Background: Influenza viruses are etiological agents which cause contagious respiratory, seasonal epidemics and, for influenza A subtypes, pandemics. The clinical picture of influenza has undergone continuous change over the years, due to intrinsic viral evolution as well as “reassortment” of its genomic segments. The history of influenza highlights its ability to adapt and to rapidly evolve, without specific circumstances. This reflects the complexity of this pathology and poses the fundamental question about its assumption as a “common illness” and its impact on public health. Summary: The global influenza epidemics and pandemics claimed millions of deaths, leaving an indelible mark on public health and showing the need for a better comprehension of the influenza virus. The clear understanding of genetic variations during the influenza seasonal epidemics is a crucial point for developing effective strategies for prevention, treatment, and vaccine design. The recent advance in next-generation sequencing approaches, model systems to virus culture, and bioinformatics pipeline played a key role in the rapid characterization of circulating influenza strains. In particular, the increase in computational power allowed the performance of complex tasks in healthcare settings through machine learning algorithms, which analyze different variables, such as medical and laboratory outputs, to optimize medical research and improve public health systems. The early detection of emerging and reemerging pathogens is a matter of importance to prevent future pandemics. Key Messages: The perception of influenza as a “trivial flu” or a more serious public health concern is a subject of ongoing debate, reflecting the multifaceted nature of this infectious disease. The variability in the severity of influenza sheds light on the unpredictability of the viral characteristics, coupled with the challenges in accurately predicting circulating strains. This adds complexity to the public health burden of influenza and highlights the need for targeted interventions.

Influenza, a contagious respiratory illness caused by influenza viruses, has been a constant presence throughout human history. However, its impact and our understanding of the disease have undergone continuous change and evolution over the years. The evocative title “Changing and Evolution of Influenza Virus: Is It a Trivial Flu?” reflects the complexity of this pathology and poses the fundamental question about its perception as a common ailment.

To fully grasp the evolution of influenza, it is essential to cast our gaze into the past. The history of influenza is marked by historical episodes that have shaped our understanding of the virus and its ability to adapt. The Spanish flu pandemic of 1918, for example, stands as a stark reminder of the devastating potential of influenza viruses. This global outbreak claimed millions of lives, leaving an indelible mark on public health and underscoring the need for a deeper comprehension of the influenza virus.

Over the years, the influenza virus has exhibited a remarkable capacity for genetic variation. Genetic mutations in the virus contribute to its ability to evade the immune system and pose challenges for vaccine development. The constant interplay between the virus and the human immune system has led to a perpetual arms race, with the virus adapting and mutating to ensure its survival. Understanding the genetic variations of the influenza virus is crucial for devising effective strategies for prevention, treatment, and vaccine design.

The impact of influenza on public health cannot be overstated. The virus has the potential to cause widespread illness, placing a significant burden on healthcare systems and economies. The seasonal flu, while often considered routine, has the capacity to strain healthcare resources and result in substantial morbidity and mortality. Furthermore, the emergence of novel influenza strains with pandemic potential adds an additional layer of complexity to the public health challenge posed by the virus.

Controversies surrounding the perception of influenza further contribute to the intricate narrative of this infectious disease. While some may dismiss influenza as a mere seasonal inconvenience, others recognize its potential for severe outcomes, especially for vulnerable populations. The perception of influenza as a trivial flu is not universal, and debates within the scientific community and among the general public highlight the nuanced understanding required to navigate the complexities of this infectious disease.

As we explore the changing landscape of influenza, it is evident that a comprehensive approach to research, surveillance, and public health measures is imperative. The development of effective vaccines, timely antiviral treatments, and robust public health strategies hinges on a nuanced understanding of the virus’s evolution. The ongoing surveillance of influenza strains, coupled with advancements in molecular biology and epidemiology, provides valuable insights into the dynamic nature of the virus and aids in the prediction and mitigation of potential outbreaks.

Influenza viruses are enveloped, negative-sense single-stranded RNA viruses, belonging to the Orthomyxoviridae family. Three genera of influenza viruses are known to affect humans, Alphainfluenzavirus, Betainfluenzavirus, and Gammainfluenzavirus, each containing a single antigenic type: influenza A, B, and C [1]. Their genome consists of seven or eight segments, which encode for proteins with different antigenic properties of hemagglutinin (H) and neuraminidase (N) surface glycoprotein, RNA polymerase (PB1-PB2-PA subunits), nucleoprotein, and matrix protein [2]. In 2011, influenza D virus (Deltainfluenzavirus), identified in an infected pig, was reported but, to date, has not yet been associated with clinical infection in people [3]. Typically, influenza A and B cause mild to moderate seasonal epidemics, while the influenza C virus is implicated in mild respiratory disease and does not induce epidemics.

