ABSTRACT
Evidence is mounting that influenza virus, a major contributor to the global disease burden, interacts with other pathogens infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. That necessity is particularly true for mathematical modeling studies, which have become critical in public health decision-making, despite their usually focusing on lone influenza virus acquisition and infection, thereby making broad oversimplifications regarding pathogen ecology. Herein, we review evidence of influenza virus interaction with bacteria and viruses, and the modeling studies that incorporated some of these. Despite the many studies examining possible associations between influenza and Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitides, respiratory syncytial virus, human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. A notable exception is the recent modeling of the pneumococcus-influenza interaction, which highlighted potential influenza-related increased pneumococcal transmission and pathogenicity. That example demonstrates the power of dynamic modeling as an approach to test biological hypotheses concerning interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and misinterpretations. Using simple transmission models, we illustrate how existing interactions might impact public health surveillance systems and demonstrate that the development of multipathogen models is essential to assess the true public health burden of influenza, and help improve planning and evaluation of control measures. Finally, we identify the public health needs, surveillance, modeling and biological challenges, and propose avenues of research for the coming years.
Author Summary Influenza is a major pathogen responsible for important morbidity and mortality burdens worldwide. Mathematical models of influenza virus acquisition have been critical to understanding its epidemiology and planning public health strategies of infection control. It is increasingly clear that microbes do not act in isolation but potentially interact within the host. Hence, studying influenza alone may lead to masking effects or misunderstanding information on its transmission and severity. Herein, we review the literature on bacterial and viral species that interact with the influenza virus, interaction mechanisms, and mathematical modeling studies integrating interactions. We report evidence that, beyond the classic secondary bacterial infections, many pathogenic bacteria and viruses probably interact with influenza. Public health relevance of pathogen interactions is detailed, showing how potential misreading or a narrow outlook might lead to mistaken public health decisionmaking. We describe the role of mechanistic transmission models in investigating this complex system and obtaining insight into interactions between influenza and other pathogens. Finally, we highlight benefits and challenges in modeling, and speculate on new opportunities made possible by taking a broader view: including basic science, clinical relevance and public health.
Introduction
Influenza virus is a major contributor to the global disease burden, and exploration of its pathogenesis, epidemiology, and evolution has occupied generations of scientists. Its complex seasonality, antigenic drift of surface proteins, wide spectrum of severity, and capacity to cross species and cause epidemics or pandemics are all characteristics that make the virus so difficult to control [1].
The human respiratory tract is an important reservoir of bacteria, fungi, viruses, bacteriophages, archaea and eukaryotes [2], harboring diverse communities of commensal, opportunistic and pathogenic microorganisms. It has been suggested that some of these species enter into non-neutral relationships [3], including competition for resources, synergism with the host immune system, or physiological modifications that alter the normal colonization or infection processes. The contribution of these phenomena to the influenza-infection burden is largely unknown.
In terms of public health, what is understood concerning influenza transmission or severity may therefore be incomplete or misguided due to ignorance of the effect of interacting pathogens. On one hand, large-scale influenza vaccination programs may unexpectedly impact other infections due to an indirect rise or fall in the risk of contracting them in a pool of influenza-infected individuals [4]. For example, if influenza outcompetes another virus and holds it at bay, an influenza vaccination program could result in an upsurge in the competitor. On the other hand, the introduction of measures to control bacterial infections (e.g. pneumococcal vaccines) may indirectly and positively impact the influenza disease burden as secondary bacterial pneumonia are associated with severe outcomes of influenza.
Seasonal influenza generates a huge burden each year during the wintertime in temperature regions and with more complex seasonal patterns in tropical regions [5]. However, influenza pandemics frequently occur outside of the usual season, and generate an unpredictable and often large burden in morbidity, mortality, and cost [6,7], mostly due to devastating role of secondary bacterial infections [8,9]. This out-of-season circulation of pandemic strains takes place in different climatic and ecological milieus than seasonal strains, and therefore pandemic strains may interact with different coinfectors. It is therefore critically important to pandemic preparedness to understand competitive and synergistic relationships. Considering influenza in a context of interactions with other environmental species, both at the individual level from a clinical perspective, or at a population level from an epidemiological perspective, is vital to improve our understanding and control of virus transmission and the risk of developing disease on infection.
