contains functionality to run `estimate_infections()` in production mode, i.e. with full logging and saving all relevant outputs and plots to dedicated folders in the hard drive. @@ -47,13 +49,13 @@ We can then run the `epinow()` function with the same arguments as `estimate_inf res <- epinow(reported_cases, generation_time = generation_time_opts(example_generation_time), delays = delay_opts(delay), - rt = rt_opts(prior = rt_prior), - target_folder = "results" + rt = rt_opts(prior = rt_prior) ) res$plots$R ``` -The initial messages here indicate where log files can be found, and summarised results and plots are in the folder given by `target_folder` (here: `results/`). +The initial messages here indicate where log files can be found. +If you want summarised results and plots to be written out where they can be accessed later you can use the `target_folder` argument. # Running the model simultaneously on multiple regions diff --git a/vignettes/estimate_infections_options-bp-1.png b/vignettes/estimate_infections_options-bp-1.png new file mode 100644 index 0000000000000000000000000000000000000000..e0e88de2a8ebb23c8d288b05c238f99f492ef474 GIT binary patch literal 42560 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zz6)BYtflmge=}A8r}(+YpQV+6PG59*pCrjqNgDM2Dr$s)C8-eA=_+IUYj*8g0>J32 z-duqWw#@GzLWU18-4S+nc5ZG?qMe9iil?2iuFxk04T%*Xh_aa_xw9D=8EHO?8H|MW z{0N}6gET@huws)#f*6Q`$H7Na*DrqUYt*dn&T13Klsvc}TcU6nVDLw%_0Eo(teRX2 zPj^mI^!l&;Df^j6ynX-r$YK42WmiHw!Xq+re>0I)8XfC|(__DVm--Bwu(t;$7N-#c zA3Lm;mR4>S^7cG8(y~^|N7IvpFoPda{XZ3F{MQl9w=1-M5mR;ESCv!nkqA{Kb;TNa H^N{}q)C-vU literal 0 HcmV?d00001 diff --git a/vignettes/estimate_infections_options.Rmd b/vignettes/estimate_infections_options.Rmd index 5a76749ac..956b15ec2 100644 --- a/vignettes/estimate_infections_options.Rmd +++ b/vignettes/estimate_infections_options.Rmd @@ -26,11 +26,6 @@ We first load the _EpiNow2_ package and also the _rstan_ package that we will us ```r library("EpiNow2") -#> -#> Attaching package: 'EpiNow2' -#> The following object is masked from 'package:stats': -#> -#> Gamma library("rstan") #> Loading required package: StanHeaders #> @@ -66,7 +61,7 @@ ggplot(reported_cases, aes(x = date, y = confirm)) + ylab("Cases") ``` -![plot of chunk data](figure/data-1.png) +![plot of chunk data](estimate_infections_options-data-1.png) # Parameters @@ -182,31 +177,31 @@ def <- estimate_infections(reported_cases, delays = delay_opts(delay), rt = rt_opts(prior = rt_prior) ) -#> Warning: There were 4 divergent transitions after warmup. See +#> Warning: There were 11 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: Examine the pairs() plot to diagnose sampling problems # summarise results summary(def) -#> measure estimate -#> -#> 1: New confirmed cases by infection date 2271 (1111 -- 4285) -#> 2: Expected change in daily cases Likely decreasing -#> 3: Effective reproduction no. 0.88 (0.6 -- 1.2) -#> 4: Rate of growth -0.027 (-0.1 -- 0.037) -#> 5: Doubling/halving time (days) -25 (19 -- -6.9) +#> measure estimate +#> +#> 1: New infections per day 2237 (956 -- 4859) +#> 2: Expected change in daily reports Likely decreasing +#> 3: Effective reproduction no. 0.88 (0.58 -- 1.3) +#> 4: Rate of growth -0.034 (-0.16 -- 0.085) +#> 5: Doubling/halving time (days) -21 (8.2 -- -4.4) # elapsed time (in seconds) get_elapsed_time(def$fit) #> warmup sample -#> chain:1 11.075 14.604 -#> chain:2 13.366 9.757 -#> chain:3 13.132 13.092 -#> chain:4 13.467 17.969 +#> chain:1 22.546 17.871 +#> chain:2 43.610 35.665 +#> chain:3 21.929 18.197 +#> chain:4 18.185 17.063 # summary plot plot(def) ``` -![plot of chunk default](figure/default-1.png) +![plot of chunk default](estimate_infections_options-default-1.png) ## Reducing the accuracy of the approximate Gaussian Process @@ -220,31 +215,31 @@ agp <- estimate_infections(reported_cases, rt = rt_opts(prior = rt_prior), gp = gp_opts(basis_prop = 0.1) ) -#> Warning: There were 4 divergent transitions after warmup. See +#> Warning: There were 19 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: Examine the pairs() plot to diagnose sampling problems # summarise results summary(agp) -#> measure estimate -#> -#> 1: New confirmed cases by infection date 2306 (1175 -- 4321) -#> 2: Expected change in daily cases Likely decreasing -#> 3: Effective reproduction no. 0.88 (0.63 -- 1.2) -#> 4: Rate of growth -0.026 (-0.093 -- 0.034) -#> 5: Doubling/halving time (days) -26 (20 -- -7.5) +#> measure estimate +#> +#> 1: New infections per day 2229 (1060 -- 4985) +#> 2: Expected change in daily reports Likely decreasing +#> 3: Effective reproduction no. 0.88 (0.61 -- 1.2) +#> 4: Rate of growth -0.036 (-0.15 -- 0.076) +#> 5: Doubling/halving time (days) -19 (9.1 -- -4.8) # elapsed time (in seconds) get_elapsed_time(agp$fit) #> warmup sample -#> chain:1 7.821 9.160 -#> chain:2 8.874 11.466 -#> chain:3 10.379 14.372 -#> chain:4 6.943 12.677 +#> chain:1 14.264 17.067 +#> chain:2 14.501 16.719 +#> chain:3 17.512 24.243 +#> chain:4 16.937 17.436 # summary plot plot(agp) ``` -![plot of chunk lower_accuracy](figure/lower_accuracy-1.png) +![plot of chunk lower_accuracy](estimate_infections_options-lower_accuracy-1.png) ## Adjusting for future susceptible depletion @@ -262,31 +257,31 @@ dep <- estimate_infections(reported_cases, pop = 1000000, future = "latest" ) ) -#> Warning: There were 12 divergent transitions after warmup. See +#> Warning: There were 9 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: Examine the pairs() plot to diagnose sampling problems # summarise results summary(dep) -#> measure estimate -#> -#> 1: New confirmed cases by infection date 2329 (1146 -- 4415) -#> 2: Expected change in daily cases Likely decreasing -#> 3: Effective reproduction no. 0.89 (0.62 -- 1.2) -#> 4: Rate of growth -0.026 (-0.094 -- 0.037) -#> 5: Doubling/halving time (days) -27 (19 -- -7.4) +#> measure estimate +#> +#> 1: New infections per day 2219 (934 -- 4735) +#> 2: Expected change in daily reports Likely decreasing +#> 3: Effective reproduction no. 0.88 (0.57 -- 1.2) +#> 4: Rate of growth -0.034 (-0.16 -- 0.076) +#> 5: Doubling/halving time (days) -20 (9.1 -- -4.3) # elapsed time (in seconds) get_elapsed_time(dep$fit) #> warmup sample -#> chain:1 13.161 9.823 -#> chain:2 12.282 14.130 -#> chain:3 14.255 18.819 -#> chain:4 10.584 9.753 +#> chain:1 24.086 18.080 +#> chain:2 25.069 31.131 +#> chain:3 24.617 25.153 +#> chain:4 21.874 18.766 # summary plot plot(dep) ``` -![plot of chunk susceptible_depletion](figure/susceptible_depletion-1.png) +![plot of chunk susceptible_depletion](estimate_infections_options-susceptible_depletion-1.png) ## Adjusting for truncation of the most recent data @@ -331,31 +326,19 @@ trunc <- estimate_infections(reported_cases, truncation = trunc_opts(trunc_dist), rt = rt_opts(prior = rt_prior) ) -#> Warning: There were 11 divergent transitions after warmup. See -#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup -#> to find out why this is a problem and how to eliminate them. -#> Warning: Examine the pairs() plot to diagnose sampling problems +#> Error in vapply(delays[parametric], attr, "weight_prior", FUN.VALUE = logical(1)): values must be length 1, +#> but FUN(X[[3]]) result is length 0 # summarise results summary(trunc) -#> measure estimate -#> -#> 1: New confirmed cases by infection date 2485 (1252 -- 4790) -#> 2: Expected change in daily cases Likely decreasing -#> 3: Effective reproduction no. 0.91 (0.65 -- 1.2) -#> 4: Rate of growth -0.019 (-0.087 -- 0.045) -#> 5: Doubling/halving time (days) -36 (15 -- -7.9) +#> Error in object[[i]]: object of type 'builtin' is not subsettable # elapsed time (in seconds) get_elapsed_time(trunc$fit) -#> warmup sample -#> chain:1 11.657 9.733 -#> chain:2 16.154 9.816 -#> chain:3 16.801 13.905 -#> chain:4 12.173 10.649 +#> Error in (function (cond) : error in evaluating the argument 'object' in selecting a method for function 'get_elapsed_time': object of type 'builtin' is not subsettable # summary plot plot(trunc) ``` -![plot of chunk truncation](figure/truncation-1.png) +![plot of chunk truncation](estimate_infections_options-truncation-1.png) ## Projecting the reproduction number with the Gaussian Process @@ -371,31 +354,31 @@ project_rt <- estimate_infections(reported_cases, prior = rt_prior, future = "project" ) ) -#> Warning: There were 19 divergent transitions after warmup. See +#> Warning: There were 8 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: Examine the pairs() plot to diagnose sampling problems # summarise results summary(project_rt) -#> measure estimate -#> -#> 1: New confirmed cases by infection date 2285 (1145 -- 4321) -#> 2: Expected change in daily cases Likely decreasing -#> 3: Effective reproduction no. 0.89 (0.61 -- 1.2) -#> 4: Rate of growth -0.025 (-0.099 -- 0.039) -#> 5: Doubling/halving time (days) -28 (18 -- -7) +#> measure estimate +#> +#> 1: New infections per day 2200 (1010 -- 4815) +#> 2: Expected change in daily reports Likely decreasing +#> 3: Effective reproduction no. 0.87 (0.59 -- 1.2) +#> 4: Rate of growth -0.036 (-0.15 -- 0.08) +#> 5: Doubling/halving time (days) -19 (8.6 -- -4.5) # elapsed time (in seconds) get_elapsed_time(project_rt$fit) #> warmup sample -#> chain:1 14.177 17.887 -#> chain:2 13.982 9.758 -#> chain:3 11.849 9.622 -#> chain:4 16.444 13.904 +#> chain:1 23.881 34.602 +#> chain:2 18.612 18.302 +#> chain:3 26.161 28.837 +#> chain:4 21.156 29.987 # summary plot plot(project_rt) ``` -![plot of chunk gp_projection](figure/gp_projection-1.png) +![plot of chunk gp_projection](estimate_infections_options-gp_projection-1.png) ## Fixed reproduction number @@ -410,25 +393,25 @@ fixed <- estimate_infections(reported_cases, ) # summarise results summary(fixed) -#> measure estimate -#> -#> 1: New confirmed cases by infection date 15887 (9181 -- 28140) -#> 2: Expected change in daily cases Increasing -#> 3: Effective reproduction no. 1.2 (1.1 -- 1.3) -#> 4: Rate of growth 0.038 (0.027 -- 0.049) -#> 5: Doubling/halving time (days) 18 (14 -- 26) +#> measure estimate +#> +#> 1: New infections per day 