Les syndromes grippaux sont à l'origine des pathologies bénignes ou graves pouvant entrainer plusieurs millions de décès chaque année dans le monde. Ils s'expriment de façon épidémique, annuellement, et peuvent prendre un aspect pandémique. L'émergence d'une nouvelle souche de virus influenza A (pH1N1) en 2009 a suscité l'accroissement de la surveillance des virus grippaux. Par ailleurs, les syndromes diarrhéiques d'origine virale, représentent un problème majeur de santé publique chez les enfants de moins de 5 ans. Peu de données existent sur la circulation des virus grippaux et diarrhéiques au Gabon. Dans ce contexte, nous avons mis en place un réseau de surveillance des virus grippaux et diarrhéiques au Gabon. L'objectif de cette étude était de caractériser les virus responsables des syndromes grippaux et diarrhéiques dans quatre principales villes du Gabon. S'agissant des grippes, 1066 écouvillons nasaux ont été récoltés sur la période allant de Juillet 2009 à Juin 2011. Trois cent dix sept (317) selles ont été récoltées chez les enfants de moins de 5 ans dans la période allant de Mars 2010 à Juin 2011. Les étiologies virales ont été analysées par PCR en temps réel avec des amorces spécifiques des virus responsables des syndromes grippaux : les virus influenza A et B saisonnier, le virus influenza A pandémique (pH1N1), les parainfluenzavirus de type 1 à 4 (PIV1-4), le virus respiratoire syncytial (VRS), le métapneumovirus humain (hMPV), les coronavirus NL63 (HCoV-NL63), HKU1(HCoV-HKU1), OC43 (HCoV-OC43), 229E (HCoV-229E), les adénovirus (AdVs), les rhinovirus (HRVs), les parechovirus (HPeVs), les entérovirus (EVs). Les virus diarrhéiques recherchés ont été : les adénovirus, les norovirus de type 1 et 2 (NoV-I, NoV- II), les sapovirus (SaVs), les astrovirus (HAstVs) et les Rotavirus A (RVA). Les diversités génétiques ont été analysées par analyses phylogénétiques suivant les séquençages des fragments amplifiés. Parmi les écouvillons analysés, 61% (n=654) étaient positifs pour au moins un des virus recherchés : AdV (16%), PIVs (15%), virus influenza (13%), EV (12%), VRS (12%), HRV (8%), HCoVs (6,5%), hMPV (2%) et HPeV (0,5%). Les enfants de moins de cinq ans représentaient la population la plus susceptible (78%). Les co-infections virales ont été retrouvées dans près d'un tiers (1/3) des cas de syndromes grippaux : 25% (2 virus), 6% (3 virus) et 1 cas de co-infections par 4 virus. Elles concernent principalement les AdVs (41%) et EVs (43%). La saisonnalité des syndromes grippaux a été également mise en évidence : 70% surviennent pendant les saisons de pluies. La prévalence des étiologies virales des diarrhées chez les enfants de moins de 5 ans était de 60,9% (n=193). Les virus responsables de celles-ci étaient : RVA (21,7%), AdV (19,6%), NoV-I (9,1%), NoV-II (13,9%), SaV (9,5%) et HastV (6,3%). Parmi les AdV, le sérotype majoritaire était AdV-41 (espèce F) alors que le génotype majoritaire des astrovirus était HAstV-1. Nous avons obtenu un génotypage en G/P total ou partiel pour 59 patients. Les souches identifiées étaient : G1P[8] (8,5%), G2P[4] (3,4%), G3P[6] (1,7%), G6P[6] (40,7%), G12P[8] (3,4%), G1 (1,7%), G2 (3,4%), G3 (3,4%), G6 (13,5%), G12 (6,7%), P[6] (8,5%), P[8] (5,1%). Ce travail a permis la mise en place d'un réseau de surveillance des virus responsables des syndromes grippaux afin de mieux faire face aux épidémies et pandémies au Gabon. L'étude des syndromes diarrhéiques a permis d'identifier les souches circulant