Mathematical modeling has been a key tool in infectious diseases for many years because it links the transmission of an infection from person-to-person to the dynamics of the infection at a population level [10]. Models allow researchers to probe the complex intricacies of transmission, and play forward the effects on an individual to see the impact on population level infection dynamics. Researchers can therefore easily create counterfactuals: “what if” scenarios; where vaccination rates, contact patterns, health behaviors, or any number of other factors, are different.
Models of influenza virus transmission have proved very useful in expanding knowledge of influenza biology, evolution and epidemiology. For example, models of evolutionary change and immunity aim to predict the dominant strain of influenza in the coming season [11]. Spatially explicit models have convincingly linked commuting movements to the spread of influenza in the USA [12]. Models have also been crucial to public health, contributing to the optimization of control strategies, including use of vaccines and antivirals [13–⇓⇓⇓⇓⇓19]. As the modeling field has developed, there has been effort to improve realism by incorporating heterogeneity in human contact patterns, age-related susceptibility, cross immunity after previous infections [18,20–⇓⇓23], and the potential effect of environmental variables on transmission [12,24]. Notably the vast majority of modeling work has neglected the microbial environment. Most mathematical and computational models of influenza are focused on single or sequential influenza-only infections and have broadly simplified pathogen ecology. For example, models have not been exploited to estimate the indirect effect of seasonal influenza vaccination on the incidence of severe bacterial infections in the elderly. Further, despite secondary bacterial infections being recognized as an important cause of mortality, modeling used to plan vaccine interventions during the pandemic in the considered influenza transmission alone [25].
The authors of relatively recent literature reviews gathered biological and epidemiological evidence for interactions between influenza virus and respiratory bacteria or viruses [3,26,27], but did not consider mechanistic transmission models. Mathematical models can be used to investigate mechanisms of interaction, and visualize the pathological and epidemiological patterns that result from them. Model outputs can be compared with or fitted to data, thereby enabling estimation of both the probability of such interactions, and the strength of the interaction. Estimation can be made across geographic regions (eg. winter seasonal vs year-round-transmission), for different virus subtypes (e.g. seasonal vs pandemic), and in different age groups (e.g. infants vs elderly). Computational and mathematical models to study influenza with other respiratory pathogens are currently underutilized.
In this review, we report evidence of influenza interaction with other pathogens and systematically review the modeling studies on influenza coinfection. We address how different interference mechanisms might lead to unexpected epidemiological patterns and misinterpretations. Finally, we identify public health needs, modeling and biological challenges, and propose avenues of research for the coming years.
Mechanisms of interaction
Here, “interaction” refers to any process by which infection caused by one pathogen affects the probability, timing, or natural history of infection by another. This process includes a wide range of mechanisms that can involve direct connections between the two pathogens, e.g. at the cellular level, or indirect interactions through an intermediate factor that influences the other. The indirect consequences of these interactions are described later. For influenza virus, interactions with bacterial or viral species can occur at several scales (Fig 1). Interacting pathogens may have two distinct profiles: natural human commensals, usually bacteria, which cause mainly asymptomatic carriage or mild symptoms often for long durations of weeks to months; or epidemic pathogens causing infection for shorter durations, from a few days to a few weeks. These two distinct epidemic profiles potentially involve different modes of interaction and lead to different levels of consequences. Here we detail proven and potential interaction mechanisms (Fig 1).
Within-Host Interactions
At the cellular level, interactions involve both direct and indirect mechanisms. First, influenza genes or gene products can enhance or inhibit the replication of other viruses or potential infection by bacteria [28] by direct interaction with pathogen proteins or nucleic acids. Further, indirect competition for host resources can occur, when pathogens compete for target cells, receptors, or cellular products required for replication, thus preventing or inhibiting superinfection by a secondary virus. Influenza-infected cells may also release cell-signaling molecules that could increase or decrease the probability of coinfection.
