Tools to enable flexible and efficient hierarchical nowcasting of right-truncated epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes. Nowcasting, in this context, is gaining situational awareness using currently available observations and the reporting patterns of historical observations. This can be useful when tracking the spread of infectious disease in real-time: without nowcasting, changes in trends can be obfuscated by partial reporting or their detection may be delayed due to the use of simpler methods like truncation. While the package has been designed with epidemiological applications in mind, it could be applied to any set of right-truncated time-series count data.
Getting started and learning more
This README is a good place to get started with epinowcast, in particular the following installation and quick start sections. As you make use of the package, or if your problem requires a richer feature set than presented here, we also provide a range of other documentation, case studies, and spaces for the community to interact with each other. Below is a short list of current resources.
-
Package website: This includes a function reference, model outline, and case studies making use of the package. The development version of our documentation (corresponding to our
developbranch) is available here. - Organisation website: This includes links to our other resources as well as guest posts from community members and schedules for any related seminars being run by community members.
- Directory of example scripts: Not as fleshed out as our complete case studies these scripts are used during package developemnt and each showcase a subset of package functionality. Often newly introduced features will be explored here before surfacing in other areas of our documentation.
-
Community forum: Our community forum is where development of tools is discussed, along with related research from our members and discussions between users. If you are interested in real-time analysis of infectious disease this is likely a good place to start regardless of if you end up making use of
epinowcast.
Installation
Installing the package
Install the latest released version of the package with:
install.packages("epinowcast", repos = "https://epinowcast.r-universe.dev")Alternatively, install the stable development version from GitHub using the following,
remotes::install_github("epinowcast/epinowcast", dependencies = TRUE)The unstable development version can also be installed from GitHub using the following,
remotes::install_github("epinowcast/epinowcast@develop", dependencies = TRUE)Installing CmdStan
If you don’t already have CmdStan installed then, in addition to installing epinowcast, it is also necessary to install CmdStan using CmdStanR’s install_cmdstan() function to enable model fitting in epinowcast. A suitable C++ toolchain is also required. Instructions are provided in the Getting started with CmdStanR vignette. See the CmdStanR documentation for further details and support.
cmdstanr::install_cmdstan()Quick start
In this quick start we use COVID-19 hospitalisations by date of positive test in Germany available up to the 1st of October 2021 to demonstrate the specification and fitting of a simple nowcasting model using epinowcast. Examples using more complex models are available in the package vignettes and in the papers linked to in the literature vignette.
Package
As well as epinowcast this quick start makes use of data.table and ggplot2 which are both installed when epinowcast is installed.
Data
Nowcasting is effectively the estimation of reporting patterns for recently reported data. This requires data on these patterns for previous observations and typically this means the time series of data as reported on multiple consecutive days (in theory non-consecutive days could be used but this is not yet supported in epinowcast). For this quick start these data are sourced from the Robert Koch Institute via the Germany Nowcasting hub where they are deconvolved from weekly data and days with negative reported hospitalisations are adjusted.
Below we first filter for a snapshot of retrospective data available 40 days before the 1st of October that contains 40 days of data and then produce the nowcast target based on the latest available hospitalisations by date of positive test.
nat_germany_hosp <-
germany_covid19_hosp[location == "DE"][age_group %in% "00+"] |>
enw_filter_report_dates(latest_date = "2021-10-01")
retro_nat_germany <- nat_germany_hosp |>
enw_filter_report_dates(remove_days = 40) |>
enw_filter_reference_dates(include_days = 40)
retro_nat_germany
#> reference_date location age_group confirm report_date
#> <IDat> <fctr> <fctr> <int> <IDat>
#> 1: 2021-07-13 DE 00+ 21 2021-07-13
#> 2: 2021-07-14 DE 00+ 22 2021-07-14
#> 3: 2021-07-15 DE 00+ 28 2021-07-15
#> 4: 2021-07-16 DE 00+ 19 2021-07-16
#> 5: 2021-07-17 DE 00+ 20 2021-07-17
#> ---
#> 857: 2021-07-14 DE 00+ 72 2021-08-21
#> 858: 2021-07-15 DE 00+ 69 2021-08-22
#> 859: 2021-07-13 DE 00+ 59 2021-08-21
#> 860: 2021-07-14 DE 00+ 72 2021-08-22
#> 861: 2021-07-13 DE 00+ 59 2021-08-22
latest_germany_hosp <- nat_germany_hosp |>
enw_latest_data() |>
enw_filter_reference_dates(remove_days = 40, include_days = 40)
head(latest_germany_hosp, n = 10)
#> reference_date location age_group confirm report_date
#> <IDat> <fctr> <fctr> <int> <IDat>
#> 1: 2021-07-13 DE 00+ 60 2021-10-01
#> 2: 2021-07-14 DE 00+ 74 2021-10-01
#> 3: 2021-07-15 DE 00+ 69 2021-10-01
#> 4: 2021-07-16 DE 00+ 49 2021-10-01
#> 5: 2021-07-17 DE 00+ 67 2021-10-01
#> 6: 2021-07-18 DE 00+ 51 2021-10-01
#> 7: 2021-07-19 DE 00+ 36 2021-10-01
#> 8: 2021-07-20 DE 00+ 96 2021-10-01
#> 9: 2021-07-21 DE 00+ 94 2021-10-01
#> 10: 2021-07-22 DE 00+ 99 2021-10-01Data preprocessing and model specification
Process reported data into format required for epinowcast and return in a data.table. At this stage specify grouping (i.e age, location) if any. It can be useful to check this output before beginning to model to make sure everything is as expected.