au Gabon, notamment celles de rotavirus ayant un impact en santé publique. Ces résultats permettent d'envisager une meilleure adaptation de la prise en charge thérapeutique et une réflexion en ce qui concerne la mise en place d'une stratégie vaccinale contre les rotavirus et les virus grippaux. / Influenza-like illness (ILI) is causing mild to severe illnesses that can cause million of deaths each year worldwide. They cause epidemics annually or pandemics. The emergence of a new strain of influenza A virus (pH1N1) in 2009 sparked increased surveillance of influenza viruses. In addition, diarrheal syndromes represent a major public health problem among children under 5 years old. Few data exist on the circulation of influenza and diarrhea viruses in Gabon. In this context, a network surveillance of ILI and diarrhea virus was established in Gabon. The objective of this study was to characterize the viruses responsible of influenza-like illness and diarrhea in four major cities of Gabon.1066 nasal swabs were collected from July 2009 to June 2011. Three hundred and seventeen (317) stools were collected from children under 5 years old from March 2010 to June 2011. Viral etiologies were analyzed by real-time PCR with primers specifics of viruses responsible of ILI: seasonal influenza A and B, pandemic influenza, parainfluenza viruses type 1-4 (PIV1-4), respiratory syncytial virus (RSV), human metapneumovirus (hMPV), coronaviruses NL63 (HCoV-NL63), HKU1 (HCoV-HKU1), OC43 (HCoV-OC43), 229E (HCoV-229E), adenoviruses (AdVs), rhinoviruses (HRVs), parechovirus (HPeVs), enteroviruses (EVs). The enteric viruses were: adenoviruses, noroviruses type 1, 2 (NoV-I, NoV- II), sapoviruses (SaVs), astroviruses (HAstVs) and rotaviruses A (RVA). Genetic diversity was analyzed by phylogenetic analysis following the sequencing of the amplified fragments.Among the swabs analyzed, 61% (n = 654) were positive for at least one virus: AdV (16%), PIVs (15%), virus influenza (13%), EV (12%), RSV (12%), HRV (8%), HCoVs (6.5%), hMPV (2%) and HPeV (0,5%). Children under five years old were the most susceptible population (78%). Viral co-infections were found in nearly one-third (1/3) cases of influenza-like illness: 25% (2 viruses), 6% (3 viruses) and 1 case of co-infection with four viruses. They mainly concerned AdV (41%) and EVs (43%). The seasonality of influenza-like illness has also been showed: 70% occured during the rainy seasons. The prevalence of viral etiologies of diarrhea in children under 5 years old was 60.9% (n = 193). The virus responsible of these were: RVA (21.7%), AdV (19.6%), NoV-I (9.1%), NoV-II (13.9%), SaV (9.5 %) and HastV (6.3%). Among the AdV, the majority serotype was AdV-41 (species F), while the majority of astrovirus genotype was HAstV-1. We got a total or partial genotyping G/P for 59 patients. The strains were identified: G1P[8] (8.5%), G2P[4] (3.4%), G3P[6] (1.7%), G6P[6] (40.7%), G12P[8] (3.4%), G1 (1.7%), G2 (3.4%), G3 (3.4%), G6 (13.5%), G12 (6.7%), P[6] (8.5%), P[8] (5.1%). This work allowed the establishment of a surveillance network of viruses responsible of ILI and diarrhea in order to deal with epidemics and pandemics in Gabon. The study of diarrheal syndromes identified strains circulating in Gabon, including rotavirus affecting public health. These results allow us to consider a better adaptation of therapeutic and reflection regarding the implementation of a vaccination strategy against rotavirus and influenza viruses.

