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Getting started with EpiNow2
In EpiNow2: Estimate and Forecast Real-Time Infection Dynamics
Quick start
In the following section we give an overview of the simple use case for epinow() and regional_epinow().
The first step to using the package is to load it as follows.
library(EpiNow2)
Reporting delays, incubation period and generation time
Distributions can be supplied in two ways. First, one can supply delay data to estimate_delay(), where a subsampled bootstrapped lognormal will be fit to account for uncertainty in the observed data without being biased by changes in incidence (see ?EpiNow2::estimate_delay()).
Second, one can specify predetermined delays with uncertainty using the distribution functions such as Gamma or LogNormal. An arbitrary number of delay distributions are supported in dist_spec() with a common use case being an incubation period followed by a reporting delay. For more information on specifying distributions see ?EpiNow2::Distributions or the delays vignette.
For example if data on the delay between onset and infection was available we could fit a distribution to it, using estimate_delay(), with appropriate uncertainty as follows (note this is a synthetic example),
reporting_delay <- estimate_delay(
rlnorm(1000, log(2), 1),
max_value = 14, bootstraps = 1
)
If data was not available we could instead specify an informed estimate of the likely delay using the distribution functions Gamma or LogNormal.
To demonstrate, we choose a lognormal distribution with mean 2, standard deviation 1 and a maximum of 10. This is just an example and unlikely to apply in any particular use case.
reporting_delay <- LogNormal(mean = 2, sd = 1, max = 10)
reporting_delay
#> - 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 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.
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
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
Users can also pass a non-parametric delay distribution vector using the NonParametric option
for both the generation interval and reporting delays. It is important to note that if doing so,
both delay distributions are 0-indexed, meaning the first element corresponds to the probability mass
at day 0 of an individual's infection. Because the discretised renewal equation doesn't support mass on day 0, the generation interval should be passed in as a 0-indexed vector with a mass of zero on day 0.
example_non_parametric_gi <- NonParametric(pmf = c(0, 0.3, 0.5, 0.2))
example_non_parametric_delay <- NonParametric(pmf = c(0.01, 0.1, 0.5, 0.3, 0.09))
These distributions are passed to downstream functions in the same way that the
parametric distributions are.
Now, to the functions.
epinow()
This function represents the core functionality of the package and includes results reporting, plotting, and optional saving. It requires a data frame of cases by date of report and the distributions defined above.
Load example case data from {EpiNow2}.
reported_cases <- example_confirmed[1:60]
head(reported_cases)
#> date confirm
#> <Date> <num>
#> 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).
estimates <- epinow(
data = reported_cases,
generation_time = gt_opts(example_generation_time),
delays = delay_opts(example_incubation_period + reporting_delay),
rt = rt_opts(prior = LogNormal(mean = 2, sd = 0.2)),
stan = stan_opts(cores = 4),
verbose = interactive()
)
names(estimates)
#> [1] "fit" "args" "observations" "timing"
The default model uses a Gaussian process to estimate time-varying transmission, which provides flexible estimates but can take several minutes to run. If speed is a priority, there are several alternatives:
- Use a weekly random walk instead of the Gaussian process (
rt = rt_opts(..., rw = 7) with gp = NULL), as shown in the regional_epinow() example below.
- Use variational inference for fast but unreliable approximate results (
stan = stan_opts(method = "vb")).
- Reduce the accuracy of the Gaussian process approximation (see
?gp_opts).
For examples of different model configurations, see the estimate_infections_options vignette.
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.
knitr::kable(summary(estimates))
|measure |estimate |
|:----------------------------|:-----------------------|
|New infections per day |2240 (1363 -- 3709) |
|Expected change in reports |Likely decreasing |
|Effective reproduction no. |0.89 (0.72 -- 1.1) |
|Rate of growth |-0.03 (-0.096 -- 0.038) |
|Doubling/halving time (days) |-23 (18 -- -7.2) |
Summarised parameter estimates can also easily be returned, either filtered for a single parameter or for all parameters.
