Introdução: As infecções respiratórias causam elevadas morbidade e mortalidade, sendo os vírus os principais agentes destas doenças. O monitoramento e vigilância de vírus respiratórios, desde os mais conhecidos até os emergentes, são importantes para a gestão em saúde, orientando tempo de profilaxia e minimizando o impacto de epidemias nas comunidades. Objetivos: Estudar a epidemiologia molecular do vírus sincicial respiratório (VSR) e descrever a epidemiologia dos seguintes vírus: influenza (IF), influenza A (H1N1), adenovírus (AdV) e parainfluenza (PIV) no Hospital de Clínicas de Porto Alegre. Para isso foram conduzidos três estudos: (1) caracterização das infecções respiratórias causadas por VSR, IF, AdV e PIV em crianças; (2) validação de uma técnica de reação em cadeia da polimerase em tempo real (RT-PCR) para detecção de VSR A/B e IF A/B e (3) caracterização de genótipos do VRS em crianças com infecções comunitárias e hospitalares. Métodos: No primeiro estudo foram levantadas as seguintes variáveis: casos de infecções respiratórias por VSR, AdV, PIV ou IF e H1N1; internações em enfermarias hospitalares e internações em unidades de terapia intensiva (UTI); infecções hospitalares e taxas de letalidade. As variáveis foram coletadas durante o atendimento de crianças (idade 0-12 anos) no HCPA entre 2007 e 2010. No segundo estudo, os alvos do ensaio de RT-PCR foram: o gene da proteína da matriz de IFA, o gene da hemaglutinina do IFB e o gene da nucleoproteína de RSVA/B. A especificidade do ensaio e seu limite de detecção foram determinados. Uma comparação entre RT-PCR e imunofluorescência indireta foi realizada. No terceiro estudo, 63 isolados de VSR (21 de origem nosocomial e 42 adquiridos na comunidade) foram sequenciados para estabelecer uma análise filogenética deste vírus. Resultados: Cada um dos vírus estudados apresentou um comportamento diferente. O VSR demonstrou ser o principal agente envolvido em infecções nosocomiais. Já os pacientes com AdV, bem como o VSR, apresentaram altas taxas de admissão em UTI em 2007 e 2010. O AdV e o H1N1 tiveram as maiores taxas de letalidade. O ensaio de RT-PCR mostrou-se específico e foi mais sensível do que a imunofluorescência, sendo capaz de detectar co-infecções. Os seguintes limites de detecção foram obtidos: 1 cópia/mL para a IFA, 10 cópias/mL para IFB, 5 cópias/mL para RSVA e 250 cópias/mL para RSVB. A investigação dos genótipos de VSR revelou que todos os isolados VSRA circulantes eram do mesmo grupo filogenético, o genótipo NA1 de origem japonesa. Por outro lado, os isolados VSRB foram distribuídos em grupos diferentes: BA4, POA1, POA2, POA3 e POA4. Este estudo foi o primeiro que descreveu a circulação do genótipo NA1 no Brasil, bem como quatro novos genótipos (POA1, POA2, POA3 e POA4). Conclusão: Os resultados obtidos no primeiro estudo demonstram o impacto das epidemias sazonais de vírus respiratórios. O segundo estudo corroborou relatos da literatura que técnicas moleculares, como RT-PCR, são adequadas para a detecção de vírus respiratórios. O terceiro estudo relatou genótipos emergentes de VSR. Estudos de vigilância como os descritos acima deveriam ser conduzidos periodicamente para acompanhar o padrão de comportamento dos vírus na população alvo. / Background: Respiratory tract infections of viral origin are a major cause of morbidity and mortality worldwide. Surveillance of well-known viruses and emerging threats is important for management, prophylaxis and to minimize impact on the community. Objective: To study the molecular epidemiology of respiratory syncytial virus (RSV) and describe the epidemiology of viruses: influenza (IF), influenza A (H1N1), adenovirus (AdV) and parainfluenza (PIV) at Hospital de Clinicas de Porto Alegre. Three studies were performed: (1) characterization of respiratory infections caused by RSV, IF, AdV and PIV in children, (2) validation of a technique of polymerase chain reaction in real-time (RT-PCR) to detect RSVA/B and IFA/B and (3) detection of genotypes of RSV in children with communityand hospital-acquired infection. Methods: In the first study, variables such as number of cases of viral (RSV, AdV, PIV or IF and H1N1) infection, hospitalizations