Unlike the other two types, influenza A virus has always posed a significant impact on public health due to its capacity to spread among animal reservoirs, such as pigs, horses, marine mammals, domestic animals, and birds with spillover to humans [4]. Influenza A virus is classified into more than 130 subtypes and, in turn, clades and subclades based on surface glycoproteins (H–H18 and N1–N11) and their combinations which play important roles in virulence, host specificity, and immune response. When two antigenically different influenza A viruses co-infect the same animal reservoir, they can swap their gene segments (“reassortment”), resulting in progeny with new properties. Thus, influenza A is the only influenza type known to cause pandemics [4].

The term “influenza” was first coined in Italy in the Middle Ages, following an epidemic in Florence, for which the astrologers attributed its periodic return to the heavenly bodies, calling it “influence of the stars.” The flu had most likely already been around for ages, even if historical documents have not been found. The first reliable evidence dates from 412 BC, when the physician Hippocrates, in his “Epidemics” book collection, describes a highly contagious disease with flu-like symptoms in Perinthus, a northern area of Greece, now Turkey [5]. Once humans began to domesticate animals and practice agriculture, alongside permanent settlements, a fertile environment for influenza was created. Between the 16th and 18th centuries, several influenza pandemics were documented. First, in 1510, originating in Africa, an influenza pandemic spread all over Europe for which the total death toll is still unknown [6], followed by another pandemic starting in Asia in 1580 and circulated to Africa, Europe, and even America with such force as to wipe out entire populations, in a few weeks [6]. Those historical pandemics followed the European colonial efforts toward America, Africa, and Asia. England, Ireland, and Virginia were affected by influenza virus in 1688, and a probable new divergent lineage struck Europe and America again between 1693 and 1699 [7]. A further two overwhelming pandemics spread in London in 1847 and, subsequently, worldwide in 1890, causing more deaths than cholera epidemics [6]. The “Spanish” influenza in 1918–1919 was the most lethal worldwide pandemic, causing some 20–50 million deaths [8]. It began with a mild clinical picture but then became more virulent in two successive waves [9]. The scientists have hypothesized that influenza-related pneumonia was caused by bacteria, such as pneumococcus, streptococcus, or Haemophilus influenzae. The viral etiology continued to be not considered, until the virus was isolated in 1932–1933 from nasal secretions of infected patients, demonstrating the intranasal human transmission [10]. In 2000, the complete coding sequence of surface glycoprotein allowed confirmation that the origin of the 1918 pandemic virus was from a combination of human and avian strains, which formed a new H1N1 influenza A virus [11]. Tumpey and colleagues [12], using a reverse genetics approach, generated the 1918 pandemic strain, showing a unique high-virulence phenotype never observed among pandemic viruses. During the 20th and 21st centuries, a further three pandemics of influenza occurred: in 1957, “Asian flu,” due to a reassorting strain, originating from avian influenza H2N2, and PB1 gene with human H1N1 influenza gene segments [13, 14]; in 1968, “Hong Kong flu,” caused by a new influenza A subtype, H3N2, genetically evolved from the strain of the 1957 pandemic [15]; and in 2009, “swine flu,” H1N1, resulting from a triple reassortment between bird, swine, and human flu strains, which further combined with an Eurasian pig flu virus [16] (shown in Fig. 1).

Fig. 1.

Milestone timeline of influenza pandemics.

Fig. 1.

Milestone timeline of influenza pandemics.

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Although the clinical picture of the last 2009 H1N1 influenza pandemic was relatively mild in several age groups, its unpredictable emergence and rapid spread give rise to serious considerations. An integrated health management would be needed to evaluate the virological characteristics of circulating influenza strains for their pandemic potential and public health risk.