During infection influenza virus impairs innate and adaptive host defenses [29,30]. Mechanisms include altered neutrophil recruitment and function, leading to defective bacterial clearance, diminished production of alveolar macrophages [31] and inhibition of T-cell-mediated immunity [30]. Infection with a second virus could be modulated similarly, e.g. by the production of cross-reactive antibodies or cell-mediated immunity that prevents or facilitates this infection. Physiological changes induced by the host response to infection may have ecological consequences. For instance, lung-tissue damage [31] and the induction of type-1 interferon signaling were shown to promote bacterial colonization [30], and broadly inhibit virus replication [32]. Damage to lung cells caused by influenza infection, such as influenza neuraminidase stripping sialic acids from the cell surface, amplifies bacterial adherence and invasion [26], and could potentially change the likelihood of infection by another virus. Symptomatic responses to infection, like fever, have also been shown to act as “danger signals” for bacteria, e.g. meningococci, which react by enhancing bacterial defenses against human immune cells [33]. In contrast, fever may diminish viral replication rate, thereby lowering the probability of coinfection. Not all interactions depend on what happens after the influenza infection: the pre-infection respiratory flora of infected individuals may partially account for the variability of influenza infection severity and outcome [27]. For example, in animal studies, S. aureus colonization was shown to trigger viral load rebounds and reduced virus clearance [34–⇓36].
Population-Level Interactions
Behavioral responses to influenza infection can also indirectly impact the transmission of bacteria or other viruses. On one hand, people with severe influenza symptoms are likely to stay home, modifying their contact patterns, and making acquisition of second infections unlikely [37,38]. On the other hand, individuals with milder symptoms might maintain their regular activities, which could increase bacterial transmission to other individuals (as observed for tuberculosis [39]) or increase the chance of acquiring a second infection. Person-to-person variation in care seeking and medication use, such as of antivirals, antibiotics, antipyretics or vaccine(s) can also influence the risk of coinfection. For example, use of the pneumococcal conjugate vaccine has decreased carriage of the bacteria in some contexts [40,41], which may lead to a decreased chance of observing influenza-pneumococcal coinfections. Public health policies, such as vaccination or pharmaceutical recommendations targeting one pathogen may also indirectly impact another (Fig 2). Further discussion of the effect of public health interventions is given in the population impact section.
Evidence of interaction
Several literature reviews described evidence of interactions between influenza virus and other respiratory bacterial or viral pathogens [3,26]. Here we describe the viruses and bacteria that potentially interact with influenza by gathering recent findings from laboratory and epidemiological studies for bacteria and viruses (Supplementary Section A).
Influenza-bacterial interactions
Experimental results suggest that most of the pathogenic and commensal bacteria present in the nasopharynx may directly or indirectly interfere with influenza infection during host colonization or infection (Table 1). The best-studied influenza-bacterial interaction is with Streptococcus pneumoniae [3]. Influenza is thought to increase bacterial adherence and facilitate the progression from carriage to severe disease [27,42], although evidence from population studies is not so clear-cut [43–⇓⇓46]. Influenza was also shown to impair methicillin-resistant Staphylococcus aureus (MRSA) clearance in coinfected mice, thereby increasing their susceptibility to MRSA infection [47]. Similarly in mice, increased severity of Haemophilus influenzae induced by influenza virus was suggested based on experiments of sequential infection with sublethal influenza then H. influenzae doses [48]. Notably, ecological studies revealed a positive association between influenza and Neisseria meningitides incidence [49] and those of in vitro studies suggested that direct interaction between influenza A neuraminidase and the N. meningitidis capsule enhanced bacterial adhesion to cultured epithelial cells [50]. Lastly, in patients with pulmonary tuberculosis there is evidence of increased risk of severe outcomes on influenza infection [51]. This finding was supported by experiments in mice [52] which demonstrated that Mycobacterium tuberculosis and influenza coinfected mice mounted weaker immune responses specific to Mycobacterium bovis Bacillus Calmette-Guerin (BCG) in the lungs compared to mice infected with BCG alone.