<p>We evaluated the comparability of influenza-related events self-reported by research participants and their outpatient medical records using data collected from the Hutterite Influenza Prevention Trial. We also explored the implications of using data on influenza symptoms from both data sources, independently and in combination, as predictors of laboratory-confirmed influenza. Self-report of influenza symptoms, physician-diagnosed otitis media and antibiotics prescribed at outpatient consultations was collected from trial participants. Similar data were also collected by fax requests for medical record information to the medical facilities. We found lower rates of self-reported prevalence for fever, sore throat, earache and otitis media and higher rates of antibiotic prescriptions compared to the medical records. Total agreements between self-report and medical report of symptoms varied between 61% and 88%. Negative agreement was considerably higher than positive agreement for each symptom, except cough. Self report of otitis media was a very specific measure (93%), but had lower sensitivity (47%). Positive predictive value was moderate at 64% but negative predictive value was good at 86%. Self-reported antibiotic prescription was a highly sensitive measure (98%), but had low specificity (50%). Positive predictive value was high at 91% but negative predictive value was modest at 65%. Fever (on its own) and combined with cough and/or sore throat were highly correlated with laboratory-confirmed influenza for all data sources. The ILI surveillance definition of fever and sore throat, based on combined symptoms by both medical records and self report, was the best predictor laboratory confirmed influenza.</p> / Doctor of Philosophy (PhD)

During the early stages of any epidemic, simple interventions such as quarantine and isolation may be sufficient to halt the spread of a novel pathogen. However, should this opportunity be missed, substantially more resource-intensive, complex, and societally intrusive interventions may be required to achieve an acceptable outcome. These disparities place a differential on the value of a given unit of knowledge across the time-domains of an epidemic. Within this dissertation we explore these value-differentials via extension of the business concept of the time-value of knowledge and propose the C4 Response Model for organizing the research response to novel pathogenic outbreaks. First, we define the C4 Response Model as a progression from an initial data-hungry collect stage, iteration between open-science-centric connect stages and machine-learning centric calibrate stages, and a final visualization-centric convey stage. Secondly we analyze the trends in knowledge-building across the stages of epidemics with regard to open and closed access article publication, referencing, and citation. Thirdly, we demonstrate a Twitter message mapping application to assess the virality of tweets as a function of their source-profile category, message category, timing, urban context, tone, and use of bots. Finally, we apply an agent-based model of influenza transmission to explore the efficacy of combined antiviral, sequestration, and vaccination interventions in mitigating an outbreak of an influenza-like-illness (ILI) within a simulated military base population. We find that while closed access outbreak response articles use more recent citations and see higher mean citation counts, open access articles are published and referenced in significantly greater numbers and are growing in proportion. We observe that tweet viralities showed distinct heterogeneities across message and profile type pairing, that tweets dissipated rapidly across time and space, and that tweets published before high-tweet-volume time periods showed higher virality. Finally, we saw that while timely responses and strong pharmaceutical interventions showed the greatest impact in mitigating ILI transmission within a military base, even optimistic scenarios failed to prevent the majority of new cases. This body of work offers significant methodological contributions for the practice of computational epidemiology as well as a theoretical grounding for the further use of the C4 Response Model. / Doctor of Philosophy / During the early stages of an outbreak of disease, simple interventions such as isolating those infected may be sufficient to prevent further cases. However, should this opportunity be missed, substantially more complex interventions such as the development of novel pharmaceuticals may be required. This results in a differential value for specific knowledge across the early, middle, and late stages of epidemic. Within this dissertation we explore these differentials via extension of the business concept of the time-value of knowledge, whereby key findings may yield greater benefits during early epidemics. We propose the C4 Response Model for organizing research regarding this time-value. First, we define the C4 Response Model as a progression from an initial knowledge collection stage, iteration between knowledge connection stages and machine learning-centric calibration stages, and a final conveyance stage. Secondly we analyze the trends in knowledge-building across the stages of epidemics with regard to open and closed access scientific article publication, referencing, and citation. Thirdly, we demonstrate a Twitter application for improving public health messaging campaigns by identifying optimal combinations of source-profile categories, message categories, timing, urban origination, tone, and use of bots. Finally, we apply an agent-based model of influenza transmission to explore the efficacy of combined antiviral, isolation, and vaccination interventions in mitigating an outbreak of an influenza-like-illness (ILI) within a simulated military base population. We find that while closed access outbreak response articles use more recent citations and see higher mean citation counts, open access articles are growing in use and are published and referenced in significantly greater numbers. We observe that tweet viralities showed distinct benefits to certain message and profile type pairings, that tweets faded rapidly across time and space, and that tweets published before high-tweet-volume time periods are retweeted more. Finally, we saw that while early responses and strong pharmaceuticals showed the greatest impact in preventing influenza transmission within military base populations, even optimistic scenarios failed to prevent the majority to new cases. This body of work offers significant methodological contributions for the practice of computational epidemiology as well as a theoretical grounding for the C4 Response Model.

