List. Parameters for daily report inference via MCMC. Elements include:
-
method
: String. Method name to infer the daily incidence reports from aggregated ones.
Either linear
or renewal
is currently implemented.
The linear
method simply performs a linear interpolation that matches the aggregated values.
The renewal
method fits a SIR-like model using a renewal equation to infer the daily incidence.
In this case, the fitting algorithm is a Markov Chain Monte Carlo (MCMC) implemented in JAGS
and needs the parameters below (e.g., burn,iter,chains,...
).
The renewal
method is more adapted for short single wave epidemics as this models
i) naturally fits a single wave and ii) has longer computing time.
For longer time series, user may perfer the linear
method.
-
popsize
: Integer. Population size to use in MCMC simulation to infer daily observations from aggregated input data.
-
burn
: Numeric. Length of burn-in period (number of days).
-
iter
: Numeric. Number of iterations after burn-in period (number of days).
-
chains
: Numeric. Number of chains to simulate.
-
prior_R0_shape
: Shape of the (hyper-)parameter for the prior Gamma distribution for R0.
-
prior_R0_rate
: Rate of the (hyper-)parameter for the prior Gamma distribution for R0.
-
prior_alpha_shape
: Shape of the (hyper-)parameter for the prior Gamma distribution for alpha.
-
prior_alpha_rate
: Rate of the (hyper-)parameter for the prior Gamma distribution for alpha.
-
first.agg.period
: length of aggregation period for first aggregated observation (number of days); if NULL, assume same aggregation period as observed for second observation (gap between first and second observations)