in general wards and intensive care units (ICUs), nosocomial infections and lethality rates were collected. These variables obtained from each children (age 0-12 years) between 2007 and 2010. In the second study the assay RT-PCR was used to target the matrix gene of IFA, the hemagglutinin gene of IFB and the nucleoprotein gene of RSVA/B. The specificity of the assay and its limit of detection were determined. A comparison of RT-PCR and indirect immunofluorescence was performed. In the third study, 63 RSV isolates (21 nosocomial and 42 community-acquired) were submitted to sequencing to establish a phylogenetic analysis of this virus. Results: The different viruses presented a diversity of behaviors according to hospitalization, nosocomial outbreaks or lethality in children. RSV accounted for most nosocomial infections. Rates of ICU admission for AdV and RSV infection were highest in 2007 and 2010. During 2008–2009, H1N1 and AdV had the highest ICU admission rates. AdV and H1N1 had the highest lethality rates. The RT-PCR assay was more sensitive than immunofluorescence and it was able to detect viral co-infections. Futhermore, the limits of detection were 1 copy/μL for IFA, 10 copies/μL for IFB, 5 copies/μL for RSVA, and 250 copies/μL for RSVB. The genotyping study showed that hospital- and community-acquired RSVA isolates were from the same phylogenetic group (the same group of the NA1 Japanese isolates). Conversely, RSVB isolates were distributed across different groups: BA4, POA1, POA2, POA3 and POA4. This was the first study to describe circulation of the NA1 genotype in Brazil, as well as four RSVB genotypes: POA1, POA2, POA3 and POA4. Conclusion: The results obtained in the first study demonstrate the impact of seasonal epidemics of respiratory viruses. The second study confirmed that molecular techniques such as RT-PCR, are suitable for the detection of respiratory viruses. The third study reported emerging genotypes of RSV. Surveillance studies such as this should be performed periodically to monitor the behavior pattern of the virus in the target population.

Introdução: As infecções respiratórias causam elevadas morbidade e mortalidade, sendo os vírus os principais agentes destas doenças. O monitoramento e vigilância de vírus respiratórios, desde os mais conhecidos até os emergentes, são importantes para a gestão em saúde, orientando tempo de profilaxia e minimizando o impacto de epidemias nas comunidades. Objetivos: Estudar a epidemiologia molecular do vírus sincicial respiratório (VSR) e descrever a epidemiologia dos seguintes vírus: influenza (IF), influenza A (H1N1), adenovírus (AdV) e parainfluenza (PIV) no Hospital de Clínicas de Porto Alegre. Para isso foram conduzidos três estudos: (1) caracterização das infecções respiratórias causadas por VSR, IF, AdV e PIV em crianças; (2) validação de uma técnica de reação em cadeia da polimerase em tempo real (RT-PCR) para detecção de VSR A/B e IF A/B e (3) caracterização de genótipos do VRS em crianças com infecções comunitárias e hospitalares. Métodos: No primeiro estudo foram levantadas as seguintes variáveis: casos de infecções respiratórias por VSR, AdV, PIV ou IF e H1N1; internações em enfermarias hospitalares e internações em unidades de terapia intensiva (UTI); infecções hospitalares e taxas de letalidade. As variáveis foram coletadas durante o atendimento de crianças (idade 0-12 anos) no HCPA entre 2007 e 2010. No segundo estudo, os alvos do ensaio de RT-PCR foram: o gene da proteína da matriz de IFA, o gene da hemaglutinina do IFB e o gene da nucleoproteína de RSVA/B. A especificidade do ensaio e seu limite de detecção foram determinados. Uma comparação entre RT-PCR e imunofluorescência indireta foi realizada. No terceiro estudo, 63 isolados de VSR (21 de origem nosocomial e 42 adquiridos na comunidade) foram sequenciados para estabelecer uma análise filogenética deste vírus. Resultados: Cada um dos vírus estudados apresentou um comportamento diferente. O VSR demonstrou ser o principal agente envolvido em infecções nosocomiais. Já os pacientes com AdV, bem como o VSR, apresentaram altas taxas de admissão em UTI em 2007 e 2010. O AdV e o H1N1 tiveram as maiores taxas