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EpiLPS: a fast and flexible Bayesian tool for estimating epidemiological parameters

Oswaldo Gressani (oswaldo.gressani@uhasselt.be)

EpiLPS (Gressani et al. 2022) is an Epidemiological modeling tool using Laplacian-P-Splines. It can be used to estimate (key) epidemiological parameters such as the time-varying reproduction number (the average number of secondary cases generated by an infected individual) or the incubation period of an infectious disease. It can also be used for nowcasting. The website associated to the project https://epilps.com/ provides a detailed documentation (or vignette) on how to use the underlying routines.

Citation

Associated literature:

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

  2. Gressani, O., Torneri, A., Hens, N. and Faes, C. (2023). Flexible Bayesian estimation of incubation times. MedRxiv preprint. https://www.medrxiv.org/content/10.1101/2023.08.07.23293752v1.

  3. Sumalinab, B., Gressani, O., Hens, N. and Faes, C. (2023). Bayesian nowcasting with Laplacian-P-splines. MedRxiv preprint. https://www.medrxiv.org/content/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. https://doi.org/10.1101/2023.10.30.23297251

To cite the EpiLPS package in publications starting from version 1.2.0 use:

Gressani, O. (2021). EpiLPS: A Fast and Flexible Bayesian Tool for 
Estimating Epidemiological Parameters. [Computer Software]. https://epilps.com/

For versions before 1.2.0 use:

Gressani, O. (2021). EpiLPS: A Fast and Flexible Bayesian Tool for 
Estimation of the Time-varying Reproduction Number. [Computer Software]. https://epilps.com/

Package version

This is version 1.3.0 (2024-03-07) - “Extension to nowcasting”.

Acknowledgments

This project is funded by the European Union’s Research and Innovation Action under the H2020 work programme, EpiPose (grant number 101003688). It is also supported by the ESCAPE project (101095619) and the VERDI project (101045989), funded by the European Union.

License

EpiLPS: a fast and flexible Bayesian tool for estimating epidemiological parameters. Copyright (C) 2021-2024 Oswaldo Gressani.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

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Version

Install

install.packages('EpiLPS')

Monthly Downloads

330

Version

1.3.0

License

GPL-3

Maintainer

Oswaldo Gressani

Last Published

March 8th, 2024

Functions in EpiLPS (1.3.0)

incubsim

Simulation of incubation times
perfRestim

Routine to measure the performance of estimR and estimRmcmc
plot.nowcasted

Plot of nowcasted cases and delay distribution
summary.Rt

Summarize the estimated reproduction number
zika2015

Data on the 2015 Zika virus disease in Colombia
sars2003

Daily incidence of the 2003 SARS epidemic in Hong Kong
cov19mort2021

Mortality data for Belgium in 2021
eruptions

Eruption times in Yellowstone National Park
Idist

Density function and discrete distribution for a disease interval
Rmodelpriors

Prior specification for model hyperparameters
epicurve

Plot the epidemic curve
episim

Simulation of an incidence time series
estimIncub

Estimation of the incubation density based on coarse data
plot.Idist

Plot the interval distribution from an Idist object
estimRmcmc

Estimation of the reproduction number with Laplacian-P-splines via MCMC
estimR

Estimation of the reproduction number with Laplacian-P-splines
plot.Rt

Plot the estimated reproduction number
influenza2009

Data on the 2009 pandemic influenza in Pennsylvania
histosmooth

Histogram smoothing with Laplacian-P-splines
nowcastingR

Nowcasting the reproduction number
cov19incidence2022

Incidence data for Belgium in 2022
nowcasting

Nowcasting and estimation of occurred but not yet reported events
plot.Rtnow

Plot the nowcasted reproduction number
plot.incubestim

Plot the estimated incubation distribution