EpiLPS (an acronym for Epidemiological modeling with Laplacian-P-Splines) is a tool aiming at exploring the synergy between Laplace approximations and Bayesian P-splines to reach state-of-the art methodological advancements in statistical modeling of infectious diseases. EpiLPS grew up originally as a method to estimate the time-varying reproduction number [1] Gressani et al. (2022). Recent extensions to nowcasting ([2] Sumalinab et al. 2024) and to estimation of incubation times ([3] Gressani et al. 2024) transformed EpiLPS into a small inferential ecosystem of statistical methods that deliver fast and accurate estimates of key epidemiological characteristics.
The aim of this website is to give a short overview of the functionalities of EpiLPS, mainly through documentation and vignettes. Recent updates will be added to the in-development version of the package available on this GitHub repository. The stable version can be found on CRAN.
New methodology to estimate the distribution of incubation times in a semi-parametric way. A small vignette illustrates how to use the underlying routines. Article available open access in the American Journal of Epidemiology 10.1093/aje/kwae192.
October 2024
The EpiLPS package is ready to nowcast the reproduction number. Read this vignette to get started. Related paper is available open access in Epidemiology 10.1097/ede.0000000000001744.
October 2024
A new routine for nowcasting has been added to EpiLPS. Discover what you can do with it here. You can also read our recent article available open access in the Journal of Computational and Graphical Statistics 10.1080/10618600.2024.2395414.
October 2024[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] Sumalinab, B., Gressani, O., Hens, N. and Faes, C. (2024). Bayesian nowcasting with Laplacian-P-splines. Journal of Computational and Graphical Statistics (Accepted manuscript version). 10.1080/10618600.2024.2395414
[3] Gressani, O., Torneri, A., Hens, N. and Faes, C. (2024). Flexible Bayesian estimation of incubation times. American Journal of Epidemiology (Accepted manuscript version). 10.1093/aje/kwae192
[4] Sumalinab, B., Gressani, O., Hens, N. and Faes, C. (2024). An efficient approach to nowcasting the time-varying reproduction number. Epidemiology, 35(4):512-516. 10.1097/ede.0000000000001744