Virus-virus interactions
Within its family, influenza interacts between types (A and B), subtypes (e.g. H3N2, H1N1) and strains. Competitive exclusion due to homologous immunity is widely accepted [53,54], and has been applied extensively in models of influenza-strain coexistence [55,56]. Antigenic change (measured through antigenic distance) occurs constantly in influenza, strongly indicating that the virus escapes from immunity resulting from prior infection by genetic change [57]. There is mounting evidence that the first influenza infection is important, and may affect severity of future infections [58–⇓60]. Some evidence also supports the finding that influenza can interact with other influenza viruses, and non-influenza respiratory viruses via non-specific immunity following infection [61,62].
For non-influenza interactions, many viruses are suspected of interfering with influenza virus acquisition, based on different types of studies (Table 2). During the 2009 influenza pandemic, Casalegno et al. reported that, in France, the second pandemic wave was delayed due to the September rhinovirus epidemic [63], even though the shift was not observed in other countries [64,65]. Coinfection by the two viruses might also enhance disease severity for individuals [66–⇓68], although evidence is discordant [69–⇓71]. Similarly, competitive interaction with RSV has been posited for many years [72,73], and some evidence was found for delayed RSV epidemics due to the second wave of the 2009 pandemic in France [74] and tropical regions [75,76]. There is discrepancy in the findings of interaction between influenza and RSV, with most studies finding increased severity [71,77,78] whereas others found no effect [66] and some found less severity [79]. Competitive interaction with parainfluenza viruses was also inferred, based on less frequent coinfection pairs than expected [80], but that observation is not consistent across studies [81–83]. In terms of severity, parainfluenza and influenza coinfection is usually more severe than influenza alone [66,68,84] but not always [69,70].
The general pattern is that bacteria tend to synergize with influenza, often boosting transmission of either pathogen, or increasing invasion of the bacteria following influenza infection. It is not always clear if this is a true synergy - where both pathogens benefit - or rather that influenza facilitates bacterial invasion. In contrast, viral pathogens tend to form competitive interactions with influenza, although whether these are direct, specific interactions with particular other viruses, or the result of an “early advantage” to the first infector, remains unclear. This pattern may occur because of the differing natural histories of bacteria and viruses; where the former tends to infect hosts for long time periods, and the latter has shorter infections, more similar to the natural history of influenza itself.
Impact of Interactions at the Population Level
Although coinfections occur at the host level, their consequences are far-reaching (Fig 2). Coinfection may alter the natural history, severity or timing of illness in an individual, and thereby modify the morbidity, healthcare-seeking behavior and treatment of that individual. Heterogeneity in these can affect the probability of, and timing of, reporting disease, thereby transferring the effect from individual hosts to the population level.
Development and implementation of public health policies rely on analyses of population surveillance data on influenza epidemics and burden. Policies then generate changes in medical interventions at the population level, e.g. change in vaccination rates, or at the individual level, e.g. recommendations for antibiotics or antivirals in certain groups. These public health interventions then have their own impacts on the dynamics of pathogens and coinfections. Therefore as coinfections may alter surveillance data, and policies based on evidence from surveillance data may alter coinfection or interference risk, there is a complex cycle of dependence, which highlights the difficulty—as well as the potential importance—of assessing the impact of coinfections (Fig 2).
