Correlation of Influenza Virus Excess Mortality with Antigenic Variation: Application to Rapid Estimation of Influenza Mortality Burden

Aiping Wu, Yousong Peng, Xiangjun Du, Yuelong Shu and Taijiao Jiang

PLOS Computational Biology, 2010, vol. 6, issue 8, 1-10

Abstract: The variants of human influenza virus have caused, and continue to cause, substantial morbidity and mortality. Timely and accurate assessment of their impact on human death is invaluable for influenza planning but presents a substantial challenge, as current approaches rely mostly on intensive and unbiased influenza surveillance. In this study, by proposing a novel host-virus interaction model, we have established a positive correlation between the excess mortalities caused by viral strains of distinct antigenicity and their antigenic distances to their previous strains for each (sub)type of seasonal influenza viruses. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. Rapid estimation of influenza mortality burden for new seasonal strains should help formulate a cost-effective response for influenza control and prevention.Author Summary: In epidemiology, investigators usually rely on surveillance data to assess the impact of an influenza virus on human health. However, accurate assessment of the influenza mortality burden at the early stage of influenza infection is rather challenging because the early influenza surveillance data are very limited and prone to bias as well. This speaks to an urgent need for the development of a more effective method for rapid and accurate estimation of influenza mortality burden. By proposing a novel host-virus interaction model, we have established a quantitative relationship between the antigenic variation of human influenza virus and its mortality burden. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. We believe that our work will help develop a timely and sensible influenza preparedness programme that balances the gains of public health with the social and economic costs.

Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1000882

DOI: 10.1371/journal.pcbi.1000882

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