The genetic variations of the influenza virus stand as a testament to the virus’s remarkable ability to adapt and persist over time. At the molecular level, the influenza virus undergoes constant changes, primarily driven by two key mechanisms: antigenic drift and antigenic shift [17]. Antigenic drift refers to the gradual accumulation of small genetic mutations in the H and N surface proteins of the influenza virus. These play crucial roles in viral entry into host cells and the release of new viral particles [18]. The continuous accumulation of mutations in these proteins can lead to changes in the virus’s antigenic profile, allowing it to evade recognition by previously acquired immune responses. The consequences of antigenic drift are particularly evident in seasonal influenza epidemics [19]. The gradual changes in viral surface proteins enable the virus to partially escape immunity acquired from previous infections or vaccinations. This phenomenon necessitates the regular updating of influenza vaccines to align with the prevalent strains. The dynamic nature of antigenic drift adds an element of unpredictability to influenza epidemiology, contributing to the variability in the severity and impact of each flu season [20]. In contrast, antigenic shift represents a more dramatic and infrequent reorganization of the influenza virus genome. This process occurs when two different influenza viruses infect the same host cell and exchange genetic material. The result is a novel virus with a combination of genetic segments from both parent viruses [21]. Antigenic shift has historically been associated with the emergence of pandemic influenza strains, capable of causing widespread illness on a global scale [22]. The potential for antigenic shift introduces an element of unpredictability and poses a significant challenge for public health preparedness [23]. Unlike antigenic drift, which occurs gradually and can be monitored through continuous surveillance, antigenic shift can lead to the sudden emergence of a completely new influenza subtype [24]. The lack of preexisting immunity to such novel strains increases the likelihood of severe illness and facilitates rapid transmission within populations. Avian influenza viruses, in particular, are recognized as reservoirs for potential pandemic strains [25]. These viruses naturally infect birds, and certain subtypes, such as H5N1 and H7N9, have demonstrated the ability to cross the species barrier and infect humans. The genetic diversity of avian influenza viruses, coupled with their potential to reassort with human-adapted strains, creates a constant threat of novel pandemic viruses emerging from avian reservoirs [26]. Advancements in molecular biology, particularly the widespread use of next-generation sequencing technologies, have revolutionized the study of influenza virus genetics. Whole-genome sequencing allows the analysis of the complete genetic makeup of influenza viruses with unprecedented speed and precision. This technological leap has facilitated real-time monitoring of genetic variations in circulating influenza strains, enhancing our ability to detect emerging threats and inform public health responses. The importance of genetic surveillance is underscored by the continuous evolution of influenza viruses. Monitoring the genetic changes in circulating strains provides critical information for updating vaccines, understanding transmission dynamics, and anticipating potential shifts in viral virulence. The intricate dance between the influenza virus and the human immune system further complicates the dynamics of genetic variations. Immune selection pressure, exerted by the host’s immune responses, can drive the evolution of influenza viruses. The virus, in turn, employs strategies to evade immune recognition, leading to a perpetual cycle of adaptation and counteradaptation [27]. One phenomenon that exemplifies the interplay between the influenza virus and the immune system is immune escape. This occurs when the virus undergoes genetic changes that allow it to evade recognition by neutralizing antibodies. The constant evolutionary pressure exerted by the immune system selects for variants that can escape immune detection, contributing to the ongoing genetic diversity of influenza viruses. Understanding the genetic determinants of influenza virulence is crucial for predicting the severity of influenza seasons and the potential for pandemics. Certain genetic markers associated with increased virulence, such as specific amino acid substitutions in viral proteins, have been identified through extensive research. However, the intricate relationship between genetic variations and clinical outcomes remains a subject of ongoing investigation, highlighting the complex interplay of factors influencing influenza pathogenesis [28]. The role of genetic variations extends beyond the dynamics of influenza within human populations. The influenza virus’s zoonotic potential, exemplified by its ability to infect a wide range of animal species, introduces additional layers of complexity [29]. The ongoing surveillance of influenza viruses in animals, particularly domestic poultry and wild birds, is crucial for identifying potential sources of novel strains with pandemic potential. The One Health approach, recognizing the interconnectedness of human, animal, and environmental health, is integral to understanding the broader implications of influenza virus genetics [29]. The spillover of influenza viruses from animals to humans underscores the need for collaborative efforts between human and veterinary health professionals. By monitoring genetic variations in influenza viruses across diverse host species, researchers can gain insights into the factors influencing cross-species transmission and the emergence of novel strains. While genetic variations drive the evolution of influenza viruses, the implications of these changes extend to the development and efficacy of influenza vaccines. The annual formulation of influenza vaccines involves predicting the most likely strains to circulate in the upcoming season [30]. The accuracy of these predictions directly impacts the vaccine’s effectiveness in conferring immunity. The phenomenon of vaccine mismatch, where the circulating influenza strains differ from those included in the vaccine formulation, highlights the challenges posed by genetic variations. Antigenic drift, leading to subtle changes in viral surface proteins, can result in diminished vaccine efficacy against the drifted strains. This necessitates continuous efforts to improve the accuracy and timeliness of vaccine strain selection, incorporating real-time genetic surveillance data. The development of a universal influenza vaccine, capable of providing broad and durable protection against multiple influenza strains, represents a long-sought goal. Such a vaccine would mitigate the need for annual updates and offer enhanced preparedness against the unpredictable nature of antigenic drift and shift. Ongoing research into conserved regions of the influenza virus, less prone to genetic variability, holds promise for the development of a universal vaccine [31].