pobs <- enw_preprocess_data(retro_nat_germany, max_delay = 40)
pobs
#> obs new_confirm latest
#> <list> <list> <list>
#> 1: <data.table[860x9]> <data.table[860x11]> <data.table[41x10]>
#> missing_reference reporting_triangle metareference metareport
#> <list> <list> <list> <list>
#> 1: <data.table[0x6]> <data.table[41x42]> <data.table[41x9]> <data.table[80x12]>
#> metadelay time snapshots by groups max_delay max_date
#> <list> <int> <int> <list> <int> <num> <IDat>
#> 1: <data.table[40x4]> 41 41 1 40 2021-08-22Construct a parametric lognormal intercept only model for the date of reference using the metadata produced by enw_preprocess_data(). Note that epinowcast uses a sparse design matrix for parametric delay distributions to reduce runtimes so the design matrix shows only unique rows with index containing the mapping to the full design matrix.
reference_module <- enw_reference(~1, distribution = "lognormal", data = pobs)Construct a model with a random effect for the day of report using the metadata produced by enw_preprocess_data().
report_module <- enw_report(~ (1 | day_of_week), data = pobs)Construct a model with a lognormal random walk on expected cases. See enw_expectation() for other suggested choices.
expectation_module <- enw_expectation(
~ 0 + (1 | day), data = pobs
)Model fitting
First compile the model. This step can be left to epinowcast but here we want to use multiple cores per chain to speed up model fitting and so need to compile the model with this feature turned on.
model <- enw_model(threads = TRUE)We now fit the model and produce a nowcast using this fit. Note that here we use two chains each using two threads as a demonstration but in general using 4 chains is recommended. Also note that warm-up and sampling iterations have been set below default values to reduce compute requirements but this may not be sufficient for many real world use cases. Finally, note that here we have silenced fitting progress and potential warning messages for the purposes of keeping this quick start short but in general this should not be done.
options(mc.cores = 2)
nowcast <- epinowcast(pobs,
expectation = expectation_module,
reference = reference_module,
report = report_module,
fit = enw_fit_opts(,
save_warmup = FALSE, pp = TRUE,
chains = 2, threads_per_chain = 2,
iter_sampling = 500, iter_warmup = 500,
show_messages = FALSE, refresh = 0
),
model = model
)
#> Running MCMC with 2 parallel chains, with 2 thread(s) per chain...
#>
#> Chain 2 finished in 41.3 seconds.
#> Chain 1 finished in 43.0 seconds.
#>
#> Both chains finished successfully.
#> Mean chain execution time: 42.1 seconds.
#> Total execution time: 43.1 seconds.Results
Print the output from epinowcast which includes diagnostic information, the data used for fitting, and the cmdstanr object.
