The EpiEstim package has been extended to allow users to
estimate the effective transmission advantage of new
pathogens/variants/strains compared to a reference
pathogen/variant/strain in real-time. This vignette will take you
through the key stages of estimating this advantage and also provide an
example of how this method can be applied in a typical outbreak analysis
workflow. Please also see the ‘FAQs’ section.
The incidence data needs to be supplied in the form of a multidimensional array, containing the incidence for each day of the outbreak (row), location (column) and variant/strain under investigation (third dimension), e.g.:
|
|
The serial interval distributions must be supplied as a matrix with the number of columns matching the number of variants being considered. Each column should contain the probability mass function (PMF) for the discrete serial interval of each variant, starting with the PMF for day zero in the first row (which should be 0) and each column should sum to 1, e.g.:
| Variant_1 | Variant_2 | |
|---|---|---|
| [1,] | 0 | 0 |
| [2,] | 0.1 | 0.2 |
| [3,] | 0.3 | 0.4 |
| [4,] | 0.5 | 0.6 |
| … | … | … |
##Estimate advantage
To estimate the effective transmission advantage of new variants
compared to a reference variant we use the
estimate_advantage() function in EpiEstim.
estimate_advantage(incid = incidence_object,
si_distr = si_matrix,
priors = default_priors(),
mcmc_control = default_mcmc_controls(),
t_min = NULL, t_max = nrow(incid),
incid_imported = NULL,
precompute = TRUE,
reorder_incid = TRUE)As described above, the incid= and
si_distr= arguments should be supplied with a
multidimensional array of incidence and a matrix containing the serial
interval for each variant respectively. In addition, the user can
supply:
priors= with a list of the prior parameters (shape
and scale) for the reproduction number and the transmission advantage.
The default priors can be obtained using the
default_priors() function.
mcmc_control= with a list of the default properties
of the MCMC, i.e. the number of iterations, burn-in and the thinning of
the MCMC chains. Burn-in is the number of iterations to be discarded as
“warm-up”, for instance, if burnin=10 the output is only recorded after
10 iterations have run. Thinning determines how much the MCMC chains
should be thinned out, if thin = 10 then 1 in every 10 iterations will
be kept. The default parameters can be obtained using
default_mcmc_controls().
t_min= and t_max= with integers that
give the minimum and maximum time step over which the transmission
advantage will be estimated. Note that cases before t_min
will still be accounted for as potential infectors in the likelihood.
Indeed, cases before t_min can contribute to the overall
infectiousness from t_min to t_max through the serial interval
distribution. t_min must be >1 and if
t_min = NULL then this is automatically calculated as the
maximum of the 95th percentile of the SI distribution.
t_max must be > t_min and <= the number
of rows of the incidence data. The default value of t_max
is nrow(incid).
Other optional parameters include:
incid_imported= where a multidimensional array can
be supplied (row = time, column = location, third dimension = variant)
containing the incidence of imported cases, such that
incid - incid_imported would be the incidence of locally
infected cases. If incid_imported = NULL it will be assumed
that (other than in the first time step) all cases arose from local
transmission.
precompute= which can be TRUE or
FALSE, but defaults to TRUE. This determines
whether the shape of the posterior distributions for R and the
transmission advantage (for the non-reference variant(s)) for each time
step and location should be precalculated. This makes
estimate_advantage() faster, only use
precompute=FALSE for debugging.
reorder_incid= which can be TRUE or
FALSE, but defaults to TRUE. If
TRUE then during the estimation of the transmission
advantage the incidence array will be temporarily re-ordered so that all
variants are compared to the most transmissible variant (temporarily
considered the reference) regardless of the order in which the user
supplied the data. Note that the results will always be returned in the
order supplied by the user. We recommend that this is set to
TRUE.