head(summary(estimates, type = "parameters", params = "R"))
#> date variable strat type median mean sd lower_90
#> <Date> <char> <int> <char> <num> <num> <num> <num>
#> 1: 2020-02-22 R NA estimate 2.178490 2.184456 0.12007169 1.993521
#> 2: 2020-02-23 R NA estimate 2.143038 2.146818 0.10919319 1.972582
#> 3: 2020-02-24 R NA estimate 2.104298 2.107843 0.10069813 1.947632
#> 4: 2020-02-25 R NA estimate 2.062887 2.067720 0.09427498 1.918777
#> 5: 2020-02-26 R NA estimate 2.022027 2.026632 0.08946219 1.886428
#> 6: 2020-02-27 R NA estimate 1.980695 1.984750 0.08574980 1.851349
#> lower_50 lower_20 upper_20 upper_50 upper_90
#> <num> <num> <num> <num> <num>
#> 1: 2.104145 2.150307 2.208533 2.262534 2.385199
#> 2: 2.073533 2.115388 2.167645 2.217938 2.330660
#> 3: 2.040771 2.078324 2.128667 2.170718 2.280794
#> 4: 2.001783 2.041288 2.085802 2.126371 2.227861
#> 5: 1.963473 2.000645 2.045253 2.083873 2.180490
#> 6: 1.924749 1.959991 2.003451 2.040541 2.132498
Reported cases can be extracted using get_predictions() which returns summarised estimates by default.
head(get_predictions(estimates))
#> date median mean sd lower_90 lower_50 lower_20 upper_20
#> <Date> <num> <num> <num> <num> <num> <num> <num>
#> 1: 2020-02-22 35 35.9840 9.774483 21 29 33.0 38
#> 2: 2020-02-23 53 53.5585 13.729998 33 44 49.0 56
#> 3: 2020-02-24 64 65.2305 15.386804 42 55 60.6 68
#> 4: 2020-02-25 72 73.1890 16.513589 48 61 68.0 76
#> 5: 2020-02-26 82 83.2085 18.383280 56 70 78.0 87
#> 6: 2020-02-27 119 120.7925 24.347502 84 104 113.0 125
#> upper_50 upper_90
#> <num> <num>
#> 1: 42 53
#> 2: 62 78
#> 3: 74 93
#> 4: 84 103
#> 5: 94 116
#> 6: 137 162
A range of plots are returned (with the single summary plot shown below). These plots can also be generated using the following plot method.
plot(estimates)
regional_epinow()
The regional_epinow() function runs the epinow() function across multiple regions in
an efficient manner.
Define cases in multiple regions delineated by the region variable.
reported_cases <- data.table::rbindlist(list(
data.table::copy(reported_cases)[, region := "testland"],
reported_cases[, region := "realland"]
))
head(reported_cases)
#> date confirm region
#> <Date> <num> <char>
#> 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. We also assign "testland" a different ascertainment of 50%, using the opts_list() function, which is used to assign region-specific settings.
obs <- opts_list(
obs_opts(),
reported_cases,
testland = obs_opts(scale = Fixed(0.5))
)
estimates <- regional_epinow(
data = reported_cases,
generation_time = gt_opts(example_generation_time),
delays = delay_opts(example_incubation_period + reporting_delay),
rt = rt_opts(prior = LogNormal(mean = 2, sd = 0.2), rw = 7),
obs = obs,
gp = NULL,
stan = stan_opts(cores = 4, warmup = 250, samples = 1000),
logs = NULL
)
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.
Summary measures that are returned include a table formatted for reporting (along with raw results for further processing).
knitr::kable(estimates$summary$summarised_results$table)
|Region |New infections per day |Expected change in reports |Effective reproduction no. |Rate of growth |Doubling/halving time (days) |
|:--------|:----------------------|:--------------------------|:--------------------------|:-----------------------|:----------------------------|
|realland |1675 (4 -- 3482) |Likely decreasing |0.78 (0.37 -- 1.1) |-0.062 (-0.11 -- 0.024) |-11 (28 -- -6.5) |
|testland |3978 (2132 -- 8168) |Likely decreasing |0.85 (0.63 -- 1.2) |-0.042 (-0.11 -- 0.043) |-16 (16 -- -6.3) |
A range of plots are again returned (with the single summary plot shown below).
estimates$summary$summary_plot
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EpiNow2 documentation built on June 17, 2026, 1:07 a.m.
