Biology subjects

Opatowski, L.

Publications and source records attributed to Opatowski, L..

12 recordsLinked to original sources

bioRxiv2026

From sewage to shoreline: Tracing antibiotic resistance gene trends through tropical island wastewater treatment pathways

Wastewater is a key reservoir and transmission route for antibiotic resistance genes (ARGs), enabling their spread from influent to effluent and into receiving environments. However, how combined selective pressures (antibiotics, biocides, heavy metals, pharmaceuticals) influence resistant bacteria and ARG persistence over space and time remains poorly understood. Likewise, the role of the wastewater microbiome in ARG dynamics is still unclear, as few studies integrate microbiome shifts with chemical and environmental drivers. Here, we investigated how microbiome dynamics, chemical exposures, and environmental conditions shape clinically relevant ARG dynamics from sewage to receiving environments in Guadeloupe, French Caribbean. We analysed data collected from three wastewater continuums, (hospital-based, domestic, touristic) over four campaigns (September 2021-February 2023). We characterised ARG and microbiome composition spatiotemporal patterns and used a mixed-effect model to investigate ARG associations with potential drivers, including exposome factors, microbiome dissimilarity and environmental factors. Several ARGs were negatively associated with microbiome dissimilarity (Bray-Curtis distances) (aac(6)-Ib, aph(3)-III, blaSHV, blaTEM, intI1, qnrS, sul1 and tetM). Negative associations were also observed between upstream-downstream differences in anti-inflammatory drug concentrations and the abundance of aac(6)-Ib, aph(3)-III, blaCTX-M, ermB, intI1, and tetM. In contrast, ARG relative abundance was positively associated with upstream-downstream differences in antibiotic concentrations, suggesting selection along the continuum. These findings indicate that ARG dissemination along wastewater-to-coastal pathways is shaped by opposing processes, with microbiome turnover potentially limiting ARG persistence while chemical gradients promote specific gene enrichment. The outcome is ARG-specific, with implications for antimicrobial resistance risks associated with recreational waters, seafood consumption, and coastal ecosystem interactions.

microbiology↗

bioRxiv2025

Limitations of non-mechanistic methods for characterizing pathogen-pathogen interactions: A simulation study

Pathogen-pathogen interactions occur when infection with one pathogen influences ones chance of infection or disease due to another. Increasingly, evidence suggests that interactions are a common feature of infectious disease epidemiology. However, due to both the nonlinearities and stochasticity inherent to infectious disease transmission, and the frequency of confounding (e.g., by shared seasonal forcing), simple, purely statistical methods for characterizing interactions may be prone to failure. Here, we perform a simulation study to evaluate several more complex non-mechanistic approaches for inferring causality from time series data: generalized additive models (GAMs), Granger causality, transfer entropy, and convergent cross-mapping (CCM). Specifically, we use a two-pathogen mechanistic transmission model, calibrated to produce dynamics resembling outbreaks of influenza and respiratory syncytial virus (RSV), to generate synthetic datasets with a range of values for interaction strength and duration. We then apply each method to all synthetic datasets. We find that Granger causality, transfer entropy, and CCM all fail to consistently infer whether data contain signal of an interaction; in particular, methods tend to incorrectly identify interactions where none are modeled (average sensitivity = 80.6%, 92.1%, 72.1%, respectively; average specificity = 31.0%, 33.3%, 33.1%). Furthermore, we find little to no association between point estimates from each method and true interaction strength. In contrast, GAMs infer the existence of interactions more accurately than the other methods (sensitivity = 85.2%, specificity = 72.5%), and consistently yield larger point estimates for stronger interactions. However, their practical utility is limited by an inability to evaluate interaction asymmetry (i.e., whether the effect of pathogen A on pathogen B is identical to that of B on A). Overall performance patterns were similar when methods were applied to two real-world datasets from Hong Kong and Canada. We conclude that accurately and comprehensively characterizing pathogen-pathogen interactions based on outbreak data remains a significant challenge. For this reason, it is critical that any proposed methods be rigorously evaluated before being used to draw conclusions about interactions. Author SummaryPathogen-pathogen interactions occur when infection with one pathogen either increases or decreases a persons risk of infection or illness due to a second, distinct pathogen. Because interactions affect several common human pathogens, including influenza and SARS-CoV-2, a better understanding of interactions could improve epidemic control. However, past work has shown that simple methods commonly used to study interactions can lead to inaccurate conclusions. Here, we tested four methods frequently used in other fields, including ecology and neuroscience, to see whether they may also be useful for identifying interactions. Specifically, we tested each method using simulated outbreak data generated from a mathematical model. We found that most methods struggled to correctly determine whether an interaction effect was present; in particular, methods often falsely identified interactions when none occurred. Although one of the tested methods, generalized additive models, performed comparatively well at identifying interactions, it provided relatively little additional information about the interactions. Because pathogen-pathogen interactions are so challenging to study, it is important that researchers rigorously test methods before applying them to interactions, so as not to publish potentially misleading results. More broadly, a complete understanding of interactions will likely require a variety of approaches, including both laboratory and modeling studies.

ecology↗

bioRxiv2025

Impacts of wastewater treatment and the exposome on antibiotic resistance gene dynamics: Insights from Guadeloupe, a French Caribbean Island

Wastewater treatment plants (WWTPs) are global hotspots for the dissemination of antibiotic resistance genes (ARGs), primarily driven by different anthropogenic activities. Chemical compounds commonly found in wastewater, including antibiotics, biocides, non-steroidal anti-inflammatory drugs (NSAIDs), and heavy metals, along with environmental factors such as temperature and precipitation, might enhance the selection of antibiotic-resistant bacteria (ARB) even at low concentrations, inadvertently promoting the proliferation of ARGs. Since WWTPs can vary in their removal efficiency and influent compositions, it is important to assess their impact in influencing the relative abundance of ARGs and pollutant concentrations in discharged effluent. We analyzed data collected in Guadeloupe, French Caribbean, using random forest and lasso to investigate changes in the resistome and exposome within untreated wastewater influents and treated effluents from three WWTPs. These WWTPs represented three distinct wastewater continuums--hospital-based, urban non-touristic, and urban touristic--each with unique influent compositions. The study, which spanned four sampling campaigns from September 2021 to January 2023, revealed that the reduction in ARG abundance by the WWTPs was lower than expected. Of the 16 clinically relevant genes and mobile genetic elements examined, the relative abundance of aph(3)-III, blaOXA, blaSHV, blaTEM, ermB, intI1, qnrS, and tetM was reduced by 59.8% to 89.9% across the three WWTPs. An increasing trend in the relative abundance of the mcr-1 gene was observed, showing a 9.52-fold increase in the treated effluent in the hospital continuum. Of the nine antibiotics tested, trimethoprim levels dropped by 70.8% in the touristic continuum and 78.8% in the non-touristic, while ciprofloxacin saw a significant 64.4% reduction only in the touristic continuum, while no other antibiotics or biocides among the eight tested were effectively removed. Regarding heavy metals, significant reductions were observed only in the non-touristic continuum, with cadmium (Cd), copper (Cu), and mercury (Hg) concentrations reduced by 32.5%, 21.1%, and 36.2%, respectively. Our findings provide insights into the associations between environmental factors and the relative abundance of ARGs in Guadeloupe. Generally, depending on the continuum, biocides, the antibiotic erythromycin, certain heavy metals (As, Cd, Cu, Cr, and Hg) and water temperature were associated with the relative abundance of clinically relevant ARGs. This study was not able to assess the impact of climate. Future research should include longitudinal studies with more frequent sampling to better investigate the evolution of antibiotic resistance in bacterial species and its associated drivers, including pollutants and climate-related factors.

microbiology↗

bioRxiv2022

Collateral impacts of pandemic COVID-19 drive the nosocomial spread of antibiotic resistance

Circulation of multidrug-resistant bacteria (MRB) in healthcare facilities is a major public health problem. These settings have been greatly impacted by the COVID-19 pandemic, notably due to surges in COVID-19 caseloads and the implementation of infection control measures. Yet collateral impacts of pandemic COVID-19 on MRB epidemiology remain poorly understood. Here, we present a dynamic transmission model in which SARS-CoV-2 and MRB co-circulate among patients and staff in a hospital population in an early pandemic context. Responses to SARS-CoV-2 outbreaks are captured mechanistically, reflecting impacts on factors relevant for MRB transmission, including contact behaviour, hand hygiene compliance, antibiotic prescribing and population structure. In a first set of simulations, broad parameter ranges are accounted for, representative of diverse bacterial species and hospital settings. On average, COVID-19 control measures coincide with MRB prevention, including fewer incident cases and fewer cumulative person-days of patient MRB colonization. However, surges in COVID-19 caseloads favour MRB transmission and lead to increased rates of antibiotic resistance, especially in the absence of concomitant control measures. In a second set of simulations, methicillin-resistant Staphylococcus aureus and extended-spectrum beta-lactamase-producing Escherichia coli are simulated in specific hospital wards and pandemic response scenarios. Antibiotic resistance dynamics are highly context-specific in these cases, and SARS-CoV-2 outbreaks significantly impact bacterial epidemiology only in facilities with high underlying risk of bacterial transmission. Crucially, antibiotic resistance burden is reduced in facilities with timelier, more effective implementation of COVID-19 control measures. This highlights the control of antibiotic resistance as an important collateral benefit of robust pandemic preparedness. Significance StatementImpacts of COVID-19 on the spread of antibiotic resistance are poorly understood. Here, an epidemiological model accounting for the simultaneous spread of SARS-CoV-2 and antibiotic-resistant bacteria is presented. The model is tailored to healthcare settings during the first wave of the COVID-19 pandemic, and accounts for hand hygiene, inter-individual contact behaviour, and other factors relevant for pathogen spread. Simulations demonstrate that public health policies enacted to slow the spread of COVID-19 also tend to limit bacterial transmission. However, surges in COVID-19 cases simultaneously select for higher rates of antibiotic resistance. Selection for resistance is thus mitigated by prompt implementation of effective COVID-19 prevention policies. This highlights the control of antibiotic resistance as an important collateral benefit of pandemic preparedness.

evolutionary biology↗

bioRxiv2022

COVID-19 pandemic responses may impact the spread of antibiotic-resistant bacteria: a modelling study

Non-pharmaceutical interventions implemented to block SARS-CoV-2 transmission in early 2020 led to global reductions in the incidence of invasive pneumococcal disease (IPD). By contrast, most European countries reported an increase in antibiotic resistance among invasive Streptococcus pneumoniae isolates from 2019 to 2020, while an increasing number of studies reported stable pneumococcal carriage prevalence over the same period. To disentangle the impacts of the COVID-19 pandemic on pneumococcal epidemiology in the community setting, we propose a mathematical model formalizing simultaneous transmission of SARS-CoV-2 and antibiotic-sensitive and -resistant strains of S. pneumoniae. To test hypotheses underlying these trends five mechanisms were built in into the model and examined: (1) a population-wide reduction of antibiotic prescriptions in the community, (2) lockdown effect on pneumococcal transmission, (3) a reduced risk of developing an IPD due to the absence of common respiratory viruses, (4) community azithromycin use in COVID-19 infected individuals, (5) and a longer carriage duration of antibiotic-resistant pneumococcal strains. Among 31 possible pandemic scenarios involving mechanisms individually or in combination, model simulations surprisingly identified only two scenarios that reproduced the reported trends in the general population. They included factors (1), (3), and (4). These scenarios replicated a nearly 50% reduction in annual IPD, and an increase in antibiotic resistance from 20% to 22%, all while maintaining a relatively stable pneumococcal carriage. Exploring further, higher SARS-CoV-2 R0 values and synergistic within-host virus- bacteria interaction mechanisms could have additionally contributed to the observed antibiotic resistance increase. Our work demonstrates the utility of the mathematical modeling approach in unraveling the complex effects of the COVID-19 pandemic responses on AMR dynamics.

evolutionary biology↗

bioRxiv2021

Modelling the impact of urban and hospital wastewaters eco-exposomes on the antibiotic-resistance dynamics.

Antibiotic-resistance emergence and selection have become major public health issues globally. The presence of antibiotic resistant bacteria (ARB) in natural and anthroposophical environments threatens to compromise the sustainability of care in human and animal populations. This study was undertaken to develop a simple model formalizing the selective impact of antibiotics and pollutants on the dynamics of bacterial resistance in water and use the model to analyze longitudinal spatiotemporal data collected in hospital and urban wastewaters. Longitudinal-sampling data were collected between 2012 and 2015 in four different locations in Haute-Savoie, France: hospital and urban wastewaters, before and after water-treatment plants. Concentration in three different types of compounds: 1) heavy metals 2) antibiotics and 3) surfactants; and abundance of 88 individual genes and mobile genetic elements, mostly conferring resistance to antibiotics, were simultaneously collected. A simple hypothesis-driven model describing the weekly ARB dynamics was proposed to fit available data by assuming normalized gene abundance to be proportional to ARB populations in water. Compounds impacts on the dynamics of 17 genes found in multiple sites were estimated. We found that while mercury and vancomycin had relevant effects on ARB dynamics, respectively positively affecting the dynamics of 10 and 12 identified genes, surfactants antagonistically affected genes dynamics (identified for three genes). This simple model enables analyzing the relationship between resistance-gene persistence in aquatic environments and specific compounds inherent to human activities. Applying our model to longitudinal data, we identified compounds that act as co-selectors for antibiotic resistance. HighlightsO_LIWe analyzed longitudinal wastewater resistance genes and environmental data C_LIO_LIWe developed a simple hypothesis-driven model to assess resistance selection C_LIO_LIMercury and vancomycin were key drivers of antibiotic resistance in wastewater C_LI

microbiology↗

bioRxiv2020

Drivers of ESBL-producing Escherichia coli dynamics in calf fattening farms: a modelling study

The contribution of bacteria in livestock to the global burden of antimicrobial resistance raises concerns worldwide. However, the dynamics of selection and diffusion of antimicrobial resistance in farm animals are not fully understood. Here, we used veal calf fattening farms as a model system, as they are a known reservoir of Extended Spectrum {beta}-Lactamase-producing Escherichia coli (ESBL-EC). Longitudinal data of ESBL-EC carriage and antimicrobial use (AMU) were collected from three veal calf farms during the entire fattening process. We developed 18 agent-based mechanistic models to assess different hypotheses regarding the main drivers of ESBL-EC dynamics in calves. The models were independently fitted to the longitudinal data using Markov Chain Monte Carlo and the best model was selected. Within-farm transmission between individuals and sporadic events of contamination were found to drive ESBL-EC dynamics on farms. In the absence of AMU, the median carriage duration of ESBL-EC was estimated to be 19.6 days (95% credible interval: [12.7; 33.3]). In the best model, AMU was found to influence ESBL-EC dynamics, by affecting ESBL-EC clearance rather than acquisition. This effect of AMU was estimated to decrease gradually after the end of exposure and to disappear after 62.5 days [50.0; 76.9]. Moreover, using a simulation study, we quantified the efficacy of ESBL-EC mitigation strategies. Decreasing ESBL-EC prevalence by 50% on arrival at the fattening farm reduced prevalence at slaughter age by 33.3%. Completely eliminating the use of selective antibiotics had a strong effect on average ESBL-EC prevalence (relative reduction of 79.6%), but the effect was mild if this use was only decreased by 50% compared to baseline (relative reduction of 3.7%).

microbiology↗

bioRxiv2019

Dynamics of Livestock-Associated Methicillin Resistant Staphylococcus aureus in pig farms networks: insight from mathematical modeling and French data

Livestock-associated methicillin resistant Staphylococcus aureus (LA-MRSA) colonizes livestock animals worldwide, especially pigs and calves. Although frequently carried asymptomatically, LA-MRSA can cause severe infections in humans. It is therefore important to better understand LA-MRSA spreading dynamics within pig farms and over pig farms networks, and to compare different strategies of control and surveillance. For this purpose, we propose a stochastic meta-population model of LA-MRSA spread along the French pig-farm network (n=10,542 farms), combining within- and between-farms dynamics, based on detailed data on breeding practices and pig exchanges between holdings. We calibrate the model using French epidemiological data. We then identify farm-level factors associated with the spreading potential of LA-MRSA in the network. We also show that, assuming control measures applied in a limited (n=100) number of farms, targeting farms depending on their centrality in the network is the only way to significantly reduce LA-MRSA global prevalence. Finally, we investigate the scenario of emergence of a new LA-MRSA strain, and find that the farms with the highest indegree would be the best sentinels for a targeted surveillance of such a strains introduction.

microbiology↗

bioRxiv2019

Clonal transmission and new mechanism of resistance to trimethoprim-sulfamethoxazole in Stenotrophomonas maltophilia strains isolated in a neonatology unit at Antananarivo, Madagascar, deciphered by whole genome sequence analysis

Stenotrophomonas maltophilia has been recognized as an emerging multidrug resistant organism in hospital settings due to its resistance to a broad range of antimicrobial agents. These include {beta}-lactams and aminoglycosides, afforded by the existence of intrinsic and acquired resistance mechanisms. Trimethoprim/sulfamethoxazole (SXT) is recommended as one of the best treatment choices against S. maltophilia infections; however increasing resistance to SXT has complicated the treatment. From July 2014 to March 2015, individuals and surfaces from a neonatology ward in Antananarivo, Madagascar, were longitudinally followed to assess the transmission of bacteria resistant to antibiotics between neonates, individuals (parents and nurses) and ward environments. Four S. maltophilia strains were successively isolated from a water-tap (N=1), from feces obtained from a newborn (N=1), and nursing staff (N=2). Antimicrobial susceptibility testing and whole genome sequencing were performed on each isolate. Based on coregenome alignment, all strains were identical and belonged to the new sequence type ST-288. They were resistant to trimethoprim-sulfamethoxazole, carbapenems and intermediate to levofloxacin. Each isolate carried the aadB, strA, strB and sul1 genes located in a class I integron but variants of the dfrA gene were absent. We assessed by PROVEAN analysis the single nucleotide mutations found in folA, folC and folM genes and only the mutation in folA (A114T:GCC[->]ACC) has an effect on the activity of trimethoprim. Our findings demonstrated the prolonged presence of SXT-resistant S. maltophilia in a clinical setting with consecutive transfers from the environment to a newborn and staff based on the isolation dates. We also hypothesized that single nucleotide mutations in folA could be responsible for trimethoprim resistance.

microbiology↗

bioRxiv2018

Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings

Antibiotic-resistance of hospital-acquired infections is a major public health issue. The worldwide emergence and diffusion of extended-spectrum {beta}-lactamase (ESBL)-producing Enterobacteriaceae, including Escherichia coli (ESBL-EC) and Klebsiella pneumoniae (ESBL-KP), is of particular concern. Preventing their nosocomial spread requires understanding their transmission. Using Close Proximity Interactions (CPIs), measured by wearable sensors, and weekly ESBL-EC- and ESBL-KP-carriage data, we traced their possible transmission paths among 329 patients in a 200-bed long-term care facility over 4 months. Based on phenotypically defined resistance profiles to 12 antibiotics, new bacterial acquisitions were tracked. Extending a previously proposed statistical method, the CPI networks ability to support observed incident colonization episodes of ESBL-EC and ESBL-KP was tested. Finally, mathematical modeling based on our findings assessed the effect of several infection-control measures. A potential infector was identified in the CPI network for 80% (16/20) of ESBL-KP acquisition episodes. The lengths of CPI paths between ESBL-KP incident cases and their potential infectors were shorter than predicted by chance (P = 0.02), indicating that CPI-network relationships were consistent with dissemination. Potential ESBL-EC infectors were identified for 54% (19/35) of the acquisitions, with longer-than-expected lengths of CPI paths. These contrasting results yielded differing impacts of infection control scenarios, with contact reduction interventions proving less effective for ESBL-EC than for ESBL-KP. These results highlight the widely variable transmission patterns among ESBL-producing Enterobacteriaceae species CPI networks supported ESBL-KP, but not ESBL-EC spread. These outcomes could help design more specific surveillance and control strategies to prevent in-hospital Enterobacteriaceae dissemination.\n\nAuthor summaryTracing extended-spectrum {beta}-lactamase (ESBL) dissemination in hospitals is an important step in the fight against the spread of multi-drug resistant bacteria. Indeed, understanding ESBL spreading dynamics will help identify efficient control interventions. In the i-Bird study, patients and hospital staff from a French long-term care facility in France carried a wearable sensor to capture their interactions at less than 1.5 meters, every 30 seconds over a 4-month period. Every week, patients were also swabbed to detect carriage of ESBL-producing Enterobacteriaceae. Based on the analysis of these longitudinal data, this study shows that ESBL-producing Klebsiella pneumoniae (ESBL-KP) mostly spreads during close-proximity interactions between individuals, while this is not the case for ESBL-producing Escherichia coli (ESBL-EC), suggesting that ESBL-KP but not ESBL-EC may be controlled by contact reduction interventions.

epidemiology↗

bioRxiv2018

Inference of significant microbial interactions from longitudinal metagenomics sequencing data

Data of next-generation sequencing (NGS) and their analysis have been facilitating advances in our understanding of microbial ecosystems such as human gut microbiota. However, inference of microbial interactions occurring within an ecosystem is still a challenge mainly due to metagenomics sequencing (e.g., 16S rDNA sequences) providing relative abundance of microbes instead of absolute cell count. In order to describe the population dynamics in microbial communities and estimate the involved microbial interactions, we introduce a procedure by integrating generalized Lotka-Volterra equations, forward stepwise regression and bootstrap aggregation. First, we successfully identify experimentally confirmed microbial interactions with relative abundance data of a cheese microbial community. Then, we apply the procedure to time-series of 16S rDNA sequences of gut microbiomes of children who were progressing to Type 1 diabetes (T1D progressors), and compare their gut microbial interactions to a healthy control group. Our results suggest that the number of inferred microbial interactions increased over time during the first three years of life. More microbial interactions are found in the gut flora of healthy children than the T1D progressors. The inhibitory effects from Actinobacteria and Bacilli to Bacteroidia, from Bacteroidia to Clostridia, and the benifit effect from Clostridia to Bacteroidia are shared between healthy children and T1D progressors. An inhibition of Clostridia by Gammaproteobacteria is found in healthy children that maintains through their first three years of life. This suppression appears in T1D progressors during the first year of life, which transforms to a commensalism relationship at the age of three years old. Gammaproteobacteria is found exerting an inhibition on Bacteroidia in the T1D progressors, which is not identified in the healthy controls.

ecology↗

bioRxiv2017

Influenza Interaction with Cocirculating Pathogens, and Its Impact on Surveillance, Pathogenesis and Epidemic Profile: A Key Role for Mathematical Modeling

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.\n\nAuthor SummaryInfluenza 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.

epidemiology↗