de letalidade. O ensaio de RT-PCR mostrou-se específico e foi mais sensível do que a imunofluorescência, sendo capaz de detectar co-infecções. Os seguintes limites de detecção foram obtidos: 1 cópia/mL para a IFA, 10 cópias/mL para IFB, 5 cópias/mL para RSVA e 250 cópias/mL para RSVB. A investigação dos genótipos de VSR revelou que todos os isolados VSRA circulantes eram do mesmo grupo filogenético, o genótipo NA1 de origem japonesa. Por outro lado, os isolados VSRB foram distribuídos em grupos diferentes: BA4, POA1, POA2, POA3 e POA4. Este estudo foi o primeiro que descreveu a circulação do genótipo NA1 no Brasil, bem como quatro novos genótipos (POA1, POA2, POA3 e POA4). Conclusão: Os resultados obtidos no primeiro estudo demonstram o impacto das epidemias sazonais de vírus respiratórios. O segundo estudo corroborou relatos da literatura que técnicas moleculares, como RT-PCR, são adequadas para a detecção de vírus respiratórios. O terceiro estudo relatou genótipos emergentes de VSR. Estudos de vigilância como os descritos acima deveriam ser conduzidos periodicamente para acompanhar o padrão de comportamento dos vírus na população alvo. / Background: Respiratory tract infections of viral origin are a major cause of morbidity and mortality worldwide. Surveillance of well-known viruses and emerging threats is important for management, prophylaxis and to minimize impact on the community. Objective: To study the molecular epidemiology of respiratory syncytial virus (RSV) and describe the epidemiology of viruses: influenza (IF), influenza A (H1N1), adenovirus (AdV) and parainfluenza (PIV) at Hospital de Clinicas de Porto Alegre. Three studies were performed: (1) characterization of respiratory infections caused by RSV, IF, AdV and PIV in children, (2) validation of a technique of polymerase chain reaction in real-time (RT-PCR) to detect RSVA/B and IFA/B and (3) detection of genotypes of RSV in children with communityand hospital-acquired infection. Methods: In the first study, variables such as number of cases of viral (RSV, AdV, PIV or IF and H1N1) infection, hospitalizations in general wards and intensive care units (ICUs), nosocomial infections and lethality rates were collected. These variables obtained from each children (age 0-12 years) between 2007 and 2010. In the second study the assay RT-PCR was used to target the matrix gene of IFA, the hemagglutinin gene of IFB and the nucleoprotein gene of RSVA/B. The specificity of the assay and its limit of detection were determined. A comparison of RT-PCR and indirect immunofluorescence was performed. In the third study, 63 RSV isolates (21 nosocomial and 42 community-acquired) were submitted to sequencing to establish a phylogenetic analysis of this virus. Results: The different viruses presented a diversity of behaviors according to hospitalization, nosocomial outbreaks or lethality in children. RSV accounted for most nosocomial infections. Rates of ICU admission for AdV and RSV infection were highest in 2007 and 2010. During 2008–2009, H1N1 and AdV had the highest ICU admission rates. AdV and H1N1 had the highest lethality rates. The RT-PCR assay was more sensitive than immunofluorescence and it was able to detect viral co-infections. Futhermore, the limits of detection were 1 copy/μL for IFA, 10 copies/μL for IFB, 5 copies/μL for RSVA, and 250 copies/μL for RSVB. The genotyping study showed that hospital- and community-acquired RSVA isolates were from the same phylogenetic group (the same group of the NA1 Japanese isolates). Conversely, RSVB isolates were distributed across different groups: BA4, POA1, POA2, POA3 and POA4. This was the first study to describe circulation of the NA1 genotype in Brazil, as well as four RSVB genotypes: POA1, POA2, POA3 and POA4. Conclusion: The results obtained in the first study demonstrate the impact of seasonal epidemics of respiratory viruses. The second study confirmed that molecular techniques such as RT-PCR, are suitable for the detection of respiratory viruses. The third study reported emerging genotypes of RSV. Surveillance studies such as this should be performed periodically to monitor the behavior pattern of the virus in the target population.

