Bayesian inference of antigenic and non-antigenic variables from haemagglutination inhibition assays for influenza surveillance.

Autor: Adabor ES; Research Centre, African Institute for Mathematical Sciences, Cape Town, South Africa.; Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa., Ndifon W; Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa.; Research Department, African Institute for Mathematical Sciences, Next Einstein Initiative, Kigali, Rwanda.
Jazyk: angličtina
Zdroj: Royal Society open science [R Soc Open Sci] 2018 Jul 25; Vol. 5 (7), pp. 180113. Date of Electronic Publication: 2018 Jul 25 (Print Publication: 2018).
DOI: 10.1098/rsos.180113
Abstrakt: Haemagglutination inhibition (HI) assays are typically used for comparing and characterizing influenza viruses. Data obtained from the assays (titres) are used quantitatively to determine antigenic differences between influenza strains. However, the use of these titres has been criticized as they sometimes fail to capture accurate antigenic differences between strains. Our previous analytical work revealed how antigenic and non-antigenic variables contribute to the titres. Building on this previous work, we have developed a Bayesian method for decoupling antigenic and non-antigenic contributions to the titres in this paper. We apply this method to a compendium of HI titres of influenza A (H3N2) viruses curated from 1968 to 2016. Remarkably, the results of this fit indicate that the non-antigenic variable, which is inversely correlated with viral avidity for the red blood cells used in HI assays, oscillates during the course of influenza virus evolution, with a period that corresponds roughly to the timescale on which antigenic variants replace each other. Together, the results suggest that the new Bayesian method is applicable to the analysis of long-term dynamics of both antigenic and non-antigenic properties of influenza virus.
Competing Interests: The authors have no competing interests.
Databáze: MEDLINE