To date, most of the published quantitative analyses of interactions rely on statistical association between incident cases of influenza-like-illness (ILI) and other infections based on regression and correlation analyses [85,86]. A major methodological challenge of detecting interactions is that significant correlation between epidemics of two pathogens in surveillance data may result from either a true biological direct or indirect interaction, or may be confounding as a result of the two pathogens sharing common ecological conditions (e.g. cold weather). Regression models do not formalize the transmission process or biological mechanism of interaction. Instead, they describe simple functional links between, for example, the incidence time series, onset or peak time, or epidemic magnitude or severity. They provide correlations between reported time series at different time lags and are useful tools to detect strong signals of associations, however, despite their apparently simple formulation, regression models “assume” strong statistical hypotheses based on the shape of the data and the association [87]. These coarse methods cannot disentangle intricate interaction mechanisms and, furthermore, the lack of mechanistic formulation of transmission and interaction hinders quantification of interaction strength, and prevents easily interpretable predictions that can be used for public health decision-making.
Due to the complex phenomena involved and many feedback interactions, mechanistic models are needed to dissect the cause-and-effect of the different components [88] (Box 1). The role of modeling is two-fold: first, mathematical modeling provides a common language to integrate heterogeneous mechanisms and test competitive hypotheses. By doing so, models contribute to building basic knowledge about infection processes. Second, modeling enables assessment of potential intervention scenarios by predicting their impact.
For these reasons, public health interventions based on modeling of infectious diseases have become informative and effective. For example, in the UK, a transmission model fitted to a vast range of ILI and influenza-surveillance data demonstrated that vaccinating children against influenza will have the same protective effect on people over 65 years old, as vaccinating those individuals [89]. This outcome is a consequence of the diminished community transmission that results from reducing infections in children. Such an impact would be impossible to identify without mechanistic models. Box 2 summarizes the benefits of mathematical models.
Models of influenza interactions
Despite mounting evidence of influenza-bacteria interactions and the concurrent increasing use of dynamic modeling to study infectious diseases in recent decades, influenza interactions have rarely been modeled. Interestingly, previous literature reviews describing evidence of interactions between influenza virus and other respiratory bacterial or viral pathogens [3,26] neglected mathematical models which, despite their limited number, provide insight into mechanisms of interaction and their consequences. We have systematically reviewed the literature for models incorporating influenza with bacteria or non-influenza viruses (Supplementary Section A).
Influenza-bacterial interaction
The only influenza-bacterium interaction that has been integrated into mathematical modeling studies is the influenza-pneumococcus system, both within-host and at the population level.
Several dynamic models of coinfection at the cellular level were proposed relatively recently [90–⇓92,93]. In a study combining modeling and empirical data from mice coinfected with two different influenza viruses and two pneumococcus strains, Smith et al. assessed the likelihood of different immunological interaction mechanisms [90]. They found a role of macrophage dysfunction leading to an increase of bacterial titers and increased virus release during coinfections [90]. However, their results suggest that coinfection-induced increase of bacterial adherence and of infected cell death were not very likely. Shrestha et al. used an immune-mediated model of the virus-bacterium interaction in the lungs to specifically quantify interaction timing and intensity [91]. They assumed that the efficiency of alveolar macrophages, which are a critical component of host immunity against bacterial infections, was reduced by viral infection and tested the impact of inoculum size, time of bacterial invasion after influenza infection, and the potential impact of antiviral administration. The model predicted that enhanced susceptibility to invasion would be observed 4-6 days after influenza infection, suggesting that early antiviral administration after influenza infection (<4 days) could prevent invasive pneumococcal disease. Smith & Smith modeled a nonlinear initial dose threshold, below which bacteria (pneumococcus) declined and above which bacteria increased. They showed that this threshold was dependent on the degree of virus-induced depletion of alveolar macrophages, using data from mice experiments. Because macrophage depletion varies through the course of influenza infection, this important finding may explain why risk of bacterial invasion also changes over the course of infection, with particularly low dose requirement in the first few days of infection [94]. In a follow up study, the same authors analyzed published data from influenza-pneumococcus co-infected mice treated with antiviral, antibiotic, or immune modulatory agents. They found that antivirals are more efficient at preventing secondary infection when used in the first two days of influenza infection; and also found an important benefit of immunotherapy, especially for low bacterial loads [92]. Lastly, in a within-host model, Boianelli and colleagues investigated the efficacy of different oseltamivir treatment regimens in influenza-pneumococcus coinfected individuals using parameters drawn from human and mouse studies. They found that increasing the dose of oseltamivir, but not duration of treatment, might increase both the antiviral and antibacterial efficacy [93].