Infectious diseases are a very hot topic nowadays in society. In recent years, there were several epidemics, infectious diseases (hepatitis A, Avian influenza, pandemic influenza A) and nobody knows when will the next "new" infection. At the outbreak of epidemics, there is great interest in the company to obtain information about the disease, but often also to unnecessary panic, because the media often publish incorrect information. It would be preferable, in order to improve public awareness and prevent the unnecessary spread of disease. Great emphasis should be given to prevent the disease - primarily on immunization, hygiene rules and principles of safe sex. Quality information should be mainly from teachers in teaching their pupils, as is clear from research books, infectious diseases are only mentioned in textbooks and the number of substantive information in them is missing. The fact that pupils of primary schools and grammar schools with basic information on infectious diseases do not meet in the classroom, evidenced by the results of a survey carried out in the 6th classes and first year at selected elementary schools and grammar schools in Prague. To raise awareness of the pupils in school was to create a methodical manual for teachers, which summarizes information about bacterial and...

Cytotoxic T-lymphocytes (CTLs) are important components of the adaptive immune system and function by scanning the intracellular environment so as to detect and de-stroy infected cells. CTL responses play a major role in controlling virus-infected cells such as in HIV or influenza and cells infected with intracellular bacteria such as in tuberculosis. To do so they require the antigens to be presented to them, which is fulfilled by the major histocompatibility complex (MHC), commonly known as human leukocyte antigen or HLA molecules in humans. Recognition of antigenic peptides to Class-1 HLA molecules is a prerequisite for triggering CTL immune responses. Individuals differ significantly in their ability to respond to an infection. Among the factors that govern the outcome of an infection, HLA polymorphism in the host is one of the most important. Despite a large body of work on HLA molecules, much remains to be understood about the relationship between HLA diversity and disease susceptibility. High complexity arises due to HLA allele polymorphism, extensive antigen cross-presentability, and host-pathogen heterogeneity. A given allele can recognize a number of different peptides from various pathogens and a given peptide can also bind to a number of different individuals. Thus, given the plurality in peptide-allele pairs and the large number of alleles, understanding the differences in recognition profiles and the implications that follow for disease susceptibilities require mathematical modelling and computational analysis. The main objectives of the thesis were to understand heterogeneity in antigen presentation by HLA molecules at different scales and how that heterogeneity translates to variations in disease susceptibilities and finally the disease dynamics in different populations. Towards this goal, first the variations in HLA alleles need to be characterized systematically and their recognition properties understood. A structure-based classification of all known HLA class-1 alleles was therefore attempted. In the process, it was also of interest to see if understanding of sub-structures at the binding grooves of HLA molecules could help in high confidence prediction of epitopes for different alleles. Next, the goal was to understand how HLA heterogeneity affect disease susceptibilities and disease spread in populations. This was studied at two different levels. Firstly, modelling the HLA genotypes and CTL responses in different populations and assessing how they recognized epitopes from a given virus. The second approach involved modelling the disease dynamics given the predicted susceptibilities in different populations. Influenza H1N1 infection was used as a case study. The specific objectives addressed are: (a) To develop a classification scheme for all known HLA class-1 alleles that can explain epitope recognition profiles and further to dissect the physic-chemical features responsible for differences in peptide specificities, (b) A statistical model has been derived from a large dataset of HLA-peptide complexes. The derived model was used to identify the interdependencies of residues at different peptide and thereby, rationalize the HLA class-I allele binding specificity at a greater detail, (c) To understand the effect of HLA heterogeneity on CTL mediated disease response. A model of HLA genotypes for different populations was required for this, which was constructed and used for estimating disease response to H1N1 via the prediction of epi-topes and (d) To model disease dynamics in different populations with the knowledge of the CTL response-grouping and to evaluate the effect of heterogeneity on different vaccination strategies. Each of the four objectives listed above are described subsequently in chapters 2 to 5, followed by Chapter 6 which summarises the findings from the thesis and presents future directions. Chapter 1 presents an introduction to the importance of the function of HLA molecules, describes structural bioinformatics as a discipline and the methods that are available for it. The chapter also describes different mathematical modelling strategies available to study host immune responses. Chapter 2 describes a novel method for structure-based hierarchical classification of HLA alleles. Presently, more than 2000 HLA class-I alleles are reported, and they vary only across peptide-binding grooves. The polymorphism they exhibit, enables them to bind to a wide range of peptide antigens from diverse sources. HLA molecules and peptides present a complex molecular recognition pattern due to multiplicity