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Quick start
In the following section we give an overview of the simple use case for epinow() and regional_epinow().
The first step to using the package is to load it as follows.
library(EpiNow2)
Reporting delays, incubation period and generation time
Distributions can be supplied in two ways. First, one can supply delay data to estimate_delay(), where a subsampled bootstrapped lognormal will be fit to account for uncertainty in the observed data without being biased by changes in incidence (see ?EpiNow2::estimate_delay()).
Second, one can specify predetermined delays with uncertainty using the distribution functions such as Gamma or LogNormal. An arbitrary number of delay distributions are supported in dist_spec() with a common use case being an incubation period followed by a reporting delay. For more information on specifying distributions see ?EpiNow2::Distributions or the delays vignette.
For example if data on the delay between onset and infection was available we could fit a distribution to it, using estimate_delay(), with appropriate uncertainty as follows (note this is a synthetic example),
reporting_delay <- estimate_delay( rlnorm(1000, log(2), 1), max_value = 14, bootstraps = 1 )
If data was not available we could instead specify an informed estimate of the likely delay using the distribution functions Gamma or LogNormal.
To demonstrate, we choose a lognormal distribution with mean 2, standard deviation 1 and a maximum of 10. This is just an example and unlikely to apply in any particular use case.
reporting_delay <- LogNormal(mean = 2, sd = 1, max = 10) reporting_delay #> - 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 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.
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 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
Users can also pass a non-parametric delay distribution vector using the NonParametric option
for both the generation interval and reporting delays. It is important to note that if doing so,
both delay distributions are 0-indexed, meaning the first element corresponds to the probability mass
at day 0 of an individual's infection. Because the discretised renewal equation doesn't support mass on day 0, the generation interval should be passed in as a 0-indexed vector with a mass of zero on day 0.
example_non_parametric_gi <- NonParametric(pmf = c(0, 0.3, 0.5, 0.2)) example_non_parametric_delay <- NonParametric(pmf = c(0.01, 0.1, 0.5, 0.3, 0.09))
These distributions are passed to downstream functions in the same way that the parametric distributions are.
Now, to the functions.
epinow()
This function represents the core functionality of the package and includes results reporting, plotting, and optional saving. It requires a data frame of cases by date of report and the distributions defined above.
Load example case data from {EpiNow2}.
reported_cases <- example_confirmed[1:60] head(reported_cases) #> date confirm #> <Date> <num> #> 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).
estimates <- epinow( data = reported_cases, generation_time = gt_opts(example_generation_time), delays = delay_opts(example_incubation_period + reporting_delay), rt = rt_opts(prior = LogNormal(mean = 2, sd = 0.2)), stan = stan_opts(cores = 4), verbose = interactive() ) names(estimates) #> [1] "fit" "args" "observations" "timing"
The default model uses a Gaussian process to estimate time-varying transmission, which provides flexible estimates but can take several minutes to run. If speed is a priority, there are several alternatives:
- Use a weekly random walk instead of the Gaussian process (
rt = rt_opts(..., rw = 7)withgp = NULL), as shown in theregional_epinow()example below. - Use variational inference for fast but unreliable approximate results (
stan = stan_opts(method = "vb")). - Reduce the accuracy of the Gaussian process approximation (see
?gp_opts).
For examples of different model configurations, see the estimate_infections_options vignette.
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.
knitr::kable(summary(estimates))
|measure |estimate | |:----------------------------|:-----------------------| |New infections per day |2240 (1363 -- 3709) | |Expected change in reports |Likely decreasing | |Effective reproduction no. |0.89 (0.72 -- 1.1) | |Rate of growth |-0.03 (-0.096 -- 0.038) | |Doubling/halving time (days) |-23 (18 -- -7.2) |
Summarised parameter estimates can also easily be returned, either filtered for a single parameter or for all parameters.