The unexpected genomic evolution of the influenza viruses that took place in nature since the first pandemic might lead to the generation of new potential pandemic viral isolates [32]. The lack of human immunity to the new emerging pathogen would result in widely lethal pandemics. The recent advances, not only in high-throughput sequencing approaches but also in model systems for virus culture and bioinformatics pipeline, play a key role in the rapid characterization of new viral strains. In this scenario, the increase in computational power and the generation of big data allow for performance of complex tasks in a healthcare setting through machine learning (ML) algorithms [33]. The main purpose of algorithm analysis is to understand the determinant features to obtain a useful performance [34]. ML assesses different variables, such as medical and laboratory outputs, to optimize medical research and identify infectious disease outbreaks, improving public health systems [33]. The timely detection of emerging and reemerging pathogens is a matter of importance to prevent future pandemics. Sequencing methods combined with computational analysis are widely applied to estimate the risk of spread and to predict the emerging clade and its origin since the influenza viruses can cross-species boundaries [35, 36]. A user-friendly interface classified quickly human influenza A virus into subtypes and clades from nucleotides and proteomic change inputs [34, 35].

Clinical symptoms (especially disease severity) and potential outbreaks should be used as alerts to perform genomic analysis of identified influenza virus strains [37]. Indeed, the development of freely available databases, containing viral sequences related to epidemiological and clinical data, and even social media contributed to improving viral surveillance [37, 38]. New algorithms seek social platforms to analyze health-related text posted, improving syndromic surveillance systems in the global public health era. However, to obtain more accurate results, it is necessary to combine physical conditions and demographic information, using several social networks simultaneously, and considering users’ privacy [38]. Recently, ML was applied to viral respiratory illnesses in different fields to improve vaccine design and treatment [39‒42].

Viral diversity due to antigenic drift and shift is responsible for drug resistance and requires an annual vaccine update [39]. Vaccine composition is usually designed to provide immunity to new emergent variants in the current flu season [43]. The prediction of viral antigenicity against H surface protein, the target of neutralizing antibodies, by serological assays is time-consuming and labor-intensive [44]. Additionally, the limitation of current vaccine selection is based on recent epidemiological data to produce stock for the next year and the procedures to evaluate immune response [35]. Novel computational methods can predict in a short time the mutations involved in viral transmission, considering the function and stability of viral proteins [35, 40]. The MFPAD tool integrated H sequences of H3N2 viruses with cross-reactive hemagglutination inhibition antibody titers of antisera collected in different time spans. The close results between computational, using the MFPAD tool, and serological data suggested this model is promising to guide the selection of vaccine candidates [40]. Berman and co-workers developed an ML framework useful to forecast all likely amino acid changes in a specific coding genomic region, reproducing an accurate mutation profile of the H protein. MutaGAN tool identifies the energetically favorable protein to predict seasonal influenza variants with fitness advantage, avoiding failure or reduced protection of the vaccine [35].

The gaps in vaccine efficacy can be filled by antiviral drugs, most effective during the first 2 days postinfection with influenza and ineffective due to the emergence of drug-resistant strains [41, 45]. Physicians could determine the amount of time that has elapsed since exposure between the host and the respiratory virus by an in silico model. This predictive model exploited the human gene expression profile during infection linked to the host’s immune response [46]. The administration of antivirals is directly related to the mutation of the target protein, such as N and MP2, resulting in the discontinuation of inhibitors to treat influenza strains circulating during the following seasonal flu [39]. Between 2007 and 2008, throughout the influenza season, H1N1 viruses resistant to the N inhibitor oseltamivir were detected worldwide. The MP2 ion channel inhibitors were ineffective against H5N1 and H3N2 viruses and the H1N1 pandemic strain in 2009 at various times. In the 2018–2019 seasonal flu, mutations related to polymerase resistance were detected among H1N1 and H3N2 circulating strains [39].

