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.
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. This site refers to the release version of our package which can be installed from our Universe or from the latest GitHub release (see installation instructions). The development version of our documentation (corresponding to our
mainbranch on GitHub) 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 development 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 methods and 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.
Install the latest released version of the package with:
install.packages("epinowcast", repos = "https://epinowcast.r-universe.dev")Alternatively, install the development version (whilst we strive to limit breaking changes or the introduction of bugs during development this version may contain both) from GitHub using the following,
remotes::install_github("epinowcast/epinowcast", dependencies = TRUE)Historical package releases can be installed from GitHub using remotes
by adding the release tag as in the following example (which installs
0.2.0),
remotes::install_github("epinowcast/epinowcast", dependencies = TRUE, ref = "v0.2.0")Note: A similar method can be used to install a particular commit of the package which may be useful for some users who are unable to use a fixed release but concerned about the stability of their dependencies.
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()Note: This install process can be sped up using the cores argument
and past versions can be installed using the version argument (which
may be useful if install historical package releases).
As an alternative to local installation we provide a Docker image with
epinowcast and all dependencies installed. This can be used to run
epinowcast without installing dependencies locally. The image is
available
here.
In this quick start, we use the example of COVID-19 hospitalisations in
Germany 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 referenced in
the literature vignette.
In addition to epinowcast, this quick start makes use of data.table
and ggplot2, which are both installed when epinowcast is installed.
library(epinowcast)
library(data.table)
library(ggplot2)Nowcasting of right-truncated case counts involves the estimation of reporting delays for recently reported data. For this, we need to count cases both by when they occurred (often called “reference date”) and by when they were reported (often called “report date”). Then, the difference between the reference date and the report date is the reporting delay. For this quick start, we use data sourced from the Robert Koch Institute via the Germany Nowcasting hub. The data represent hospitalisation counts by date of positive test and date of test report in Germany up to October 1, 2021.
We first filter for a snapshot of retrospective data available 40 days before October 1, 2021 that contains 40 days of data. Then, we create the nowcast target which is the latest available hospitalisations by date of positive test. This will allow us to visualise how our nowcast made at the time compares to what was ultimately reported.
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
#> 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 2021-08-22This data is already in a format that can be used with epinowcast, as
it contains
- a reference date (column
reference_date): the date of the observation, in this example the date of a positive test - a report date (column
report_date): the date of report for a given set of observations by reference date - a count (column
confirm): the total (i.e. cumulative) number of hospitalisations by reference date and report date.
The package also provides a range of tools to convert data from line list, incidence, or other common formats into the required format (see Data converters).
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
#> 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 2021-10-01Before modelling, the input data needs to be converted into the
“reporting triangle” format (see our model description for
details). We also need
to determine metadata to facilitate the model specification. This
includes the number of days of data to use for the reference and report
modules, the maximum delay to consider, and, optionally, a grouping
(i.e. age group, location, or both) of observations. We process reported
data into the format required for epinowcast and return it in a
data.table. At this stage, we need to specify a grouping (i.e age,
location) if any.
pobs <- enw_preprocess_data(retro_nat_germany, max_delay = 40)
pobs
#> obs new_confirm latest
#> 1: <data.table[860x9]> <data.table[860x11]> <data.table[41x10]>
#> missing_reference reporting_triangle metareference metareport
#> 1: <data.table[0x6]> <data.table[41x42]> <data.table[41x9]> <data.table[80x12]>
#> metadelay time snapshots by groups max_delay max_date
#> 1: <data.table[40x4]> 41 41 1 40 2021-08-22The returned output is in the form of a data.table with metadata
stored as variables. It can be useful to check this output before
specifying the model, just to make sure everything is as expected.
The epinowcast package is designed to provide users with a flexible
and customizable modelling framework. The package comes equipped with
several modules that users can utilize to construct models, and also
allows users to create their own modules. These ensures that models can
be tailored to the user’s specific data and context.
The default nowcasting model in epinowcast consists of three modules:
- A process (expectation) module that models the expected counts by date
of reference (
reference_date) - A parametric reference reporting model which models the reporting delay distribution from the date of reference
- A non-parametric reporting model which models differences in the
reporting delay distribution by date of report (
report_date), for example, day-of-the-week effects on the reporting delay.
In the following sections, we specify simple models for each of these modules. The appropriateness of these specifications will vary depending on your context. See our vignettes for further details on model specification and examples of more complex models.
A commonly used process model in nowcasting is to model the expected
counts by date of reference via a geometric random walk as this acts as
a minimally informed smoothing prior and thus gives a lot of weight to
the observed data. This is the default process model in epinowcast.
Users may also specify this model for themselves using the
enw_expectation() function.
expectation_module <- enw_expectation(
~ 0 + (1 | day), data = pobs
)Here, day refers to the number of days from the start of the data.
As the underlying process model is an exponential growth rate model
((1 | day)) on the growth rate is equivalent to a geometric
random walk on expected counts by reference date. We are defining a
random effect as,
Our baseline assumption for the reporting delay is that it is
log-normally distributed, and static over time and strata. We can
specify this model using the enw_reference() function,
reference_module <- enw_reference(~1, distribution = "lognormal", data = pobs)Note that the default distribution is log-normal, hence the distribution argument could be omitted here.
Even where there is evidence that reporting processes can be
approximated by a single distribution, there may be additional reporting
effects that are not captured by the reference model. For example,
reporting may be lower on weekends or holidays. We can specify a model
for these effects using a hazard formulation (which captures the
conditional relationship between different reporting delays, see our
model description for
details) using the
enw_report() function. Here we specify a model with a random effect
for the day of the week to capture weekly seasonality in the reporting
delay.
report_module <- enw_report(~ (1 | day_of_week), data = pobs)As epinowcast uses cmdstan to fit its models, it is necessary to
first compile the model. This can be done using the enw_model()
function. Note that this step can be left to epinowcast, but here we
want to use multiple cores per chain to speed up model fitting and
therefore compile the model with this feature turned on.
model <- enw_model(threads = TRUE)We can now fit the model using the “No-U-Turn Sampler Markov chain
Monte Carlo”
method.
