# Workflow for Rt estimation and forecasting

Source:`vignettes/estimate_infections_workflow.Rmd`

`estimate_infections_workflow.Rmd`

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*. The vignette uses
the default non-stationary Gaussian process model included in the
package. See other vignettes for a more thorough exploration of alternative model variants
and theoretical background.

## Data

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 R
package. An example data set called `example_confirm`

is
included in the package:

```
head(example_confirmed)
#> 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
```

Any estimation procedure is only as good as the data that feeds into
it. A thorough understanding of the data that is used for
*EpiNow2* and its limitations is a prerequisite for its use. This
includes but is not limited to biases in the population groups that are
represented (*EpiNow2* assumes a closed population with all
infections being caused by other infections in the same population),
reporting artefacts and delays, and completeness of reporting. Some of
these can be mitigated using the routines available in *EpiNow2*
as described below, but others will cause biases in the results and need
to be carefully considered when interpreting the results.

## Set up

We first load the *EpiNow2* package.

We then set the number of cores to use. We will want to run 4 MCMC chains in parallel so we set this to 4.

`options(mc.cores = 4)`

If we had fewer than 4 available or wanted to run fewer than 4 chains
(at the expense of some robustness), or had fewer than 4 computing cores
available we could set it to that. To find out the number of cores
available one can use the detectCores
function from the `parallel`

package.

## Parameters

Once a data set has been identified, a number of relevant parameters
need to be considered before using *EpiNow2*. As these will
affect any results, it is worth spending some time investigating what
their values should be.

### Delay distributions

*EpiNow2* works with different delays that apply to different
parts of the infection and observation process. They are defined using a
common interface that involves functions that are named after the
probability distributions, i.e. `LogNormal()`

,
`Gamma()`

, etc. For help with this function, see its manual
page

`?EpiNow2::Distributions`

In all cases, the distributions given can be *fixed*
(i.e. have no uncertainty) or *variable* (i.e. have associated
uncertainty). For example, to define a fixed gamma distribution with
mean 3, standard deviation (sd) 1 and maximum value 10, you would
write

```
Gamma(mean = 3, sd = 1, max = 10)
#> - gamma distribution (max: 10):
#> shape:
#> 9
#> rate:
#> 3
```

If distributions are variable, the values with uncertainty are
treated as prior probability
densities in the Bayesian inference framework used by
*EpiNow2*, i.e. they are estimated as part of the inference. For
example, to define a variable gamma distribution where uncertainty in
the mean is given by a normal distribution with mean 3 and sd 2, and
uncertainty in the standard deviation is given by a normal distribution
with mean 1 and sd 0.1, with a maximum value 10, you would write

```
Gamma(mean = Normal(3, 2), sd = Normal(1, 0.1), max = 10)
#> Warning in new_dist_spec(params, "gamma"): Uncertain gamma distribution
#> specified in terms of parameters that are not the "natural" parameters of the
#> distribution (shape, rate). Converting using a crude and very approximate
#> method that is likely to produce biased results. If possible, it is preferable
#> to specify the distribution directly in terms of the natural parameters.
#> - gamma distribution (max: 10):
#> shape:
#> - normal distribution:
#> mean:
#> 9
#> sd:
#> 2.5
#> rate:
#> - normal distribution:
#> mean:
#> 3
#> sd:
#> 1.4
```

Note the warning about parameters. We used the mean and standard deviation to define this distribution with uncertain parameters, but it would be better to use the “natural” parameters of the gamma distribution, shape and rate, for example using the values estimate and reported back after calling the previous command.

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 R
package. 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
R package and associated paper.

#### Generation intervals

The generation interval is a delay distribution that describes the
amount of time that passes between an individual becoming infected and
infecting someone else. In *EpiNow2*, the generation time
distribution is defined by a call to `gt_opts()`

, a function
that takes a single argument defined as a `dist_spec`

object
(returned by the function corresponding to the probability distribution,
i.e. `LogNormal()`

, `Gamma()`

, etc.). For example,
to define the generation time as gamma distributed with uncertain mean
centered on 3 and sd centered on 1 with some uncertainty, a maximum
value of 10 and weighted by the number of case data points we could use
the shape and rate parameters suggested above (though notes that this
will only very approximately produce the uncertainty in mean and
standard deviation stated there):

#### Reporting delays

*EpiNow2* calculates reproduction numbers based on the
trajectory of infection incidence. Usually this is not observed
directly. Instead, we calculate case counts based on, for example, onset
of symptoms, lab confirmations, hospitalisations, etc. In order to
estimate the trajectory of infection incidence from this we need to
either know or estimate the distribution of delays from infection to
count. Often, such counts are composed of multiple delays for which we
only have separate information, for example the incubation period (time
from infection to symptom onset) and reporting delay (time from symptom
onset to being a case in the data, e.g. via lab confirmation, if counts
are not by the date of symptom onset). In this case, we can combine
multiple delays with the plus (`+`

) operator, e.g.

```
incubation_period <- LogNormal(
meanlog = Normal(1.6, 0.05),
sdlog = Normal(0.5, 0.05),
max = 14
)
reporting_delay <- LogNormal(meanlog = 0.5, sdlog = 0.5, max = 10)
incubation_period + reporting_delay
#> Composite distribution:
#> - lognormal distribution (max: 14):
#> meanlog:
#> - normal distribution:
#> mean:
#> 1.6
#> sd:
#> 0.05
#> sdlog:
#> - normal distribution:
#> mean:
#> 0.5
#> sd:
#> 0.05
#> - lognormal distribution (max: 10):
#> meanlog:
#> 0.5
#> sdlog:
#> 0.5
```