There have been several population transmission models to assess influenza interactions with bacteria, and test hypotheses regarding the main mechanisms [95–⇓⇓⇓99]. Analysis of pneumococcal meningitis and viral respiratory infections in France highlighted two important processes in colonized individuals: a virus-related increase in pneumococcal pathogenicity and enhanced transmissibility of bacteria to others [95]. Analysis of bacterial pneumonia from the USA concluded that influenza increased individual risk of pneumonia [96,100]. Recently, in a simulation study, Arduin et al. used a flexible individual-based model of influenza-bacterial interaction to assess the population consequences and associated burden of a range of pneumococcus-influenza interaction mechanisms [101]. Population dynamic models have also been used to test the public health impact of control measures [97–⇓99]. Different strategies of antibiotic use (as treatment or prophylaxis) and of vaccination were assessed by modeling the dual transmission of pneumococcus and influenza [97]. When modeling a 1918-like pandemic, this model suggested that widespread antibiotic treatment of individuals with pneumonia would significantly lower mortality, whereas antibiotics in prophylaxis would effectively prevent pneumonia cases. A different model evaluated the benefit of vaccinating the UK population against pneumococcus in the context of pandemic influenza using different pandemic scenarios: 1918-like, 1957/68-like or 2009-like [98]. Those results indicated that pneumococcal vaccination would have a major impact only for pandemic with high case-fatality and secondary pneumococcal infections rates (e.g. the 1918-like), with less influence in other scenarios.
Viral interaction
Influenza-influenza interactions predominate in models of two viruses, with limited investigation of influenza-RSV interactions, and no models of other viruses.
Within-host, several models of multi-strain influenza infections were proposed [102–⇓104], especially examining the interval before the secondary infection. One model of RSV-influenza interaction at the cellular level explored the hypothesis of the viruses interacting through competition for resources within the cell [105]. This indirect competition was sufficient to explain the observed rate of virus replication. The model also explored how the speed of virus replication confers an advantage to the first infecting pathogen, and determined the “head start” on infection that the slower-replicating virus would require to maintain dominance.
Population models have been used extensively to examine the dynamics of influenza and multi-strain influenza systems (for a review see [106]) although many fewer studies examined multispecies systems. Because the influenza virus comprises two species, multiple subtypes, and potentially numerous strains of each, at any given time many viruses may be circulating, providing varying degrees of cross-protection after recovery, and sometimes with complex dynamics of within-species strain replacement due to genetic drift or reassortment. There is evidence of competition between strains, with some models requiring short periods of heterologous immunity after infection to create the ladder-like phylodynamic structure of influenza viruses [107], although recent studies could capture this feature without this mechanism [55]. One comprehensive early model tested four mechanisms of interaction between influenza types using data from Tecumseh, Michigan, but the data were insufficient to distinguish the mechanisms [108]. Influenza-influenza models must also account for the complex immune history of hosts, where there is mounting evidence that the timing of an individual’s influenza encounters, and especially the first infection, shapes their future response [58–⇓60]. The methods for modelling influenza-influenza interactions should be extended into interactions with other viruses.
One model for pandemic influenza, in which coinfection with other respiratory pathogens leads to enhanced influenza transmission, was proposed to explain the multiple waves of the 1918 influenza pandemic in the UK [109]. A recent example of influenza and RSV crossspecies analysis, in a climatically driven model, provided some evidence that RSV dominates influenza, but the model was not explicitly fitted to data [110].
