R/anovir-package.R

In anovir: Analysis of Virulence

#' @description
#' Epidemiological population dynamics models traditionally define a
#' pathogen's virulence as the increase in the per capita rate of mortality
#' of infected hosts due to infection.
#' The ANOVIR package provides functions allowing virulence to be estimated
#' by maximum likelihood techniques.
#' The approach is based on the analysis of relative survival applied to
#' the comparison of survival in matching cohorts of experimentally-infected
#' vs. uninfected hosts (Agnew 2019).
#'
#' @details
#'
#' The analysis of relative survival is a statistical method for estimating
#' excess mortality.
#' Excess mortality occurs when a target population experiences greater
#' mortality than would be expected for a given period of time.
#' Here excess mortality is estimated in the context of emprical studies
#' where survival in populations of experimentally infected hosts is
#' compared to that in matching populations of uninfected, or control, hosts.
#' In this context the relative survival approach assumes the
#' rate of mortality observed in the infected treatment arises as the sum of
#' two independent and mutually exclusive sources of mortality,
#' (i) the 'natural' or background rate of mortality, and
#' (ii) an addition rate of mortality due to infection.
#'
#' The background rate of mortality is the expected rate of mortality hosts in
#' the infected treatment would have experienced had they not been
#' exposed to infection; it is estimated from mortality
#' observed in the matching uninfected control treatment.
#' When there is background mortality, the rate of mortality of infected
#' hosts due to infection is not directly observed;
#' however it can be estimated from the difference in mortality observed
#' for an infected treatment and that observed in a matching control treatment.
#'
#' The two sources of mortality assumed in the relative survival approach,
#' and the additive effect of their rates for infected hosts, is also found
#' in the population dynamics models on which most epidemiological theory
#' is based.
#' The additional rate of mortality of infected hosts due to
#' infection is generally how these models define pathogen virulence.
#'
#'
#' @author Philip AGNEW & Jimmy LOPEZ
#' @references
#' Agnew P (2019) Estimating virulence from relative survival. bioRxiv: 530709
#' \href{https://doi.org/10.1101/530709}{doi}
#' @section Acknowledgements:
#' PA is grateful to the following people;
#'
#' - Yannis MICHALAKIS for affording me the time and
#' space at work to develop this project
#'
#' - Simon BLANFORD and Matthew THOMAS for generously providing the data from
#' their 2012 study and allowing it to be made publically available
#'
#' - Célia TOURAINE at the Institut du Cancer de Montpellier (ICM)
#' for reading the original manuscript and validating the general
#' approach of analysing relative survival as a means to
#' estimate virulence
#'
#' - Eric ELGUERO for useful input during discussions on models for
#' recovery from infection and estimating confidence intervals
#'
#' - François ROUSSET for helpful technical advice concerning R
#'
#' This work was funded by basic research funds from the French research
#' agencies of the Centre National de la Recherche Scientifique (CNRS)
#' and the Institut de Recherche pour le Développement (IRD).
#'
#' @seealso Vignettes for examples of how to use and modify
#'  the functions in this package to estimate pathogen virulence
"_PACKAGE"