Antiviral resistance based on currently approved drugs sheds light on the need to develop next-generation antivirals, and ML can predict the potential antiviral activity of specific peptides against influenza virus [47]. The AVPIden software employed a double-stage classification, mainly distinguishing antiviral peptides from a large collection and secondarily characterizing viral families that are the target of peptides [47]. The virtual scan of big data, considering biological mechanisms in the host, identified novel treatments, reducing the search time for pharmacological targets [39]. Influenza virus induces altered splicing of host genes during infection. The host heterogeneous nuclear ribonucleoprotein K (hnRNP K) promoted splicing of matrix protein segment transcript to generate MP2 protein, suggesting an intricate interaction between host and virus genomes. Manipulation of splicing mechanisms could be used as a therapeutic intervention [48].

In view of the above features, the scientific community is encouraged to store clinical data in specific repositories to ameliorate medical practice. Notwithstanding, ML ultimately benefits public health and patients; however, sensitive information needs protection from unauthorized access [49]. A summary of ML applications is shown in Figure 2.

Fig. 2.

Role of machine learning in the control, prevention, and treatment of influenza virus infection.

Fig. 2.

Role of machine learning in the control, prevention, and treatment of influenza virus infection.

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The perception of influenza as a trivial flu or a more serious public health concern has been a subject of ongoing debate, reflecting the multifaceted nature of this infectious disease [50]. While for many influenza is synonymous with the seasonal flu – a common ailment causing temporary discomfort and inconvenience – there exists a spectrum of perspectives within the scientific community and the general public [51]. One facet of the controversy centers around the variability in the severity of influenza seasons. In milder seasons, where the circulating strains closely match the vaccine formulations, the impact on public health may appear less pronounced. This can contribute to a perception that influenza is a routine and manageable health concern. However, the inherent unpredictability of the virus, coupled with the challenges in accurately predicting circulating strains, means that influenza’s impact can vary significantly from year to year [52]. The demographics of those most severely affected by influenza further contribute to the controversy. While healthy individuals may experience influenza as a transient illness, certain populations face a higher risk of severe outcomes [53]. The elderly, young children, pregnant women, and individuals with underlying health conditions are more susceptible to complications that can lead to hospitalization or, in some cases, death. This disproportionate impact on vulnerable groups underscores the importance of nuanced public health messaging that considers the diverse experiences of different populations. The public’s perception of influenza is also influenced by the effectiveness of preventive measures, particularly vaccination. In seasons where the vaccine closely matches the circulating strains, vaccination can significantly reduce the risk of infection and mitigate the severity of illness [54]. However, the dynamic nature of the influenza virus, with its potential for antigenic drift and occasional vaccine mismatches, introduces uncertainties regarding the vaccine’s effectiveness. This can contribute to public skepticism about the value of vaccination and its role in preventing influenza. Furthermore, the presentation of influenza symptoms can vary widely, adding to the challenges in perception. While some individuals may experience mild respiratory symptoms, others may face more severe manifestations, including high fever, respiratory distress, and complications such as pneumonia. The diversity in symptom severity contributes to a range of individual experiences, shaping perceptions of influenza as a mild or severe illness [55]. In addition, the controversy surrounding the perception of influenza is not confined to public understanding but extends to the scientific community. Indeed, the best strategies for influenza prevention, the effectiveness of existing vaccines, and the development of novel approaches, such as universal vaccines, are issues commonly questioned. In addition, the ongoing evolution of the influenza virus introduces a level of uncertainty, and scientific debates persist about the most effective interventions for different populations and age groups. Moreover, the interplay between influenza and other respiratory viruses, such as the common cold and respiratory syncytial virus, adds complexity to the understanding of influenza’s impact. The overlapping symptoms and seasonal circulation of multiple viruses can complicate diagnosis and contribute to challenges in accurately assessing the true burden of influenza [56].

This complexity fuels debates about the distinctiveness of influenza and the necessity of targeted interventions. The broader societal context also shapes the perception of influenza. The media’s portrayal of influenza outbreaks, particularly during pandemics, can influence public attitudes and responses. Sensationalism, coupled with the inherent unpredictability of influenza, can contribute to heightened public concern during certain seasons or when new strains emerge. Balancing the need for accurate information with the potential for panic poses an ongoing challenge for public health communicators.

The authors have no conflicts of interest to declare.

This study was not supported by any sponsor or funder.

F.S. and M.C. have substantially contributed to the conception and design of the work; G.P., F.S., N.M., and N.C. drafted the work; and G.P., F.S., A.C., C.R., F.B., A.Q., N.M., G.M., D.S., and M.C. revised it critically for important intellectual content. All authors have approved the final version to be published.

Additional Information

Grazia Pavia and Fabio Scarpa contributed equally to this work.

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