key: cord-0295330-nap7gc02 authors: Wilasang, Chaiwat; Suttirat, Pikkanet; Wiratsudakul, Anuwat; Chadsuthi, Sudarat; Modchang, Charin title: Competitive evolution of H1N1 and H3N2 influenza viruses in the United States date: 2021-09-30 journal: bioRxiv DOI: 10.1101/2021.09.30.462654 sha: f590e1447b6d89340b25691527556c4373484d03 doc_id: 295330 cord_uid: nap7gc02 Seasonal influenza causes vast public health and economic impact globally. The prevention and control of the annual epidemics remain a challenge due to the antigenic evolution of the viruses. Here, we presented a novel modeling framework based on changes in amino acid sequences and relevant epidemiological data to retrospectively investigate the competitive evolution and transmission of H1N1 and H3N2 influenza viruses in the United States during October 2002 and April 2019. To do so, we estimated the time-varying disease transmission rate from the reported influenza cases and the time-varying evolutionary rate of the viruses from the changes in amino acid sequences. By incorporating these time-varying rates into the transmission models, we found that the models could accurately capture the evolutionary transmission dynamics of influenza viruses in the United States. Our modeling results also showed that models incorporating evolutionary change of the virus could provide better modeling performance suggesting the critical role of the evolutionary change of virus on the disease transmission. The spread of the influenza virus is a major public health threat that leads to mortality, 41 hospitalization, and an economic impact. Each year, there are one billion influenza cases globally, 42 including 3 to 5 million cases of severe illness 1,2 and at least 200,000 to 600,000 recorded deaths 3-43 6 . In the US, seasonal influenza accounts for more than tens of thousands of deaths during annual 44 epidemics 7 . Moreover, the economic burden of seasonal influenza in the US has been estimated at 45 more than ten billion dollars in healthcare and social costs 7 . The influenza epidemics recur For instance, the changes in the surface protein, called hemagglutinin (HA), help the viruses to 64 escape from immune attacks 6,18,19 . The primary target of immunity against influenza is the HA 65 proteins 7,20 . By changing the properties of antigenic sites in the HA proteins, the immune system 66 may not recognize the virus and allows it to infect host cells 6,18,20,21 . Moreover, a high mutation 67 rate has been observed in several studies due to the short generation times and large population 68 sizes of the viruses 22-24 . These evolutionary processes are believed to be a major driver of seasonal 69 influenza transmission 20 . 70 In the US, influenza subtype A(H3N2) emerges and spreads more frequently than 71 A(H1N1). Nevertheless, the two subtypes seem to be competitive for the susceptible hosts. Each year, the subtype virus that gets the highest fitness from mutation may spread predominantly in 73 the population. In general, individuals infected with a single influenza strain will acquire immunity 74 against that certain strain 16,22 . Partial or cross-immunity to other subtypes is also observed 15,25-29 . However, the level of cross-immunity is weakening with an increasing antigenic change of the 76 influenza virus 2 . Therefore, co-circulation of the two influenza subtypes may result in cross-77 immunity. Mathematical models, specifically the compartmental frameworks, have been widely used 79 to investigate the transmission dynamics of influenza viruses. Most of the previous studies focused 80 on the impact of seasonality on influenza transmission dynamics 12,13,30-33 . Subsequently, the 81 evolution of these viruses has increasingly been studied as an important source of disease burden 6 . In this study, we proposed epidemiological models for seasonal influenza A(H3N2) and 94 A(H1N1) viruses that incorporate the evolutionary change of the viruses. The models integrated 95 the data on the changes of amino acid sequences of HA proteins in epitope sites to the transmission 96 models. To do so, we firstly estimated the time-varying evolutionary rate of each influenza subtype 97 using the sequence data. We then incorporated this evolutionary rate into the transmission models replaced by gaps. Aligned sequences were then edited using ClustalW, and the incomplete 126 sequences were manually removed 43,44 . Since the major target of immunity against influenza is 127 located in the epitope sites of the HA proteins 6,7,20 , we used only the amino acid sequences 128 encoding epitope sites of these proteins to analyze the antigenic change of the virus. Table 152 1. The steepness of antigenic change 2 Assumed 182 Table 1 . Table S1 and Table S2 in the Supplementary Information. (Figure 3(B) ). This result suggested that -../012#3104 might be a potential early warning for a 282 pandemic. We also found that the average -../012#3104 of A(H3N2) is less fluctuate than that of We found that the trends of monthly influenza cases generated from the model agree well We then further investigated the effects of cross-immunity between the two subtypes. The 340 strength of cross-immunity between the two influenza subtypes is represented by the parameter Ψ. If Ψ = 0, the influenza subtypes are completely different antigenically. In contrast, if Ψ = 1, the 342 two subtypes are antigenically indistinguishable 25 . Figure 6 illustrates the effect of the cross- usually present a higher attack rate than heterogeneous models, even for the same 5 . Second, we 447 ignore heterogeneity in disease transmissions such as sex, age structure, and geographical region. 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