nowcast
#> obs new_confirm latest
#> <list> <list> <list>
#> 1: <data.table[860x9]> <data.table[860x11]> <data.table[41x10]>
#> missing_reference reporting_triangle metareference metareport
#> <list> <list> <list> <list>
#> 1: <data.table[0x6]> <data.table[41x42]> <data.table[41x9]> <data.table[80x12]>
#> metadelay time snapshots by groups max_delay max_date
#> <list> <int> <int> <list> <int> <num> <IDat>
#> 1: <data.table[40x4]> 41 41 1 40 2021-08-22
#> fit
#> <list>
#> 1: <CmdStanMCMC>\n Inherits from: <CmdStanFit>\n Public:\n clone: function (deep = FALSE) \n cmdstan_diagnose: function () \n cmdstan_summary: function (flags = NULL) \n code: function () \n constrain_variables: function (unconstrained_variables, transformed_parameters = TRUE, \n data_file: function () \n diagnostic_summary: function (diagnostics = c("divergences", "treedepth", "ebfmi"), \n draws: function (variables = NULL, inc_warmup = FALSE, format = getOption("cmdstanr_draws_format", \n expose_functions: function (global = FALSE, verbose = FALSE) \n functions: environment\n grad_log_prob: function (unconstrained_variables, jacobian_adjustment = TRUE) \n hessian: function (unconstrained_variables, jacobian_adjustment = TRUE) \n init: function () \n init_model_methods: function (seed = 0, verbose = FALSE, hessian = FALSE) \n initialize: function (runset) \n inv_metric: function (matrix = TRUE) \n latent_dynamics_files: function (include_failed = FALSE) \n log_prob: function (unconstrained_variables, jacobian_adjustment = TRUE) \n loo: function (variables = "log_lik", r_eff = TRUE, ...) \n lp: function () \n metadata: function () \n num_chains: function () \n num_procs: function () \n output: function (id = NULL) \n output_files: function (include_failed = FALSE) \n print: function (variables = NULL, ..., digits = 2, max_rows = getOption("cmdstanr_max_rows", \n profile_files: function (include_failed = FALSE) \n profiles: function () \n return_codes: function () \n runset: CmdStanRun, R6\n sampler_diagnostics: function (inc_warmup = FALSE, format = getOption("cmdstanr_draws_format", \n save_data_file: function (dir = ".", basename = NULL, timestamp = TRUE, random = TRUE) \n save_latent_dynamics_files: function (dir = ".", basename = NULL, timestamp = TRUE, random = TRUE) \n save_object: function (file, ...) \n save_output_files: function (dir = ".", basename = NULL, timestamp = TRUE, random = TRUE) \n save_profile_files: function (dir = ".", basename = NULL, timestamp = TRUE, random = TRUE) \n summary: function (variables = NULL, ...) \n time: function () \n unconstrain_draws: function (files = NULL, draws = NULL) \n unconstrain_variables: function (variables) \n variable_skeleton: function (transformed_parameters = TRUE, generated_quantities = TRUE) \n Private:\n draws_: -1481.3 -1481.72 -1471.02 -1471.1 -1475.78 -1473.61 -147 ...\n init_: NULL\n inv_metric_: list\n metadata_: list\n model_methods_env_: environment\n profiles_: NULL\n read_csv_: function (variables = NULL, sampler_diagnostics = NULL, format = getOption("cmdstanr_draws_format", \n sampler_diagnostics_: 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 6 7 7 7 7 7 7 7 7 7 ...\n warmup_draws_: NULL\n warmup_sampler_diagnostics_: NULL
#> data fit_args samples max_rhat divergent_transitions
#> <list> <list> <int> <num> <num>
#> 1: <list[99]> <list[8]> 1000 1.01 0
#> per_divergent_transitions max_treedepth no_at_max_treedepth
#> <num> <num> <int>
#> 1: 0 8 15
#> per_at_max_treedepth run_time
#> <num> <num>
#> 1: 0.015 43.1Summarise the nowcast for the latest snapshot of data.
nowcast |>
summary(probs = c(0.05, 0.95)) |>
head(n = 10)
#> reference_date report_date .group max_confirm location age_group confirm
#> <IDat> <IDat> <num> <int> <fctr> <fctr> <int>
#> 1: 2021-07-14 2021-08-22 1 72 DE 00+ 72
#> 2: 2021-07-15 2021-08-22 1 69 DE 00+ 69
#> 3: 2021-07-16 2021-08-22 1 47 DE 00+ 47
#> 4: 2021-07-17 2021-08-22 1 65 DE 00+ 65
#> 5: 2021-07-18 2021-08-22 1 50 DE 00+ 50
#> 6: 2021-07-19 2021-08-22 1 36 DE 00+ 36
#> 7: 2021-07-20 2021-08-22 1 94 DE 00+ 94
#> 8: 2021-07-21 2021-08-22 1 91 DE 00+ 91
#> 9: 2021-07-22 2021-08-22 1 99 DE 00+ 99
#> 10: 2021-07-23 2021-08-22 1 86 DE 00+ 86
#> cum_prop_reported delay prop_reported mean median sd mad q5
#> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 1 39 0 72.000 72 0.0000000 0.0000 72
#> 2: 1 38 0 69.057 69 0.2486218 0.0000 69
#> 3: 1 37 0 47.073 47 0.2715598 0.0000 47
#> 4: 1 36 0 65.185 65 0.4438152 0.0000 65
#> 5: 1 35 0 50.238 50 0.4935581 0.0000 50
#> 6: 1 34 0 36.254 36 0.5193782 0.0000 36
#> 7: 1 33 0 94.513 94 0.7525938 0.0000 94
#> 8: 1 32 0 91.728 92 0.8560074 1.4826 91
#> 9: 1 31 0 100.016 100 1.0727977 1.4826 99
#> 10: 1 30 0 87.197 87 1.1249251 1.4826 86
#> q95 rhat ess_bulk ess_tail
#> <num> <num> <num> <num>
#> 1: 72 NA NA NA
#> 2: 70 1.0016127 930.1962 907.6383
#> 3: 48 0.9982702 1055.7404 1013.5831
#> 4: 66 1.0008253 902.0810 893.6840
#> 5: 51 0.9996211 761.5321 839.5581
#> 6: 37 1.0014637 946.0659 938.5932
#> 7: 96 0.9989225 894.9256 904.0204
#> 8: 93 1.0019180 887.0910 726.9359
#> 9: 102 1.0022603 973.0836 790.9693
#> 10: 89 0.9984863 925.5100 797.7415Plot the summarised nowcast against currently observed data (or optionally more recent data for comparison purposes).
