EpiLPS (an acronym for Epidemiological modeling with Laplacian-P-Splines) is a tool for fast and flexible Bayesian estimation of epidemiological parameters. It can be used (among others) to estimate the epidemic curve, the instantaneous reproduction number \(R_t\) and the incubation period of an infectious disease. Recent developments also permit nowcasting.

The core methodology behind the R package can be found in [1] Gressani et al. 2022. The aim of this website is to give a short overview of the functionalities of EpiLPS.

The in-development version of the package is available on this GitHub repository. The stable version is available on CRAN .



Virus

Estimation of incubation times

The EpiLPS package has been extended to deal with estimation of incubation times. A small vignette illustrates how to use the new routines. Read our preprint on MedRxiv for further details.

March 2024
Particles

EpiLPS 1.3.0 available on CRAN

Version 1.3.0 of EpiLPS available on CRAN.

March 2024
Virus

Nowcasting the reproduction number

The EpiLPS package is ready to nowcast the reproduction number. Read this vignette to get started and our article for further details.

March 2024
Particles

Nowcasting available in EpiLPS

A new routine for nowcasting has been added to EpiLPS. Discover what you can do with it here.

March 2024

Associated literature

[1] Gressani O, Wallinga J, Althaus CL, Hens N, Faes C (2022) EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number. PLoS Comput Biol 18(10): e1010618. 10.1371/journal.pcbi.1010618

[2] Gressani, O., Torneri, A., Hens, N. and Faes, C. (2023). Flexible Bayesian estimation of incubation times. MedRxiv preprint. 10.1101/2023.08.07.23293752

[3] Sumalinab, B., Gressani, O., Hens, N. and Faes, C. (2023). Bayesian nowcasting with Laplacian-P-splines. MedRxiv preprint. 10.1101/2022.08.26.22279249v2

[4] Sumalinab, B., Gressani, O., Hens, N. and Faes, C. (2023). An efficient approach to nowcasting the time-varying reproduction number. MedRxiv preprint. 10.1101/2023.10.30.23297251


Rocket

12K +

Total package downloads

Paper

2700 +

Related article views