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Title: Correlation of Influenza Virus Excess Mortality with Antigenic Variation: Application to Rapid Estimation of Influenza Mortality Burden}
Article:
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.
Introduction:
Seasonal influenza viruses have been and will continue to be a significant threat to public health[1]. There are three types of seasonal influenza – A, B and C. Among them, influenza H1N1 and H3N2 of type A and type B are currently circulating frequently in the human population. During evolution, many variants of influenza virus with distinct antigenic properties have emerged either by mutation or reassortment of the gene encoding the viral coat protein hemagglutinin( HA)[2]. These antigenic variants have caused morbidity and mortality with varying magnitude[3],[4]. If the mortality burden of a newly emerged virus can be estimated accurately and in time, this knowledge will be extremely valuable not only for public preparedness for an impending epidemic/pandemic but also for the health authorities to develop cost-effective control and intervention strategies. In epidemiology, investigators usually rely on the surveillance data to assess the impact of an influenza virus on human death by estimating its case fatality ratio( CFR), the ratio of the number of deaths caused by the virus to the number of the diagnosed cases of the virus infection[5], and the excess mortality it causes[6],[7]. However, accurate prediction of the influenza mortality burden at the early stage of influenza infection is rather challenging, because the morbidity and mortality data for early influenza surveillance are very limited and prone to bias as well[5]. This speaks to an urgent need for the development of a more effective method for rapid and accurate estimation of the mortality burden of influenza virus. It is generally assumed that the extent of an influenza virus to alter its antigenicity and to escape the pre-existing immunity in the human population determines its intensity of infection at the population level[8],[9] and thus the mortality burden. Despite the obvious causal relationship between the antigenic variations of influenza viruses and their mortality burdens, to our knowledge, no report has ever established a direct and positive correlation between them. In this study, based on the viral and mortality data of influenza in the USA and by using the excess all-cause mortality to represent the impact of influenza epidemics on human death[6], we describe our work on how to establish a positive correlation between the antigenic variation of human influenza virus and the total excess mortality it causes during all periods of its circulation. The established relationship has further enabled us to develop a method to rapidly estimate the mortality burden of influenza A( H1N1) virus by accurately predicting the antigenic distance between A( H1N1) strains based on their HA sequences.
Discussion:
In this study, by proposing a simplified virus-host interaction model, we have discovered a direct and positive correlation between the extent of antigenic variation of an influenza virus and the total excess mortality it may cause. The impact of influenza on human death has been a long-standing focus of influenza studies. Many factors have been thought to contribute to influenza mortality burden including viral factors such as the pathogenicity-related molecular markers[16], receptor binding[17] and antigenic variation of the influenza virus[8], as well as many external factors such as age structure of the human population, effectiveness of influenza vaccine, temperature and humidity[18]–[20]. Despite these many potential contributing factors, we found a significant correlation between the extent of antigenic variation of an influenza virus and its impact on total excess mortality. This relationship is valid for all three human influenza viruses analyzed: A( H1N1), A( H3N2) and B. Recently, Park et al[21] also found a positive correlation between the probability of being infectious and the number of amino acid differences in antigenic sites of HA based on their experiments on equine influenza virus. Therefore we argue that for an influenza virus that has been adapted in its host, the antigenic variation is the most important factor that determines its mortality burden. Although somewhat surprising, this is reasonable because the human-adapted influenza viruses like seasonal viruses generally have considerable changes only in antigenicity but not in the pathogenicity and receptor specificity. A major challenge in correlation analysis of the antigenic variation of influenza virus and its mortality burden is to attribute the excess mortality to a specific influenza strain. Although the excess mortality attributed to all influenza viruses in a given period of time can be inferred with high confidence from the mortality data reported[6],[7], further attribution of the excess mortality to a specific strain based on current limited surveillance data is very difficult. In this study, we estimated the excess mortality caused by a virus based on its virus isolation rate, the proportion of clinical samples positive that was reported for the virus in the given period of time( see Materials and Methods in detail). The virus isolation rate from clinics reflects the virus's ability to infect and its ability to cause disease as well because people with disease are more likely to go to clinic for viral testing than those without. For the same( sub) type of virus, the ability to cause disease may reflect the ability to cause death. Figure S2 shows that the combined three( sub) type-associated excess mortality inferred based on virus isolation rate correlates significantly with the influenza-associated excess mortality calculated from reported mortality data. This suggests that the viral isolation rate could largely reflect the virus's ability to cause death. But we also observed several large deviations between the predicted total excess mortality and reported total excess mortality( see the boxed dots in Figure S2). These deviations could be not only due to data noise and but also due to different ability of different viruses to cause death. It is worthy of being noted that, in case of the emergence of a particularly severe strain, our calculation could lead to a significant underestimation of the excess mortality caused by the virus. Therefore, our calculated excess mortality based on the virus isolation rate may underestimate the magnitude of differences in excess mortality between viruses. Although the correlation between the antigenic variation of influenza virus and its mortality burden is impressive, a major limitation of our work is that most of the correlation coefficients have large confidence intervals( see Table S6 and Table S7), leading to the relatively high standard deviation of the coefficient in the regression model( see Equation 1) and the large confidence interval of the prediction for A/Brisbane/59/2007([3, 104] on 95% confidence level). This may be due to the data noise and the limited data available. Therefore, we think that our results need to be further validated once data from additional seasons available. Human influenza A viruses will continue to have significantly negative impact on public health and cause substantial morbidity and mortality. Timely and accurate estimation of their impact on human death will help formulate more sensible and cost-effective influenza prevention and control policies. The discovery of a significantly positive correlation between antigenic variation of an influenza virus and its excess mortality has allowed us to further establish a quantitative relationship between them. In addition to A( H1N1), we also quantified the relationship between antigenic variation and excess mortality for influenza A( H3N2) and B virus( see Figure S3 and Table S8 for A( H3N2), and Figure S4 and Table S9 for B). The established quantitative relationship for a( sub) type could be applicable to any challenging antigenic strain of same( sub) type, if its predecessors could be known that provided cross-protection for it and caused a wide infection in the current population. In the case of 2009 Swine-origin Influenza A( H1N1) Virus( S-OIV), the direct application of the established relationship is not appropriate as its predecessors provided little cross-protection except for a marginal cross-protection observed in some people aged over 65[22]. Given its antigenic variation much larger than seasonal A( H1N1) antigenic strains, we speculate that the 2009 S-OIV would cause a larger mortality than past seasonal A( H1N1) viruses. Since the current experimental methods in determining antigenic distances between viral isolates are time-consuming, we further proposed a sequence-based approach, EADpred, to predict antigenic distance between A( H1N1) viral strains, which enables us to rapidly assess the mortality burden of an A( H1N1) antigenic variant. Our method only relies on the HA sequence data of the influenza viruses rather than the surveillance data, which offers a rapid and reliable tool to assess the potential impact of an influenza virus on human death even before infection occurs in humans. Since a rapid and accurate prediction of influenza mortality burden should greatly help develop a timely and sensible preparedness programme that balances the gains of public health and the social and economic costs, we believe that our method will be very useful for rapid assessment of the influenza mortality burden of other future A( H1N1) variants, and is also applicable to the antigenic variants of human A( H3N2) and B viruses with proper modification.
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 .