Illustration from a simple model
To demonstrate how both synergistic and competitive interactions can be modeled, we used a simple transmission model and simulated the effect of interactions (Box 3, Figure 3, and Supplementary section B). We show how these interactions at the individual level can impact the epidemics at the population model. The “bacterial type” interaction firstly shows an increase in bacterial prevalence when influenza infection increases bacterial transmission, in a facilitative interaction. In a synergistic interaction, where coinfection increases transmission of both influenza and bacteria, prevalence of bacteria increases, and the epidemic of influenza has a quicker and higher peak. In the competitive interaction, progressively decreasing the probability that a second pathogen can infect an already-infected host causes the epidemic peaks to separate in time. It also decreases the peak size of the outcompeted pathogen, without altering the number of people infected in total (Figure 3).
Limits of the Current View
Historically, scientific and medical studies have tended to focus on host-pathogen interactions in an independent manner, by studying each pathogen alone. It is clear that the human host simultaneously encounters many microbes. Many respiratory viruses and bacteria have been linked to influenza epidemiology, based on in vivo evidence from individual and epidemiological studies. These non-neutral interactions, mostly facilitative for bacteria and competitive for viruses, probably have individual- and population-level effects on influenza pathogenicity, burden and potentially its epidemic profile. Furthermore, this is a complex system in which each host-pathogen or pathogen-pathogen interaction phenomenon may impact the others. Surprisingly however, such interactions remain poorly studied and, in particular, very few modeling studies have addressed these questions.
Mathematical models are crucial to guide public health decision-makers, who, for ethical or cost reasons, cannot conduct large-scale trials. Two examples of interventions based on modeling results and mobilizing important public resources are pandemic preparedness (stockpiling of antivirals, use of vaccine doses) [111] and national immunization programs [19]. Neglecting the cocirculating pathogens— i.e., adopting influenza tunnel vision—and the indirect impact of coinfections, may potentially affect the estimation of the risk associated with influenza infection and, consequently, the accuracy of model predictions. Interaction strength may also change from year-to-year, and depend on the circulating influenza strain. For evaluation of interventions, this neglect can lead to overestimation of the impact - if burden was measured without considering the changing landscape of coinfection in the population; or underestimation - if the effect of an intervention does not account for the potentially decreased burden of an interacting pathogen as a result of diminished influenza transmission. For all these reasons, we think that adopting a more holistic approach to modeling of respiratory pathogens will improve their surveillance and the strategy to control them.
Opportunities
Considering influenza virus in its ecological context and its interactions as a cause of its associated morbidity and mortality should offer opportunities for prevention and treatment. On one hand, models can assess available vaccines in optimal combinations and for better-targeted populations. In addition to influenza vaccines that (partially) protect against virus acquisition, antibacterial vaccines are also critical. Pneumococcal vaccines have good efficacy against influenza-associated non-bacteremic pneumonias [112,113]. The 23-valent pneumococcal polysaccharide vaccine significantly lowered the risk of invasive pneumococcal disease and attributed mortality in the elderly [114]. Better understanding of possible influenza-pneumococcus interactions and integrating those into transmission models could potentially identify synergies between these vaccination programs, and optimize the use of both vaccines.
In addition, optimization of antibiotic and antiviral prescriptions should be considered. First, antibiotics have historically been used extensively to prevent secondary infections [115,116]. However, increasing rates of antibiotic resistance worldwide led to policies to mitigate antibiotic consumption, focusing particular attention on reducing antibiotic prescriptions for viral infections. Second, neuraminidase inhibitors were found to prevent some secondary bacterial pneumonias in animal models, human investigations and modeling studies, beyond the window in which they directly impact the influenza viral load [91,117,118]. Although antivirals may only modestly attenuate influenza symptoms, a body of evidence suggests they could avoid severe and economically important outcomes of influenza infection [118–⇓⇓121].