This is a type of Hamiltonian Monte Carlo (HMC) algorithm and is the
core fitting method used by cmdstan. The NUTS MCMC method is
efficient, automatically tunes its own parameters and is robust to
correlations between parameters, making it fast and effective at
generating samples from the posterior distribution. We specify fitting
options using enw_fit_opts() (note that the settings shown here are
tuned for speed and may not be appropriate for many real world use
cases). We also pass our preprocessed data (pobs), our pre-compiled
model (model), and our model modules (expectation_module,
reference_module, and report_module) to epinowcast, where they are
combined and used to fit the model.
options(mc.cores = 2)
nowcast <- epinowcast(data = 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
),
model = model
)The epinowcast() function returns an epinowcast object which
includes diagnostic information, the data used for fitting, and the
underlying CmdStanModel
object.
nowcast
#> obs new_confirm latest
#> 1: <data.table[860x9]> <data.table[860x11]> <data.table[41x10]>
#> missing_reference reporting_triangle metareference metareport
#> 1: <data.table[0x6]> <data.table[41x42]> <data.table[41x9]> <data.table[80x12]>
#> metadelay time snapshots by groups max_delay max_date
#> 1: <data.table[40x4]> 41 41 1 40 2021-08-22
#> fit data fit_args samples max_rhat
#> 1: <CmdStanMCMC[42]> <list[99]> <list[7]> 1000 1.02
#> divergent_transitions per_divergent_transitions max_treedepth
#> 1: 0 0 8
#> no_at_max_treedepth per_at_max_treedepth run_time
#> 1: 70 0.07 75.8The nowcast (the combination of currently observed and predicted unobserved data) can then be summarised using
nowcast |>
summary(probs = c(0.05, 0.95)) |>
head(n = 10)
#> reference_date report_date .group max_confirm location age_group confirm
#> 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
#> 1: 1 39 0 72.000 72 0.0000000 0.0000 72
#> 2: 1 38 0 69.057 69 0.2362346 0.0000 69
#> 3: 1 37 0 47.084 47 0.2881441 0.0000 47
#> 4: 1 36 0 65.192 65 0.4464699 0.0000 65
#> 5: 1 35 0 50.256 50 0.5410700 0.0000 50
#> 6: 1 34 0 36.231 36 0.5058605 0.0000 36
#> 7: 1 33 0 94.459 94 0.7272193 0.0000 94
#> 8: 1 32 0 91.718 92 0.8621016 1.4826 91
#> 9: 1 31 0 100.048 100 1.0444074 1.4826 99
#> 10: 1 30 0 87.207 87 1.1240171 1.4826 86
#> q95 rhat ess_bulk ess_tail
#> 1: 72 NA NA NA
#> 2: 70 0.9997408 1141.9827 1008.0970
#> 3: 48 0.9992307 1147.3343 1013.5831
#> 4: 66 1.0010101 987.9730 951.6919
#> 5: 51 0.9989981 1006.8937 985.9879
#> 6: 37 0.9981707 990.3342 994.7808
#> 7: 96 1.0003595 958.4646 930.9106
#> 8: 93 0.9999599 952.8034 1050.8725
#> 9: 102 1.0015879 1103.6075 967.2563
#> 10: 89 0.9994770 977.9843 945.4616Similarly, the summarised nowcast can be plotted against the latest observed data using
plot(nowcast, latest_obs = latest_germany_hosp)
Plotting posterior predictions can be a useful way of assessing
performance and checking that the model is capturing the underlying data
generation process adequately. We can do this directly on the output of
epinowcast() using
plot(nowcast, type = "posterior") +
facet_wrap(vars(reference_date), scale = "free")
Rather than using S3 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
#> 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
#> 1: 1 33 0 1 1 1 435
#> 2: 1 33 0 1 2 2 433
#> 3: 1 33 0 1 3 3 435
#> 4: 1 33 0 1 4 4 433
#> 5: 1 33 0 1 5 5 435
#> ---
#> 33996: 1 0 1 2 496 996 2130
#> 33997: 1 0 1 2 497 997 2009
#> 33998: 1 0 1 2 498 998 2174
#> 33999: 1 0 1 2 499 999 2002
#> 34000: 1 0 1 2 500 1000 3000
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)
Here we see that the model is underestimating the incidence of hospitalisations that were ultimately reported. There are a range of potential reasons for this, the first being that the process model does not fully capture the trend or day of the week periodicity present in the data. See our case study vignettes for ideas on how deal with such issues.
If you use epinowcast in your work, please consider citing it using
the following,
#> To cite package 'epinowcast' in publications use:
#>
#> Abbott S, Lison A, Funk S, Pearson C, Gruson H, Guenther F (2021).
#> "epinowcast: Flexible hierarchical nowcasting." _Zenodo_.
#> doi:10.5281/zenodo.5637165 <https://doi.org/10.5281/zenodo.5637165>,
#> <https://github.com/epinowcast/epinowcast>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {epinowcast: Flexible hierarchical nowcasting},
#> author = {Sam Abbott and Adrian Lison and Sebastian Funk and Carl Pearson and Hugo Gruson and Felix Guenther},
#> year = {2021},
#> journal = {Zenodo},
#> doi = {10.5281/zenodo.5637165},
#> url = {https://github.com/epinowcast/epinowcast},
#> }
If making use of our methodology or the methodology on which ours is based, please cite the relevant papers from our model outline.
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.
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.
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.