plot(nowcast, latest_obs = latest_germany_hosp)
Plot posterior predictions for observed notifications by date of report as a check of how well the model reproduces the observed data.
plot(nowcast, type = "posterior") +
facet_wrap(vars(reference_date), scale = "free")
Rather than using the methods supplied for epinowcast directly, package functions can also be used to extract nowcast posterior samples, summarise them, and then plot them. This is demonstrated here by plotting the 7 day incidence for hospitalisations.
# extract samples
samples <- summary(nowcast, type = "nowcast_samples")
# Take a 7 day rolling sum of both samples and observations
cols <- c("confirm", "sample")
samples[, (cols) := lapply(.SD, frollsum, n = 7),
.SDcols = cols, by = ".draw"
][!is.na(sample)]
#> reference_date report_date .group max_confirm location age_group confirm
#> <IDat> <IDat> <num> <int> <fctr> <fctr> <int>
#> 1: 2021-07-20 2021-08-22 1 94 DE 00+ 433
#> 2: 2021-07-20 2021-08-22 1 94 DE 00+ 433
#> 3: 2021-07-20 2021-08-22 1 94 DE 00+ 433
#> 4: 2021-07-20 2021-08-22 1 94 DE 00+ 433
#> 5: 2021-07-20 2021-08-22 1 94 DE 00+ 433
#> ---
#> 33996: 2021-08-22 2021-08-22 1 45 DE 00+ 1093
#> 33997: 2021-08-22 2021-08-22 1 45 DE 00+ 1093
#> 33998: 2021-08-22 2021-08-22 1 45 DE 00+ 1093
#> 33999: 2021-08-22 2021-08-22 1 45 DE 00+ 1093
#> 34000: 2021-08-22 2021-08-22 1 45 DE 00+ 1093
#> cum_prop_reported delay prop_reported .chain .iteration .draw sample
#> <num> <num> <num> <int> <int> <int> <num>
#> 1: 1 33 0 1 1 1 434
#> 2: 1 33 0 1 2 2 438
#> 3: 1 33 0 1 3 3 436
#> 4: 1 33 0 1 4 4 435
#> 5: 1 33 0 1 5 5 433
#> ---
#> 33996: 1 0 1 2 496 996 2189
#> 33997: 1 0 1 2 497 997 2063
#> 33998: 1 0 1 2 498 998 2083
#> 33999: 1 0 1 2 499 999 2087
#> 34000: 1 0 1 2 500 1000 2308
latest_germany_hosp_7day <- copy(latest_germany_hosp)[
,
confirm := frollsum(confirm, n = 7)
][!is.na(confirm)]
# Summarise samples
sum_across_last_7_days <- enw_summarise_samples(samples)
# Plot samples
enw_plot_nowcast_quantiles(sum_across_last_7_days, latest_germany_hosp_7day)
Citation
If using epinowcast in your work please consider citing it using the following,
#>
#> To cite epinowcast in publications use:
#>
#> Sam Abbott, Adrian Lison, and Sebastian Funk (2021). epinowcast:
#> Flexible hierarchical nowcasting, DOI: 10.5281/zenodo.5637165
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {epinowcast: Flexible hierarchical nowcasting},
#> author = {Sam Abbott and Adrian Lison and Sebastian Funk},
#> journal = {Zenodo},
#> year = {2021},
#> doi = {10.5281/zenodo.5637165},
#> }If making use of our methodology or the methodology on which ours is based please cite the relevant papers from our model outline.
How to make a bug report or feature request
Please briefly describe your problem and what output you expect in an issue. If you have a question, please don’t open an issue. Instead, ask on our Q and A page. See our contributing guide for more information.
Contributing
We welcome contributions and new contributors! We particularly appreciate help on priority problems in the issues. Please check and add to the issues, and/or add a pull request. See our contributing guide for more information.
If interested in expanding the functionality of the underlying model note that epinowcast allows users to pass in their own models meaning that alternative parameterisations, for example altering the forecast model used for inferring expected observations, may be easily tested within the package infrastructure. Once this testing has been done alterations that increase the flexibility of the package model and improves its defaults are very welcome via pull request or other communication with the package authors. Even if not wanting to add your updated model to the package please do reach out as we would love to hear about your use case.
Code of Conduct
Please note that the epinowcast project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.