Accurate burden quantification is crucial to designing and implementing public health interventions against influenza. Focusing efforts to better understand these interactions is therefore critical, especially in the context of pandemic influenza, but also to plan for seasonal epidemics, by forecasting the onset and peak times, and estimating the expected burden. Deeper understanding of the ecology of the vast number of microorganisms that can contribute is needed. To obtain it, further modeling work is needed, to test observed and putative interactions and to determine their effects at the population level. From an experimental perspective, models can be used to analyze available surveillance and experimental data, generate hypotheses regarding interaction mechanisms at play in transmission or infection, and test their likelihoods. Competing assumptions on the biological interaction processes can be assessed and the strength of interactions can also be estimated. From a public health viewpoint, these new models will help better estimate the burden of influenza virus interactions in terms of morbidity and mortality, the cost-effectiveness of interventions, and, critically, more accurately predict the real impact of control measures.
Challenges
Integrating transmission and infection by multiple pathogens into mathematical modelsposes several challenges. From a methodological perspective, modeling several pathogens with interrelated natural histories makes classical compartmental approaches more difficult. Individual-based frameworks are better adapted for this task, e.g., this approach could be used to investigate the effect of the interval between influenza infection and bacterial acquisition, which reportedly affects the risk of bacterial invasion [30,122]. Individual-based models are often more computationally intensive and can introduce new difficulties in terms of parameter estimation, requiring the design of new methods. Recent developments in statistical inference methods, like particle Markov chain Monte Carlo (pMCMC) or maximum likelihood estimation via iterated filtering (MIF) [123,124], now enable modelers to jointly fit complex population-based models to multiple types of data, thereby allowing more data, and more diverse types of data to inform the model parameters.
Epidemiological data represent the second major challenge. To date, modeling studies have been limited by the poor knowledge of respiratory viruses and bacteria circulating in the community, especially because little is known about prevalence, incidence, at-risk populations and even epidemic profiles in different populations. On an individual level, new studies are required to assess the effect of coinfections, rather than ecological associations from incidence data. Important features include: i) coinfection-induced alteration of diseases’ natural histories, e.g. increased acquisition and severity risk, changes of infection durations and generation times; ii) specific at risk-periods for secondary infection or invasion of the coinfecting pathogen, or at-risk periods for severe outcomes; iii) at-risk populations, as characterized by individuals’ age, comorbidities or behavioral risk factors.
For population-level data, in most countries surveillance of influenza acquisition is based on networks of general practitioners who notify patients consulting for clinical symptoms of ILI [125]. Surveillance-data streams based on syndromic surveillance [126], inpatient data [127] and pathogen testing [128,129] should be combined, and linked at the patient level, to better identify non-influenza infections, or anomalous epidemics that could signal interaction. Improvement of data quality in patient records and detection of the biases inherent in different types of surveillance data are critical to achieve this goal. The latter could be reached by developing new microbiological tools, including new sampling kits able to rapidly detect multiple pathogens for use during medical consultations.
Conclusion
We examined the epidemiological and biological evidence supporting influenza virus interference and interaction with other pathogens. We highlighted opportunities to improve knowledge and control of the virus, if we can move forward from the tunnel vision of singlepathogen models. It is time to develop a more holistic approach to pathogen dynamics in mathematical modeling, with novel methodological innovations, and further efforts in data collection and surveillance. The motivation to do so lies in the real opportunity to improve public health practices, and create better, more cost-effective interventions against influenza.
Funding
LO acknowledges funding from the French Government’s “Investissement d’Avenir” program, Laboratoire d’Excellence “Integrative Biology of Emerging Infectious Diseases” (grant no. ANR-10-LABX-62-IBEID) and from Région Île-de-France (Domaine d’Intérêt Majeur Maladies Infectieuses). RME acknowledges funding from the National Institute for Health Research through the Health Protection Research Unit in Immunisation at the London School of Hygiene & Tropical Medicine in partnership with Public Health England.
Acknowledgments
The authors thank Elizabeth Miller and Matthieu Domenech de Celles for helpful comments on the manuscript. They thank the three anonymous reviewers for helpful and constructive comments. Box 1. Mathematical modeling definitions
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