La surveillance de l’influenza s’appuie sur un large spectre de données, dont les données de surveillance syndromique provenant des salles d’urgences. De plus en plus de variables sont enregistrées dans les dossiers électroniques des urgences et mises à la disposition des équipes de surveillance. L’objectif principal de ce mémoire est d’évaluer l’utilité potentielle de l’âge, de la catégorie de triage et de l’orientation au départ de l’urgence pour améliorer la surveillance de la morbidité liée aux cas sévères d’influenza. Les données d’un sous-ensemble des hôpitaux de Montréal ont été utilisées, d’avril 2006 à janvier 2011. Les hospitalisations avec diagnostic de pneumonie ou influenza ont été utilisées comme mesure de la morbidité liée aux cas sévères d’influenza, et ont été modélisées par régression binomiale négative, en tenant compte des tendances séculaires et saisonnières. En comparaison avec les visites avec syndrome d’allure grippale (SAG) totales, les visites avec SAG stratifiées par âge, par catégorie de triage et par orientation de départ ont amélioré le modèle prédictif des hospitalisations avec pneumonie ou influenza. Avant d’intégrer ces variables dans le système de surveillance de Montréal, des étapes additionnelles sont suggérées, incluant l’optimisation de la définition du syndrome d’allure grippale à utiliser, la confirmation de la valeur de ces prédicteurs avec de nouvelles données et l’évaluation de leur utilité pratique. / Surveillance of influenza relies on a wide array of data, including emergency department based syndromic surveillance data. An increasing number of variables are recorded in emergency department electronic records and are available for surveillance. The main objective of this research is to evaluate the potential utility of age, triage scores, and disposition data for enhanced monitoring of the burden of severe influenza cases. Data from a subset of Montreal hospitals was used, from April 2006 to January 2011. Pneumonia and influenza hospitalizations were taken as a measure of the burden of severe influenza cases, and were modeled using a negative binomial regression approach, taking into account seasonal and secular trends. Age-, triage score-, and disposition-stratified influenza-like illness visits improved the fit of predictive models for pneumonia and influenza hospitalization, as compared to overall influenza-like illness visits. Before integration of these variables into the Montreal surveillance system, additional steps are suggested, including the optimization of an influenza-like illness syndrome definition, the confirmation of the value of these predictors using new data, and the evaluation of their practical utility.

This thesis describes how transmission of the 2009 A/H1N1 influenza pandemic in the United States varied geographically, with emphasis on population distribution and age structure. This is made possible by the availability of medical claims records maintained in the private sector that capture the weekly incidence of influenza-like illness in 834 US cities. First, a probabilistic method is developed to infer each city's outbreak onset time. This reveals a clear wave-like pattern of transmission originating in the south-eastern US. Then, a mechanistic mathematical model is constructed to describe the between-city transmission of the epidemic. A model selection procedure reveals that transmission to a city is modulated by its population size, surrounding population density, and possibly by students mixing in schools. Geographic variation in transmissibility is explored further by nesting a latent Gaussian process within the mechanistic transmission model, revealing a possible region of elevated transmissibility in the south-eastern US. Then, using the mechanistic model and a probabilistic back-tracing procedure, the geographic introduction sites (the `transmission hubs') of the outbreak are identified. The transmission hubs of the 2009 pandemic were generally mid-sized cities, contrasting with the conventional perspective that major outbreaks should start in large population centres with high international connectivity. Transmission is traced forward from these hubs to identify `basins of infection', or regions where outbreaks can be attributed with high probability to a particular hub. The city-level influenza data is also separated into 12 age categories. Techniques adapted from signal processing reveal that school-aged children may have been key drivers of the epidemic. Finally, to provide a point of comparison, the procedures described above are applied to the 2003-04 and 2007-08 seasonal influenza outbreaks. Since the 2007-08 outbreak featured three antigenically distinct strains of influenza, it is possible to identify which antigenic strains may have been responsible for infecting each transmission hub. These strains are identified using a probabilistic model that is joined with the geographic transmission model, providing a link between population dynamics and molecular surveillance.