sf_none

Estimating the force of infection from time varying, age varying, or constant
serocatalytic models from population based seroprevalence studies using a Bayesian framework,
including data simulation functions enabling the generation of serological surveys based on this
models. This tool also provides a flexible prior specification syntax for the force of infection
and the seroreversion rate, as well as methods to assess model convergence and comparison
criteria along with useful visualisation functions.

Zulma M. Cucunubá

serofoi

Bayesian Estimation of the Force of Infection from Serological
Data

Nicolás T. Domínguez

Ben Lambert

Pierre Nouvellet

Geraldine Gómez

Jaime A. Pavlich-Mariscal

Hugo Gruson

David Santiago Quevedo

Miguel Gámez

Sumali Bajaj

Everlyn Kamau

Richard Creswell

International Development Research Center (IDRC) 

Pontificia Universidad Javeriana 

sf_none function

Sets empty prior distribution — sf_none

Sets empty prior distribution

sf_none: Sets empty prior distribution

Description

Usage

Arguments

Value