16780 (9407 -- 30551) +#> 2: Expected change in daily reports Increasing +#> 3: Effective reproduction no. 1.2 (1.1 -- 1.3) +#> 4: Rate of growth 0.05 (0.035 -- 0.065) +#> 5: Doubling/halving time (days) 14 (11 -- 20) # elapsed time (in seconds) get_elapsed_time(fixed$fit) #> warmup sample -#> chain:1 0.666 0.524 -#> chain:2 0.671 0.398 -#> chain:3 0.706 0.425 -#> chain:4 0.551 0.514 +#> chain:1 1.270 0.951 +#> chain:2 1.109 0.804 +#> chain:3 1.151 0.893 +#> chain:4 1.242 0.926 # summary plot plot(fixed) ``` -![plot of chunk fixed](figure/fixed-1.png) +![plot of chunk fixed](estimate_infections_options-fixed-1.png) ## Breakpoints @@ -456,25 +439,25 @@ bkp <- estimate_infections(bp_cases, ) # summarise results summary(bkp) -#> measure estimate -#> -#> 1: New confirmed cases by infection date 2537 (2117 -- 3067) -#> 2: Expected change in daily cases Decreasing -#> 3: Effective reproduction no. 0.91 (0.88 -- 0.93) -#> 4: Rate of growth -0.02 (-0.025 -- -0.014) -#> 5: Doubling/halving time (days) -35 (-50 -- -28) +#> measure estimate +#> +#> 1: New infections per day 2356 (1932 -- 2898) +#> 2: Expected change in daily reports Decreasing +#> 3: Effective reproduction no. 0.89 (0.86 -- 0.92) +#> 4: Rate of growth -0.027 (-0.034 -- -0.02) +#> 5: Doubling/halving time (days) -25 (-35 -- -20) # elapsed time (in seconds) get_elapsed_time(bkp$fit) #> warmup sample -#> chain:1 1.301 1.325 -#> chain:2 1.147 1.201 -#> chain:3 1.710 1.384 -#> chain:4 0.961 1.147 +#> chain:1 1.819 2.313 +#> chain:2 1.927 2.225 +#> chain:3 2.038 3.406 +#> chain:4 2.024 2.143 # summary plot plot(bkp) ``` -![plot of chunk bp](figure/bp-1.png) +![plot of chunk bp](estimate_infections_options-bp-1.png) ## Weekly random walk @@ -491,25 +474,25 @@ rw <- estimate_infections(reported_cases, ) # summarise results summary(rw) -#> measure estimate -#> -#> 1: New confirmed cases by infection date 2182 (1103 -- 4267) -#> 2: Expected change in daily cases Likely decreasing -#> 3: Effective reproduction no. 0.87 (0.62 -- 1.2) -#> 4: Rate of growth -0.031 (-0.097 -- 0.032) -#> 5: Doubling/halving time (days) -22 (21 -- -7.1) +#> measure estimate +#> +#> 1: New infections per day 2136 (1056 -- 4315) +#> 2: Expected change in daily reports Likely decreasing +#> 3: Effective reproduction no. 0.87 (0.63 -- 1.2) +#> 4: Rate of growth -0.035 (-0.11 -- 0.044) +#> 5: Doubling/halving time (days) -20 (16 -- -6.3) # elapsed time (in seconds) get_elapsed_time(rw$fit) #> warmup sample -#> chain:1 2.500 3.987 -#> chain:2 2.341 4.075 -#> chain:3 2.303 3.118 -#> chain:4 2.235 3.873 +#> chain:1 4.182 6.196 +#> chain:2 4.202 6.631 +#> chain:3 4.999 7.652 +#> chain:4 4.127 4.635 # summary plot plot(rw) ``` -![plot of chunk weekly_rw](figure/weekly_rw-1.png) +![plot of chunk weekly_rw](estimate_infections_options-weekly_rw-1.png) ## No delays @@ -521,31 +504,37 @@ no_delay <- estimate_infections( reported_cases, generation_time = generation_time_opts(example_generation_time) ) -#> Warning: There were 5 divergent transitions after warmup. See +#> Warning: There were 32 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: Examine the pairs() plot to diagnose sampling problems +#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable. +#> Running the chains for more iterations may help. See +#> https://mc-stan.org/misc/warnings.html#bulk-ess +#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable. +#> Running the chains for more iterations may help. See +#> https://mc-stan.org/misc/warnings.html#tail-ess # summarise results summary(no_delay) -#> measure estimate -#> -#> 1: New confirmed cases by infection date 2776 (2336 -- 3311) -#> 2: Expected change in daily cases Decreasing -#> 3: Effective reproduction no. 0.88 (0.77 -- 0.99) -#> 4: Rate of growth -0.028 (-0.055 -- -0.0034) -#> 5: Doubling/halving time (days) -25 (-200 -- -13) +#> measure estimate +#> +#> 1: New infections per day 2759 (2325 -- 3311) +#> 2: Expected change in daily reports Decreasing +#> 3: Effective reproduction no. 0.87 (0.76 -- 0.98) +#> 4: Rate of growth -0.042 (-0.098 -- 0.0045) +#> 5: Doubling/halving time (days) -17 (150 -- -7.1) # elapsed time (in seconds) get_elapsed_time(no_delay$fit) #> warmup sample -#> chain:1 17.540 15.429 -#> chain:2 20.581 19.910 -#> chain:3 16.588 28.510 -#> chain:4 15.363 17.303 +#> chain:1 34.378 41.249 +#> chain:2 24.455 27.261 +#> chain:3 28.171 43.757 +#> chain:4 32.226 33.559 # summary plot plot(no_delay) ``` -![plot of chunk no_delays](figure/no_delays-1.png) +![plot of chunk no_delays](estimate_infections_options-no_delays-1.png) ## Non-parametric infection model @@ -565,22 +554,22 @@ non_parametric <- estimate_infections(reported_cases, ) # summarise results summary(non_parametric) -#> measure estimate -#> -#> 1: New confirmed cases by infection date 2306 (1696 -- 3051) -#> 2: Expected change in daily cases Decreasing -#> 3: Effective reproduction no. 0.89 (0.72 -- 0.99) -#> 4: Rate of growth -0.024 (-0.05 -- -0.0025) -#> 5: Doubling/halving time (days) -29 (-280 -- -14) +#> measure estimate +#> +#> 1: New infections per day 2539 (2066 -- 3083) +#> 2: Expected change in daily reports Decreasing +#> 3: Effective reproduction no. 0.92 (0.8 -- 0.99) +#> 4: Rate of growth -0.023 (-0.044 -- -0.0015) +#> 5: Doubling/halving time (days) -30 (-460 -- -16) # elapsed time (in seconds) get_elapsed_time(non_parametric$fit) #> warmup sample -#> chain:1 0.950 0.325 -#> chain:2 1.007 0.331 -#> chain:3 0.996 0.332 -#> chain:4 0.958 0.332 +#> chain:1 1.631 0.451 +#> chain:2 1.862 0.363 +#> chain:3 1.969 0.454 +#> chain:4 2.113 0.539 # summary plot plot(non_parametric) ``` -![plot of chunk nonparametric](figure/nonparametric-1.png) +![plot of chunk nonparametric](estimate_infections_options-nonparametric-1.png) diff --git a/vignettes/estimate_infections_options.Rmd.orig b/vignettes/estimate_infections_options.Rmd.orig index f1339b6ec..add78a2e0 100644 --- a/vignettes/estimate_infections_options.Rmd.orig +++ b/vignettes/estimate_infections_options.Rmd.orig @@ -17,8 +17,10 @@ knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 6.5, - fig.height = 6.5 + fig.height = 6.5, + fig.path = "estimate_infections_options-" ) +set.seed(9876) ``` The `estimate_infections()` function encodes a range of different model options. diff --git a/vignettes/estimate_infections_workflow-results-1.png b/vignettes/estimate_infections_workflow-results-1.png new file mode 100644 index 0000000000000000000000000000000000000000..0e8fe08396786978d3fdcbf156f1b627fdb4b19f GIT binary patch literal 50728 zcmb?@RX|j6`{s}$At2p|NO#9bNDD&I&Q@(wjrm*!$36+{7IqxqljFyQeY>j?B;qvzd{Pe@vSy%Zc4{i|{iFE9 z$!9m_m&m~qYypD?h>o*S(eCbU74S*V(Q<3(&p%m%pCoAU6mwitj?c!#JKc6h?W)=v z8X7Kx9(IDZH5h=?)jmxw^5=&v*4xdF2(HGO%*l!0j2pCij?