Using epinow() for running in production mode

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. This is done with the epinow() function, that takes the same options as estimate_infections() with some additional options that determine, for example, where output gets stored and what output exactly. The function can be a useful option when, e.g., running the model daily with updated data on a high-performance computing server to feed into a dashboard. For more detail on the various model options available, see the Examples vignette, for more on the general modelling approach the Workflow, and for theoretical background the Model definitions vignette

Running the model on a single region

To run the model in production mode for a single region, set the parameters up in the same way as for estimate_infections() (see the Workflow vignette). Here we use the example delay and generation time distributions that come with the package. This should be replaced with parameters relevant to the system that is being studied.

library("EpiNow2")
#> 
#> 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)
delay <- example_incubation_period + reporting_delay
rt_prior <- LogNormal(mean = 2, sd = 1)

We can then run the epinow() function with the same arguments as estimate_infections().

res <- epinow(reported_cases,
  generation_time = gt_opts(example_generation_time),
  delays = delay_opts(delay),
  rt = rt_opts(prior = rt_prior),
  logs = NULL
)
plot(res)
plot of chunk epinow
plot of chunk epinow

By default, logging messages are printed to the console indicating the progress of the model fitting and where log files can be found. Here we have set logs = NULL to suppress these messages. 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

The package also contains functionality to conduct inference contemporaneously (if separately) in production mode on multiple time series, e.g. to run the model on multiple regions. This is done with the regional_epinow() function.

Say, for example, we construct a dataset containing two regions, testland and realland (in this simple example both containing the same case data).

cases <- example_confirmed[1:60]
cases <- data.table::rbindlist(list(
  data.table::copy(cases)[, region := "testland"],
  cases[, region := "realland"]
 ))

To then run this on multiple regions using the default options above, we could use

region_rt <- regional_epinow(
  data = cases,
  generation_time = gt_opts(example_generation_time),
  delays = delay_opts(delay),
  rt = rt_opts(prior = rt_prior),
  logs = NULL
)
## summary
region_rt$summary$summarised_results$table
#>      Region New infections per day Expected change in reports
#>      <char>                 <char>                     <fctr>
#> 1: realland    2237 (1380 -- 3751)          Likely decreasing
#> 2: testland    2226 (1344 -- 3648)          Likely decreasing
#>    Effective reproduction no.          Rate of growth
#>                        <char>                  <char>
#> 1:         0.89 (0.71 -- 1.1) -0.03 (-0.095 -- 0.041)
#> 2:          0.89 (0.7 -- 1.1) -0.03 (-0.097 -- 0.039)
#>    Doubling/halving time (days)
#>                          <char>
#> 1:             -23 (17 -- -7.3)
#> 2:             -23 (18 -- -7.1)
## plot
region_rt$summary$plots$R
plot of chunk regional_epinow
plot of chunk regional_epinow

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.

gp <- opts_list(gp_opts(), cases)
gp <- modifyList(gp, list(realland = NULL), keep.null = TRUE)
rt <- opts_list(rt_opts(), cases, realland = rt_opts(rw = 7))
region_separate_rt <- regional_epinow(
  data = cases,
  generation_time = gt_opts(example_generation_time),
  delays = delay_opts(delay),
  rt = rt, gp = gp,
  logs = NULL
)
## summary
region_separate_rt$summary$summarised_results$table
#>      Region New infections per day Expected change in reports
#>      <char>                 <char>                     <fctr>
#> 1: realland    2006 (1070 -- 4068)          Likely decreasing
#> 2: testland    2252 (1414 -- 3790)          Likely decreasing
#>    Effective reproduction no.          Rate of growth
#>                        <char>                  <char>
#> 1:         0.85 (0.61 -- 1.2) -0.042 (-0.11 -- 0.042)
#> 2:          0.9 (0.72 -- 1.1) -0.027 (-0.09 -- 0.038)
#>    Doubling/halving time (days)
#>                          <char>
#> 1:             -17 (17 -- -6.3)
#> 2:             -25 (18 -- -7.7)
## plot
region_separate_rt$summary$plots$R
plot of chunk regional_epinow_multiple
plot of chunk regional_epinow_multiple