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Understanding the emergence of new influenza strains is crucial for seasonal vaccine strain selection and for assessing pandemic risk [1,2]. In particular, recent mismatches between vaccine composition strains and subsequent circulating viruses have led to low vaccine effectiveness and highlighted the need for improved predictions about which antigenic variants will be dominant during future epidemics [3]. During the process of selecting vaccine strains from candidate circulating strains, sequence data can provide an indirect measurement of viral transmission fitness by revealing which new genetic variants have successfully become fixed globally [2,4]. Moreover, recent work has shown the importance of age-specific mixing and travel patterns in shaping the global circulation of influenza strains [5]. Current influenza surveillance also includes analysis of epidemiological dynamics, with local outbreaks used as a proxy for transmissibility of viruses [2]. However, the relationship between influenza transmission dynamics, population herd immunity, and viral evolution remains poorly understood. Specifically, there is a need to understand how selection pressure imposed via population-level immunity and transmission combine to shape the observed evolution of influenza viruses (Fig 1). In recent years, considerable resources have been committed to better monitor influenza evolution and resulting global diversity, most notably through the development of national influenza centers, with around 150 countries currently participating [6]. This effort has resulted in a wealth of data being accumulated, in particular, genetic sequences. During the same period, serology has increasingly been employed to measure individual immunity against specific influenza strains [7,8], and there have been calls for a “World Serology Bank” to curate such data [9]. Studies of social mixing patterns have also started to elucidate the role of agespecific social contacts in the transmission of respiratory infections [10–12]. Given this growth in data, there is now potential to develop a comprehensive framework to bring disparate data streams together to jointly analyze influenza evolution, immunity, and transmission. For example, how do antigenic drift, herd immunity, and human behavior combine to influence outbreaks? Although patterns of cross-reactive antibody responses against circulating influenza viruses have been well characterized [13] and the landscape of antibody responses can be mapped at the individual host level [7], it has been challenging to estimate the population-level transmission potential of a specific virus from available data. Previous infections may generate cross-protective responses against related influenza strains, which will in turn reduce the transmissibility of such strains within a population. However, there is currently a critical link missing between the measurement of individual serological responses and analytic tools to predict transmission dynamics.

The role of human immunity and social behavior in shaping influenza evolution