`;opdwe-F)JcEPJU60 zTf6U!W=9t+d2hr8J)Gsr$;s{L@;v>07x4GFK@UFh8(?Z&g+wi*fO#wl4|Z@%yt3GRq6TTfQs#)-Xvo{>>d{CdjY6oRU+U%ShzM$roWACDg&swykv zi)}!7GHOt0*|e^I2EBq%JVjt^%{&`qlHP{PCWKa-Ep`12R$Me`6VYwLl&Hh0kVvm! 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"estimate_infections_workflow-" ) +set.seed(9876) ``` This vignette describes the typical workflow for estimating reproduction numbers and performing short-term forecasts for a disease spreading in a given setting using _EpiNow2_. @@ -29,7 +32,7 @@ See other vignettes for a more thorough exploration of [alternative model varian Obtaining a good and full understanding of the data being used is an important first step in any inference procedure such as the one applied here. _EpiNow2_ expects data in the format of a data frame with two columns, `date` and `confirm`, where `confirm` stands for the number of confirmed counts - although in reality this can be applied to any data including suspected cases and lab-confirmed outcomes. The user might already have the data as such a time series provided, for example, on public dashboards or directly from public health authorities. -Alternatively, they can be constructed from individual-level data, for example using the [incidence2](https://cran.r-project.org/web/packages/incidence2/index.html) R package. +Alternatively, they can be constructed from individual-level data, for example using the [incidence2](https://CRAN.R-project.org/package=incidence2) R package. An example data set called `example_confirm` is included in the package: ```{r} @@ -92,7 +95,7 @@ We used the mean and standard deviation to define this distribution with uncerta There are various ways the specific delay distributions mentioned below might be obtained. Often, they will come directly from the existing literature reviewed by the user and studies conducted elsewhere. Sometimes it might be possible to obtain them from existing databases, e.g. using the [epiparameter](https://github.com/epiverse-trace/epiparameter) R package. -Alternatively they might be obtainable from raw data, e.g. linelists. +Alternatively they might be obtainable from raw data, e.g. line-listed individual-level records. The _EpiNow2_ package contains functionality for estimating delay distributions from observed delays in the `estimate_delays()` function. For a more comprehensive treatment of delays and their estimation avoiding common biases one can consider, for example, the [dynamicaltruncation](https://github.com/parksw3/epidist-paper) R package and associated paper. diff --git a/vignettes/figure/bp-1.png b/vignettes/figure/bp-1.png deleted file mode 100644 index f1beee16e52ae2e6c0ccc9e143bc02ae8f36d3e8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 66069 zcmcF~Wm_CUw=DsJyF<{x;7)K01a}5^cXxLSPH=Y!?(XjHGPt|DU2^U@@40{Ae(2|^ zsp+n++PkWHueDc&%E^c#!r{Szfq@~4iwOb2z`o4=yI`O`YXT^iEy2Jbk<5PmlC$_F z`pd@B#$Lfz&%j93$lA!>%m64V2nNOx8L6aYim!yhm)2ZMLpBjGS6CQ}^v)VzX9=7R zTseFu(Q*<;@3DgPXU%u`1bgrCT5D%;`-Zwscn4DC|iGKOL7PM5xG4g%e2<#oRzedX! zQs89>c2WNkwvXE~op>9qw;oIdClmRyW-{du^KTttTFR1$_rBHN&X6V5m#a}c ztVQKi-$#Xq*-BGoNN3vvUVIva*DU&017xFX8NDzzAe!!zsO=NCLQt}lc0rE6E9uQ@ z`%ylOn$2LZNWrvP+k))juK4;4lsww-4;_n3*r&T_{?4VCplk%VI(74m44ezS)-&m( z!#Q~a(-HQO3BfkEsp+cRA4mzSq|K;*k4CDPvJGDUer(#uG6wfLr!z6*TAbo6Fb)%- 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