in their associations. Thus, a powerful grouping scheme that not only provides an insightful classification, but is also capable of dissecting the physicochemical basis of recognition specificity is necessary to address this complexity. The study reports a hierarchical classification of 2010 class-I alleles by using a systematic divisive clustering method. All-pair distances of alleles were obtained by comparing binding pockets in the structural models. By varying the similarity thresholds, a multilevel classification with 7 supergroups was derived, each further categorized to yield a total of 72 groups. An independent clustering scheme based only on the similarities in their epitope pools correlated highly with pocket-based clustering. Physicochemical feature combinations that best explains the basis for the observed clustering are identified. Mutual information calculated for the set of peptide ligands enables identification of binding site residues that contribute to peptide specificity. The grouping of HLA molecules achieved here will be useful for rational vaccine design, understanding disease susceptibilities and predicting risk of organ transplants. The results are presented in an interactive web- server http://proline.iisc.ernet.in/hlaclassify. In Chapter 3, the knowledge of structural features responsible for generating peptide recognition specificities are first analysed and then utilized for predicting T-cell epi-topes for any class-1 HLA allele. Since identification of epitopes is critical and central to many of the questions in immunology, a study of several HLA-peptide complexes is carried out at the structural level and factors are identified that discriminate good binder peptides from those that do not. T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics, critically dependent on experimentally obtained training data. Analysis of the training set from IEDB for several alleles indicate that sampling of the peptide space is extremely sparse covering only a tiny fraction of all possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction. A new epitope prediction method is therefore developed. The method has four distinct modules, (a) structural modelling, estimating statistical pair-potentials and constraint derivation, (b) implicit modelling and interaction profiling, (c) binding affinity prediction through feature representation and (d) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles . HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any HLA Class-1 allele, by estimating binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It stands on the strength of mechanistic understanding of HLA-peptide recognition and provides an estimate of the total ligand space for each allele. The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy. In chapter 4, the effect of genetic heterogeneity on disease susceptibilities are investigated. Individuals differ significantly in their ability to respond to an infection. Among the factors that govern the outcome of an infection, HLA polymorphism in the host is one of the most important. Despite a large body of work on HLA molecules, much remains to be understood about how host HLA diversity affects disease susceptibilities. High complexity due to polymorphism, extensive cross-presentability among HLA alleles, host and pathogen heterogeneity, demands for an investigation through computational approaches. Host heterogeneity in a population is modelled through a molecular systems approach starting with mining ‘big data’ from literature. The in-sights derived through this is used to investigate the effect of heterogeneity in a population in terms of the impact it makes on recognizing a pathogen. A case study of influenza virus H1N1 infection is presented. For this, a comprehensive CTL immunome is defined by taking a consensus prediction by three distinct methods. Next, HLA genotypes are constructed for different populations using a probabilistic method. Epidemic incidences in general are observed to correlate with poor CTL response in populations. From this study, it is seen that large populations can be classified into a small number of groups called response-types, specific to a given viral strain. Individuals of a response type are expected to exhibit similar CTL responses. Extent of CTL responses varies significantly across different populations and increases with increase in genetic heterogeneity. Overall, the study presents a conceptual advance towards understanding how genetic heterogeneity influences disease susceptibility in individuals and in populations. Lists of top-ranking epitopes and proteins are also derived, ranked on the basis of conservation, antigenic cross-reactivity and population coverage, which pro- vide ready short-lists for rational vaccine design (flutope). Next, in Chapter 5, the effect of genetic heterogeneity on disease dynamics has been investigated. A mathematical framework has been developed to incorporate the heterogeneity information in the form of response-types described in the previous chap-ter. The spread of a disease in a population is a complex process, controlled by various factors, ranging from molecular level recognition events to socio-economic causes. The ‘response-typing’ described in the previous chapter allows identification of distinct groups of individuals, each with a different extent of susceptibility to a given strain of the virus. 