nowcaster: Statistical Models for Notification Delay Correction of
Epidemiological Data. The package wraps statistical models to estimate
not yet reported data using empirical delay distribution either from
individual-level data or from the differences of aggregated time series
of cases. nowcaster it was built for and during epidemiological
emergency use, it was constructed for the Brazilian Severe Acute
Respiratory Illness (SARI) surveillance system (SIVEP-Gripe), at the
time of Covid-19 pandemic.
Every single system of notification has an intrinsic delay between the
date of onset of the event and the date of report. nowcaster can
estimate how many counts of any epidemiological data of interest
(i.e., daily cases and deaths counts) by fitting a negative binomial
model to the time steps of delay between onset date of the event,
(i.e., date of first symptoms for cases or date of occurrence of
death) and the date of report (i.e., date of notification of the case
or death).
You can install direct from CRAN:
install.packages("nowcaster")
Or you can install the developing version directly from GitHub:
devtools::install_github("https://github.com/covid19br/nowcaster")
If you have any problem installing, please refer to next section on the dependencies of the package.
nowcaster is based on the
R-INLA and
mgcv.
Please make sure you have stable version of those packages to be able to
fully work with nowcaster
Installing INLA:
install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)
Installing mgcv:
install.packages("mgcv")
There are other alternative packages, that can produce nowcasting estimation, here it is some options: