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

ABSTRACTEvidence 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 andStreptococcus 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 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.</jats:sec

Insights into Persistence Mechanisms of a Zoonotic Virus in Bat Colonies Using a Multispecies Metapopulation Model.

Rabies is a worldwide zoonosis resulting from Lyssavirus infection. In Europe, Eptesicus serotinus is the most frequently reported bat species infected with Lyssavirus, and thus considered to be the reservoir of European bat Lyssavirus type 1 (EBLV-1). To date, the role of other bat species in EBLV-1 epidemiology and persistence remains unknown. Here, we built an EBLV-1−transmission model based on local observations of a three-cave and four-bat species (Myotis capaccinii, Myotis myotis, Miniopterus schreibersii, Rhinolophus ferrumequinum) system in the Balearic Islands, for which a 1995-2011 serological dataset indicated the continuous presence of EBLV-1. Eptesicus serotinus was never observed in the system during the 16-year follow-up and therefore was not included in the model. We used the model to explore virus persistence mechanisms and to assess the importance of each bat species in the transmission dynamics. We found that EBLV-1 could not be sustained if transmission between M. schreibersii and other bat species was eliminated, suggesting that this species serves as a regional reservoir. Global sensitivity analysis using Sobol's method revealed that following the rate of autumn−winter infectious contacts, M. schreibersii's incubation- and immune-period durations, but not the infectious period length, were the most relevant factors driving virus persistence

Impact of Capsular Switch on Invasive Pneumococcal Disease Incidence in a Vaccinated Population

BACKGROUND: Despite the dramatic decline in the incidence of invasive pneumococcal disease (IPD) observed since the introduction of conjugate vaccination, it is feared that several factors may undermine the future effectiveness of the vaccines. In particular, pathogenic pneumococci may switch their capsular types and evade vaccine-conferred immunity. METHODOLOGY/PRINCIPAL FINDINGS: Here, we first review the literature and summarize the available epidemiological data on capsular switch for S. pneumoniae. We estimate the weekly probability that a persistently carried strain may switch its capsule from four studies, totalling 516 children and 6 years of follow-up, at 1.5x10(-3)/week [4.6x10(-5)-4.8x10(-3)/week]. There is not enough power to assess an increase in this frequency in vaccinated individuals. Then, we use a mathematical model of pneumococcal transmission to quantify the impact of capsular switch on the incidence of IPD in a vaccinated population. In this model, we investigate a wide range of values for the frequency of vaccine-selected capsular switch. Predictions show that, with vaccine-independent switching only, IPD incidence in children should be down by 48% 5 years after the introduction of the vaccine with high coverage. Introducing vaccine-selected capsular switch at a frequency up to 0.01/week shows little effect on this decrease; yearly, at most 3 excess cases of IPD per 10(6) children might occur due to switched pneumococcal strains. CONCLUSIONS: Based on all available data and model predictions, the existence of capsular switch by itself should not impact significantly the efficacy of pneumococcal conjugate vaccination on IPD incidence. This optimistic result should be tempered by the fact that the selective pressure induced by the vaccine is currently increasing along with vaccine coverage worldwide; continued surveillance of pneumococcal populations remains of the utmost importance, in particular during clinical trials of the new conjugate vaccines

Transmission Characteristics of the 2009 H1N1 Influenza Pandemic: Comparison of 8 Southern Hemisphere Countries

While in Northern hemisphere countries, the pandemic H1N1 virus (H1N1pdm) was introduced outside of the typical influenza season, Southern hemisphere countries experienced a single wave of transmission during their 2009 winter season. This provides a unique opportunity to compare the spread of a single virus in different countries and study the factors influencing its transmission. Here, we estimate and compare transmission characteristics of H1N1pdm for eight Southern hemisphere countries/states: Argentina, Australia, Bolivia, Brazil, Chile, New Zealand, South Africa and Victoria (Australia). Weekly incidence of cases and age-distribution of cumulative cases were extracted from public reports of countries' surveillance systems. Estimates of the reproduction numbers, R0, empirically derived from the country-epidemics' early exponential phase, were positively associated with the proportion of children in the populations (p = 0.004). To explore the role of demography in explaining differences in transmission intensity, we then fitted a dynamic age-structured model of influenza transmission to available incidence data for each country independently, and for all the countries simultaneously. Posterior median estimates of R0 ranged 1.2–1.8 for the country-specific fits, and 1.29–1.47 for the global fits. Corresponding estimates for overall attack-rate were in the range 20–50%. All model fits indicated a significant decrease in susceptibility to infection with age. These results confirm the transmissibility of the 2009 H1N1 pandemic virus was relatively low compared with past pandemics. The pattern of age-dependent susceptibility found confirms that older populations had substantial – though partial - pre-existing immunity, presumably due to exposure to heterologous influenza strains. Our analysis indicates that between-country-differences in transmission were at least partly due to differences in population demography

Modélisation mathématique de la dynamique de diffusion de bactéries résistantes aux antibiotiques : application au pneumocoque

The thesis aims at building mathematical models to study the dynamics of pneumococcal strains transmission in the community. The phenomenon of emergence and diffusion of resistant bacteria in populations involve microbial, individual and population scales simultaneously. That makes them complex and difficult to anticipate. In that context, modelling, which allows formalisation and simulation of the different scales, can help in analysing, predicting and understanding better the spread of bacteria. Three main issues are raised. What would be the impact of antibiotic exposure changes on resistances distributions? What would be the effects induced by the pneumococcal conjugate vaccine on the distribution of strains? Do viral respiratory infections and antibiotic consumption play a role in pneumococcal meningitis infections? Four compartmental models are developed to answer these questions. The results of our simulations reinforce the idea that antibiotic exposure is a major factor in the selection of antibiotic resistant bacteria. More precisely, they show that the choices of prescribed drugs and doses at the individual level could have a strong impact on the distributions of resistances in the community. Moreover, our results suggest that respiratory viruses, antibiotic consumption and resistance associated-fitness could explain part of the dynamics of pneumococcal meningitis in the community.L'objectif de cette thèse est de développer des outils de modélisation mathématique afin d'étudier la dynamique de transmission des souches de pneumocoque dans la communauté. La dynamique d'émergence et de diffusion de bactéries résistantes dans la population est difficile à anticiper : les phénomènes se produisent à différentes échelles (bactérie, hôte et population) et les bactéries circulent dans des environnements humains complexes (nombreux antibiotiques et vaccins). Dans ce cadre, la formalisation mathématique et la simulation peuvent contribuer à mieux comprendre et anticiper les phénomènes en jeu. Trois questions principales sont posées dans ce travail. Elles portent sur : l'effet d'une modification de l'exposition antibiotique sur la distribution des résistances du pneumocoque ; les conséquences de l'usage de vaccins conjugués sur les distributions de souches ; et l'étude de la dynamique d'incidence des méningites à pneumocoque. Pour y répondre, 4 modèles mathématiques compartimentaux spécifiques sont construits. Les résultats des simulations renforcent l'idée que l'exposition antibiotique est un facteur environnemental majeur pour la sélection des pneumocoques résistants aux antibiotiques. En particulier, ils mettent en évidence l'importance du choix des molécules antibiotiques utilisées et des doses prescrites sur la distribution des résistances dans la population. Ils suggèrent de plus que les virus hivernaux, la consommation antibiotique et l'épidémicité différenciée en fonction de la résistance pourraient expliquer en partie la dynamique des méningites à pneumocoque dans la population

Contributions of modelling for the control of COVID-19 nosocomial transmission

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COVID-19 containment measures and incidence of invasive bacterial disease

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A One‐Health Quantitative Model to Assess the Risk of Antibiotic Resistance Acquisition in Asian Populations: Impact of Exposure Through Food, Water, Livestock and Humans

International audienceAntimicrobial resistance (AMR) has become a major threat worldwide, especially in countries with inadequate sanitation and low antibiotic regulation. However, adequately prioritizing AMR interventions in such settings requires a quantification of the relative impacts of environmental, animal, and human sources in a One-Health perspective. Here, we propose a stochastic quantitative risk assessment model for the different components at interplay in AMR selection and spread. The model computes the incidence of AMR colonization in humans from five different sources: water or food consumption, contacts with livestock, and interhuman contacts in hospitals or the community, and combines these incidences into a per-year acquisition risk. Using data from the literature and Monte-Carlo simulations, we apply the model to hypothetical Asian-like settings, focusing on resistant bacteria that may cause infections in humans. In both scenarios A, illustrative of low-income countries, and B, illustrative of high-income countries, the overall individual risk of becoming colonized with resistant bacteria at least once per year is high. However, the average predicted incidence of colonization was lower in scenario B at 0.82 (CrI [0.13, 5.1]) acquisitions/person/year, versus 1.69 (CrI [0.66, 11.13]) acquisitions/person/year for scenario A. A high percentage of population with no access to improved water on premises and a high percentage of population involved in husbandry are shown to strongly increase the AMR acquisition risk. The One-Health AMR risk assessment framework we developed may prove useful to policymakers throughout Asia, as it can easily be parameterized to realistically reproduce conditions in a given country, provided data are available

Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control

International audienceThe human microbiome can protect against colonization with pathogenic antibiotic-resistant bacteria (ARB), but its impacts on the spread of antibiotic resistance are poorly understood. We propose a mathematical modeling framework for ARB epidemiology formalizing within-host ARB-microbiome competition, and impacts of antibiotic consumption on microbiome function. Applied to the healthcare setting, we demonstrate a trade-off whereby antibiotics simultaneously clear bacterial pathogens and increase host susceptibility to their colonization, and compare this framework with a traditional strain-based approach. At the population level, microbiome interactions drive ARB incidence, but not resistance rates, reflecting distinct epidemiological relevance of different forces of competition. Simulating a range of public health interventions (contact precautions, antibiotic stewardship, microbiome recovery therapy) and pathogens ( Clostridioides difficile , methicillin-resistant Staphylococcus aureus , multidrug-resistant Enterobacteriaceae) highlights how species-specific within-host ecological interactions drive intervention efficacy. We find limited impact of contact precautions for Enterobacteriaceae prevention, and a promising role for microbiome-targeted interventions to limit ARB spread

Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study

International audienceBackground: Epidemiological surveillance relies on microbial strain typing, which defines genomic relatedness among isolates to identify case clusters and their potential sources. Although predefined thresholds are often applied, known outbreak-specific features such as pathogen mutation rate and duration of source contamination are rarely considered. We aimed to develop a hypothesis-based model that estimates genetic distance thresholds and mutation rates for point-source single-strain food or environmental outbreaks. Methods: In this modelling study, we developed a forward model to simulate bacterial evolution at a specific mutation rate (μ) over a defined outbreak duration (D). From the distribution of genetic distances expected under the given outbreak parameters and sample isolation dates, we estimated a distance threshold beyond which isolates should not be considered as part of the outbreak. We embedded the model into a Markov Chain Monte Carlo inference framework to estimate the most probable mutation rate or time since source contamination, which are both often imprecisely documented. A simulation study validated the model over realistic durations and mutation rates. We then identified and analysed 16 published datasets of bacterial source-related outbreaks; datasets were included if they were from an identified foodborne outbreak and if whole-genome sequence data and collection dates for the described isolates were available. Findings: Analysis of simulated data validated the accuracy of our framework in both discriminating between outbreak and non-outbreak cases and estimating the parameters D and μ from outbreak data. Precision of estimation was much higher for high values of D and μ. Sensitivity of outbreak cases was always very high, and specificity in detecting non-outbreak cases was poor for low mutation rates. For 14 of the 16 outbreaks, the classification of isolates as being outbreak-related or sporadic is consistent with the original dataset. Four of these outbreaks included outliers, which were correctly classified as being beyond the threshold of exclusion estimated by our model, except for one isolate of outbreak 4. For two outbreaks, both foodborne Listeria monocytogenes, conclusions from our model were discordant with published results: in one outbreak two isolates were classified as outliers by our model and in another outbreak our algorithm separated food samples into one cluster and human samples into another, whereas the isolates were initially grouped together based on epidemiological and genetic evidence. Re-estimated values of the duration of outbreak or mutation rate were largely consistent with a priori defined values. However, in several cases the estimated values were higher and improved the fit with the observed genetic distance distribution, suggesting that early outbreak cases are sometimes missed. Interpretation: We propose here an evolutionary approach to the single-strain conundrum by estimating the genetic threshold and proposing the most probable cluster of cases for a given outbreak, as determined by its particular epidemiological and microbiological properties. This forward model, applicable to foodborne or environmental-source single point case clusters or outbreaks, is useful for epidemiological surveillance and may inform control measures. Funding: European Union Horizon 2020 Research and Innovation Programme