3 different approaches are used for modelling: (i) an SIR model where different response types are considered as partitions of each S, I and R compartment. Initially SIR models are developed, such that the S compartment is sub-divided into further groups based on the ‘response-types’ obtained in the previous chapter. This analysis shows an effect in infection sweep time, i.e., how long the infection stays in the population. A stochastic model incorporates the environmental noise due to random variation in population influx, due to birth, death or migration. The system is observed to show higher stability in the presence of genetic heterogeneity. As the contagion spreads only through direct host to host contact. The topology of the contact network, plays major role in deciding the extent of disease dynamics. An agent based computational framework has been developed for modelling disease spread by considering spatial distribution of the agents, their movement patterns and resulting contact probabilities. The agent-based model (ABM) incorporates the temporal patterns of contacts. The ABM is based on a city block model and captures movement of individuals parametrically. A new concept of system ‘characteristic time’ has been introduced in context of a time-evolving network. ‘Characteristic time’ is the minimum time required to ensure, every individual is connected to all other individuals, in the time aggregated contact network. For any given temporal system, disease time must exceed ‘characteristic time’ in order to spread throughout the population. Shorter ‘characteristic time’ of the system is suggestive of faster spread of the disease. A disease spread network is constructed which shows how the disease spreads from one infected individual to others in the city, given the contact rules and their relative susceptibilities to that viral strain. A high degree of population heterogeneity is seen to results in longer disease residence time. Susceptible individuals preferentially get infected first thereby exposing more susceptible individuals to the disease. Vaccination strategies are derived from the model, which indicates that vaccinating only 20% of the agents, who are hub nodes or highly central nodes and who also have a high degree to susceptible agents, lead to high levels of herd immunity and can confer protection to the rest of the population. Overall, the thesis has provided biologically meaningful classification of all known HLA class-1 alleles and has unravelled the physico-chemical basis for their peptide recognition specificities. The thesis also presents a new algorithm for estimating pep-tide binding affinities and consequently predicting epitopes for all alleles. Finally the thesis presents a conceptual advance in relating HLA diversity to disease susceptibilities and explains how different populations can respond differently to a given infection. A case study with the influenza H1N1 virus identified populations who are most susceptible and those who are least susceptible, in the process identifying important epitopes and responder alleles, providing important pointers for vaccine design. The influence of heterogeneity and response-typing on disease dynamics is also presented for influenza H1N1 infection, which has led to the rational identification of effective vaccination strategies. The methods and concepts developed here are fairly generic and can be adapted easily for studying other infectious diseases as well. Three new web-resources, a) HLAclassify, b) HLaffy and c) Flutope have been developed, which host pre-computed results as well as allow interactive querying to an user to perform analysis with a specific allele, peptide or a pathogenic genome sequence.

Introduction: Epidemiological studies have indicated that 5-38% of influenza like illnesses (ILI) develop into severe disease due to, among others, factors such as; underlying chronic diseases, age, pregnancy, and viral mutations. There are suggestions that dual or multiple virus infections may affect disease severity. This study investigated the association between co-infection between influenza A viruses and other respiratory viruses and disease severity. Methodology: Datum for samples from North West England tested between January 2007 and June 2012 was analysed for patterns of co-infection between influenza A viruses and ten respiratory viruses. Risk of hospitalization to a general ward ICU or death in single versus mixed infections was assessed using multiple logistic regression models. Results: One or more viruses were identified in 37.8% (11,715/30,975) of samples, of which 10.4% (1,214) were mixed infections and 89.6% (10,501) were single infections. Among patients with influenza A(H1N1)pdm09, co-infections occurred in 4.7% (137⁄2,879) vs. 6.5% (59⁄902) in those with seasonal influenza A virus infection. In general, patients with mixed respiratory virus infections had a higher risk of admission to a general ward (OR: 1.43, 95% CI: 1.2 – 1.7, p = <0.0001) than those with a single infection. Co-infection between seasonal influenza A viruses and influenza B virus was associated with a significant increase in the risk of admission to ICU/ death (OR: 22.0, 95% CI: 2.21 – 219.8 p = 0.008). RSV/seasonal influenza A viruses co-infection also associated with increased risk but this was not statistically significant. For the pandemic influenza A(H1N1)pdm09 virus, RSV and AdV co-infection increased risk of hospitalization to a general ward, whereas Flu B increased risk of admission to ICU/ death, but none of these were statistically significant. Considering only single infections, RSV and hPIV1-3 increased risk of admission to a general ward (OR: 1.49, 95% CI: 1.28 – 1.73, p = <0.0001 and OR: 1.34, 95% CI: 1.003 – 1.8, p = 0.05) and admission to ICU/ death (OR: 1.5, 95% CI: 1.20 – 2.0, p = <0.0001 and OR: 1.60, 95% CI: 1.02 – 2.40, p = 0.04). Conclusion: Co-infection is a significant predictor of disease outcome; there is insufficient public health data on this subject as not all samples sent for investigation of respiratory virus infection are tested for all respiratory viruses. Integration of testing for respiratory viruses’ co-infections into routine clinical practice and R&D on integrated drugs and vaccines for influenza A&B, RSV, and AdV, and development of multi-target diagnostic tests is encouraged.