anovir-package {anovir} | R Documentation |

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).

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.

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).

Philip AGNEW & Jimmy LOPEZ

Agnew P (2019) Estimating virulence from relative survival. bioRxiv: 530709 doi

Vignettes for examples of how to use and modify the functions in this package to estimate pathogen virulence

[Package *anovir* version 0.1.0 Index]