head(summary(estimates, type = "parameters", params = "R")) #> date variable strat type median mean sd lower_90 #> <Date> <char> <int> <char> <num> <num> <num> <num> #> 1: 2020-02-22 R NA estimate 2.178490 2.184456 0.12007169 1.993521 #> 2: 2020-02-23 R NA estimate 2.143038 2.146818 0.10919319 1.972582 #> 3: 2020-02-24 R NA estimate 2.104298 2.107843 0.10069813 1.947632 #> 4: 2020-02-25 R NA estimate 2.062887 2.067720 0.09427498 1.918777 #> 5: 2020-02-26 R NA estimate 2.022027 2.026632 0.08946219 1.886428 #> 6: 2020-02-27 R NA estimate 1.980695 1.984750 0.08574980 1.851349 #> lower_50 lower_20 upper_20 upper_50 upper_90 #> <num> <num> <num> <num> <num> #> 1: 2.104145 2.150307 2.208533 2.262534 2.385199 #> 2: 2.073533 2.115388 2.167645 2.217938 2.330660 #> 3: 2.040771 2.078324 2.128667 2.170718 2.280794 #> 4: 2.001783 2.041288 2.085802 2.126371 2.227861 #> 5: 1.963473 2.000645 2.045253 2.083873 2.180490 #> 6: 1.924749 1.959991 2.003451 2.040541 2.132498
Reported cases can be extracted using get_predictions() which returns summarised estimates by default.
head(get_predictions(estimates)) #> date median mean sd lower_90 lower_50 lower_20 upper_20 #> <Date> <num> <num> <num> <num> <num> <num> <num> #> 1: 2020-02-22 35 35.9840 9.774483 21 29 33.0 38 #> 2: 2020-02-23 53 53.5585 13.729998 33 44 49.0 56 #> 3: 2020-02-24 64 65.2305 15.386804 42 55 60.6 68 #> 4: 2020-02-25 72 73.1890 16.513589 48 61 68.0 76 #> 5: 2020-02-26 82 83.2085 18.383280 56 70 78.0 87 #> 6: 2020-02-27 119 120.7925 24.347502 84 104 113.0 125 #> upper_50 upper_90 #> <num> <num> #> 1: 42 53 #> 2: 62 78 #> 3: 74 93 #> 4: 84 103 #> 5: 94 116 #> 6: 137 162
A range of plots are returned (with the single summary plot shown below). These plots can also be generated using the following plot method.
plot(estimates)
regional_epinow()
The regional_epinow() function runs the epinow() function across multiple regions in
an efficient manner.
Define cases in multiple regions delineated by the region variable.
reported_cases <- data.table::rbindlist(list( data.table::copy(reported_cases)[, region := "testland"], reported_cases[, region := "realland"] )) head(reported_cases) #> date confirm region #> <Date> <num> <char> #> 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. We also assign "testland" a different ascertainment of 50%, using the opts_list() function, which is used to assign region-specific settings.
obs <- opts_list( obs_opts(), reported_cases, testland = obs_opts(scale = Fixed(0.5)) ) estimates <- regional_epinow( data = reported_cases, generation_time = gt_opts(example_generation_time), delays = delay_opts(example_incubation_period + reporting_delay), rt = rt_opts(prior = LogNormal(mean = 2, sd = 0.2), rw = 7), obs = obs, gp = NULL, stan = stan_opts(cores = 4, warmup = 250, samples = 1000), logs = NULL )
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.
Summary measures that are returned include a table formatted for reporting (along with raw results for further processing).
knitr::kable(estimates$summary$summarised_results$table)
|Region |New infections per day |Expected change in reports |Effective reproduction no. |Rate of growth |Doubling/halving time (days) | |:--------|:----------------------|:--------------------------|:--------------------------|:-----------------------|:----------------------------| |realland |1675 (4 -- 3482) |Likely decreasing |0.78 (0.37 -- 1.1) |-0.062 (-0.11 -- 0.024) |-11 (28 -- -6.5) | |testland |3978 (2132 -- 8168) |Likely decreasing |0.85 (0.63 -- 1.2) |-0.042 (-0.11 -- 0.043) |-16 (16 -- -6.3) |
A range of plots are again returned (with the single summary plot shown below).
estimates$summary$summary_plot
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