This example will take you through a workflow using incidence data for two variants of SARS-CoV-2 (wildtype and alpha) across 7 NHS regions in England. (For detailed description of the data see Bhatia et al. 2021)
incid <- readRDS("./data_mv_vignette/incid.rds")Incidence
Let us say we have incidence data in the format below, where each row corresponds to a time step in the outbreak, each column corresponds to a region in England, and each third dimension corresponds to a variant.
head(incid)
#> , , wild
#>
#> East of England London Midlands North East and Yorkshire North West
#> [1,] 7 19 28 21 37
#> [2,] 8 20 44 30 48
#> [3,] 10 19 43 30 48
#> [4,] 10 23 52 30 58
#> [5,] 8 23 46 36 53
#> [6,] 6 19 32 14 37
#> South East South West
#> [1,] 7 8
#> [2,] 10 7
#> [3,] 11 6
#> [4,] 10 6
#> [5,] 12 5
#> [6,] 13 2
#>
#> , , alpha
#>
#> East of England London Midlands North East and Yorkshire North West
#> [1,] 0 0 1 0 0
#> [2,] 0 1 1 0 1
#> [3,] 0 0 1 0 0
#> [4,] 1 0 1 0 0
#> [5,] 0 0 0 0 0
#> [6,] 0 0 0 0 1
#> South East South West
#> [1,] 0 0
#> [2,] 0 0
#> [3,] 1 1
#> [4,] 0 0
#> [5,] 0 0
#> [6,] 0 0We also assume that the SI is the same for both variants, with a mean of 5.4 days and a standard deviation of 1.5 days (Rai, Shukla, and Dwivedi 2021) and produce a matrix of SI’s with the number of columns equal to the number of variants:
mean_SI <- 5.4
sd_SI <- 1.5
SI <- EpiEstim::discr_si(seq(0, 20), mean_SI, sd_SI)
si_matrix <- cbind(SI,SI)
head(si_matrix)
#> SI SI
#> [1,] 0.000000e+00 0.000000e+00
#> [2,] 4.674689e-05 4.674689e-05
#> [3,] 8.034951e-03 8.034951e-03
#> [4,] 7.964056e-02 7.964056e-02
#> [5,] 2.125381e-01 2.125381e-01
#> [6,] 2.683362e-01 2.683362e-01Estimate transmission advantage
Now that we have our incidence and SI distributions we can supply
these to the estimate_advantage() function. We use the
default priors and MCMC controls.
output <- EpiEstim::estimate_advantage(incid = incid,
si_distr = si_matrix,
priors = default_priors(),
mcmc_control = default_mcmc_controls())The output is a list containing 4 elements:
output$epsilon
output$RIn this example, we did not specify t_min= or
t_max=. You will notice that the R estimates do not start
until day 25, this is because: 1) One of the regions doesn’t have cases
of the alpha variant until day 16, 2) The 95th percentile of the SI is 9
days.
output$convergence
output$diagcoda). The length of diag is equal to the number of
rows in epsilon (i.e. the number of non-reference variants, in this case
only 1). Each element of diag contains a named list of the point
estimate and upper confidence limits.Summarise results
To summarise the estimated transmission advantage one can use the posterior median and 95% CrI as follows.
estimate_advantage() be interpreted with
caution?The inference framework jointly estimates the instantaneous
reproduction number of the reference variant and the effective
transmission advantage of new variants. R estimates here should be
interpreted with caution because they represent estimates from the joint
distribution of the reproduction number and transmission advantage,
therefore depending on the incidence of the reference as well
as the new variant(s). Moreover, temporal variation in estimates of
the instantaneous reproduction number can arise from a number of
factors, such as the implementation of control measures, changes in
population behaviour, or variability in the reporting of cases over
time. If only interested in estimating the reference Rt, we
recommend using estimate_R().
"The Gelman-Rubin algorithm suggests the MCMC may not have converged within the number of iterations specified."This means that the MCMC chains for the estimation have not
converged. To avoid this you may need to run
estimate_advantage() with more MCMC iterations. E.g. by
altering mcmc_control().
"Input SI distributions should sum to 1. Normalising now"The SI distributions supplied have been re-normalised so that they sum to 1.