Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. An overview of the implemented continuous-time individual level models for epidemics is given by Almutiry and Deardon (2019) <doi:10.1515/ijb-2017-0092>.

Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. <doi:10.18637/jss.v098.i10>.

Estimation of Time Varying Reproduction Numbers and Restored
Incidence Curves Using Variational Techniques
c(person("Luis", "Alvarez", email = "lalvarez@ulpgc.es", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-8443-9162")),
person("Jean-David", "Morel", role = "ctb", comment = c(ORCID = "0000-0002-7122-9924")),
person("Jean-Michel", "Morel", role = "ctb", comment = c(ORCID = "0000-0002-6108-897X"))
)
Estimation,  by inverting a renewal equation,
of time-varying reproduction numbers and restored incidence curves with
festive days and weekly biases corrected as described in
Alvarez et al. (2021) <doi:10.1073/pnas.2105112118> and
Alvarez et al. (2022) <doi:10.3390/biology11040540>.
knitr, ggplot2, grid, dplyr, testthat (>= 3.0.0), rmarkdown

c(person("Luis", "Alvarez", email = "lalvarez@ulpgc.es", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6953-9587")),
person("Jean-David", "Morel", role = "ctb", comment = c(ORCID = "0000-0002-7122-9924")),
person("Jean-Michel", "Morel", role = "ctb", comment = c(ORCID = "0000-0002-6108-897X"))
)

Estimation,  by inverting a renewal equation,
of time-varying reproduction numbers and restored incidence curves with
festive days and weekly biases corrected as described in
Alvarez et al. (2021) <doi:10.1073/pnas.2105112118> and
Alvarez et al. (2022) <doi:10.3390/biology11040540>. EpiInvert can manage
daily incidence data and weekly aggregated incidence data. This version of the
package also includes EpiInvertForecast, a learning method for the short time
forecast of the restored incidence curve.