In *EpiNow2*, the reporting delay distribution is defined by a
call to `delay_opts()`

, a function that takes a single
argument defined as a `dist_spec`

object (returned by
`LogNormal()`

, `Gamma()`

etc.). For example, if
our observations were by symptom onset we would use

`delay_opts(incubation_period)`

If they were by date of lab confirmation that happens with a delay
given by `reporting_delay`

, we would use

```
delay <- incubation_period + reporting_delay
delay_opts(delay)
```

#### Truncation

Besides the delay from infection to the event that is recorded in the data, there can also be a delay from that event to being recorded in the data. For example, data reported by symptom onset may only become part of the dataset once lab confirmation has occurred, or even a day or two after that confirmation. Statistically, this means our data is right-truncated. In practice, it means that recent data will be unlikely to be complete.

The amount of such truncation that exists in the data can be
estimated from multiple snapshots of the data, i.e. what the data looked
like at multiple past dates. One can then use methods that use the
amount of backfilling that occurred 1, 2, … days after data for a date
are first reported. In *EpiNow2*, this can be done using the
`estimate_truncation()`

method which returns, amongst others,
posterior estimates of the truncation distribution. For more details on
the model used for this, see the estimate_truncation vignette.

`?estimate_truncation`

In the `estimate_infections()`

function, the truncation
distribution is defined by a call to `trunc_opts()`

, a
function that takes a single argument defined as a
`dist_spec`

(either defined by the user or obtained from a
call to `estimate_truncation()`

or any other method for
estimating right truncation). This will then be used to correct for
right truncation in the data.

The separation of estimation of right truncation on the one hand and
estimation of the reproduction number on the other may be attractive for
practical purposes but is questionable statistically as it separates two
processes that are not strictly separable, potentially introducing a
bias. An alternative approach where these are estimated jointly is being
implemented in the epinowcast package, which is
being developed by the *EpiNow2* developers with
collaborators.

### Completeness of reporting

Another issue affecting the progression from infections to reported
outcomes is underreporting, i.e. the fact that not all infections are
reported as cases. This varies both by pathogen and population (and
e.g. the proportion of infections that are asymptomatic) as well as the
specific outcome used as data and where it is located on the severity
pyramid (e.g. hospitalisations vs. community cases). In *EpiNow2*
we can specify the proportion of infections that we expect to be
observed (with uncertainty assumed represented by a truncated normal
distribution with bounds at 0 and 1) using the `scale`

argument to the `obs_opts()`

function. For example, if we
think that 40% (with standard deviation 1%) of infections end up in the
data as observations we could specify.

### Initial reproduction number

The default model that `estimate_infections()`

uses to
estimate reproduction numbers requires specification of a prior
probability distribution for the initial reproduction number. This
represents the user’s initial belief of the value of the reproduction
number, where there is no data yet to inform its value. By default this
is assumed to be represented by a lognormal distribution with mean and
standard deviation of 1. It can be changed using the
`rt_opts()`

function. For example, if the user believes that
at the very start of the data the reproduction number was 2, with
uncertainty in this belief represented by a standard deviation of 1,
they would use

### Weighing delay priors

When providing uncertain delay distributions one can end up in a
situation where the estimated means are shifted a long way from the
given distribution means, and possibly further than is deemed realistic
by the user. In that case, one could specify narrower prior
distributions (e.g., smaller `mean_sd`

) in order to keep the
estimated means closer to the given mean, but this can be difficult to
do in a principled manner in practice. As a more straightforward
alternative, one can choose to weigh the generation time priors by the
number of data points in the case data set by setting
`weigh_delay_priors = TRUE`

(the default).

## Estimation and forecasting

All the options are combined in a call to the
`estimate_infections()`

function. For example, using some of
the options described above one could call

```
def <- estimate_infections(
example_confirmed,
generation_time = gt_opts(generation_time),
delays = delay_opts(delay),
rt = rt_opts(prior = rt_prior)
)
#> Warning: There were 3 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
```

Alternatively, for production environments, we recommend using the
`epinow()`

function. It uses
`estimate_infections()`

internally and provides functionality
for logging and saving results and plots in dedicated directories in the
user’s file system.

### Forecasting secondary outcomes

The `estimate_infections()`

function works with a single
time series of outcomes such as cases by symptom onset or
hospitalisations. Sometimes one wants to further create forecasts of
other secondary outcomes such as deaths. The package contains
functionality to estimate the delay and scaling between multiple time
series with the `estimate_secondary()`

function, as well as
for using this to make forecasts with the
`forecast_secondary()`

function.

## Interpretation

To visualise the results one can use the `plot()`

function
that comes with the package

`plot(def)`

The results returned by the `estimate_infections`

model
depend on the values assigned to all to parameters discussed in this
vignette, i.e. delays, scaling, and reproduction numbers, as well as the
model variant used and its parameters. Any interpretation of the results
will therefore need to bear these in mind, as well as any properties of
the data and/or the subpopulations that it represents. See the Model options vignette for
an illustration of the impact of model choice.