UK circulating strains of human parainfluenza 3: an amplicon based next generation sequencing method and phylogenetic analysis.

Background: Human parainfluenza viruses type 3 (HPIV3) are a prominent cause of respiratory infection with a significant impact in both pediatric and transplant patient cohorts.  Currently there is a paucity of whole genome sequence data that would allow for detailed epidemiological and phylogenetic analysis of circulating strains in the UK. Although it is known that HPIV3 peaks annually in the UK, to date there are no whole genome sequences of HPIV3 UK strains available.  Methods: Clinical strains were obtained from HPIV3 positive respiratory patient samples collected between 2011 and 2015.  These were then amplified using an amplicon based method, sequenced on the Illumina platform and assembled using a new robust bioinformatics pipeline. Phylogenetic analysis was carried out in the context of other epidemiological studies and whole genome sequence data currently available with stringent exclusion of significantly culture-adapted strains of HPIV3. Results: In the current paper we have presented twenty full genome sequences of UK circulating strains of HPIV3 and a detailed phylogenetic analysis thereof.  We have analysed the variability along the HPIV3 genome and identified a short hypervariable region in the non-coding segment between the M (matrix) and F (fusion) genes. The epidemiological classifications obtained by using this region and whole genome data were then compared and found to be identical. Conclusions: The majority of HPIV3 strains were observed at different geographical locations and with a wide temporal spread, reflecting the global distribution of HPIV3. Consistent with previous data, a particular subcluster or strain was not identified as specific to the UK, suggesting that a number of genetically diverse strains circulate at any one time. A small hypervariable region in the HPIV3 genome was identified and it was shown that, in the absence of full genome data, this region could be used for epidemiological surveillance of HPIV3.Wellcome Open Grant PHE PhD studentshi

Virus-virus interactions impact the population dynamics of influenza and the common cold

The human respiratory tract hosts a diverse community of cocirculating viruses that are responsible for acute respiratory infections. This shared niche provides the opportunity for virus–virus interactions which have the potential to affect individual infection risks and in turn influence dynamics of infection at population scales. However, quantitative evidence for interactions has lacked suitable data and appropriate analytical tools. Here, we expose and quantify interactions among respiratory viruses using bespoke analyses of infection time series at the population scale and coinfections at the individual host scale. We analyzed diagnostic data from 44,230 cases of respiratory illness that were tested for 11 taxonomically broad groups of respiratory viruses over 9 y. Key to our analyses was accounting for alternative drivers of correlated infection frequency, such as age and seasonal dependencies in infection risk, allowing us to obtain strong support for the existence of negative interactions between influenza and noninfluenza viruses and positive interactions among noninfluenza viruses. In mathematical simulations that mimic 2-pathogen dynamics, we show that transient immune-mediated interference can cause a relatively ubiquitous common cold-like virus to diminish during peak activity of a seasonal virus, supporting the potential role of innate immunity in driving the asynchronous circulation of influenza A and rhinovirus. These findings have important implications for understanding the linked epidemiological dynamics of viral respiratory infections, an important step towards improved accuracy of disease forecasting models and evaluation of disease control interventions

Reducing Respiratory Virus Testing In Hospitalized Children With Machine Learning And Text Mining

Despite pressure from the federal government for US hospitals to adopt electronic medical records systems (EMR), the benefits of adopting such systems have not been fully realized. One proposed advantage of EMRs involves secondary use, in which personal health information is used for purposes other than direct health care delivery, particularly quality improvement. We sought to determine whether information recorded in the EMR could improve diagnostic pathways used to diagnose respiratory viruses in children, the most common etiology of diagnoses in the pediatric population. These tests potentially represent a source of unnecessary testing. We performed a retrospective observational study analyzing pediatric inpatients receiving respiratory virus testing at Yale-New Haven Children\u27s Hospital between March 2010 to March 2012. Billing data (age, gender, season), laboratory data (sample adequacy, results), and clinical documents were gathered. We used MetaMap, a program distributed by the National Library of Medicine, to identify phrases denoting symptoms and diseases in the admission notes of patients. Identified concepts were added as additional variables to be modeled. Weka, another freely available software that allows for easy incorporation of machine learning algorithms, was used to derive models based on the C4.5 decision tree algorithm that aim to predict whether or not patients should be tested. Orders for pediatric patients accounted for 26.3% of all respiratory virus test orders placed during this time. Negative test results accounted for 69.5% of all tests ordered during the study period. The lengths of stay for all viral diagnoses were not statistically different. Models based on age, gender and season alone, were predictive for influenza (AUC 0.743, SE = 0.126), parainfluenza (AUC 0.686, SE = 0.078), RSV (AUC 0.658, SE = 0.048), and hMPV (AUC 0.713, SE = 0.143). Using MetaMap terms alone, only the model for RSV showed discriminatory ability (AUC 0.661, SE = 0.048). When basic variables were used in conjunction with MetaMap concepts, only the model for RSV showed improved performance (AUC 0.722, SE = 0.051) in comparison to both the basic and MetaMap models. Respiratory virus tests for general admission pediatric inpatients are ordered year-round and are mostly negative. Using models based on decision tree learning, our results showed that test volume could be reduced by about 20-50% for certain tests, as measured by model specificity. Furthermore, clinical concepts obtained via text mining in conjunction with basic variables improved prediction of RSV test results. The tradeoff between the false negative rates required to achieve any substantive specificity may be mitigated by our finding that hospital stays were nearly identical, regardless of the diagnostic outcome. These results support the use of EMR data for the auditing of and improvement of laboratory utilization. In addition, the improvement of predictive modeling for RSV with a simple implementation of text mining support the idea that clinical notes can be used for secondary use

Influenza interaction with cocirculating pathogens, and its impact on surveillance, pathogenesis and epidemic profile: a key role for mathematical modeling

ABSTRACT Evidence is mounting that influenza virus, a major contributor to the global disease burden, interacts with other pathogens infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. That necessity is particularly true for mathematical modeling studies, which have become critical in public health decision-making, despite their usually focusing on lone influenza virus acquisition and infection, thereby making broad oversimplifications regarding pathogen ecology. Herein, we review evidence of influenza virus interaction with bacteria and viruses, and the modeling studies that incorporated some of these. Despite the many studies examining possible associations between influenza and Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitides , respiratory syncytial virus, human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. A notable exception is the recent modeling of the pneumococcus-influenza interaction, which highlighted potential influenza-related increased pneumococcal transmission and pathogenicity. That example demonstrates the power of dynamic modeling as an approach to test biological hypotheses concerning interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and misinterpretations. Using simple transmission models, we illustrate how existing interactions might impact public health surveillance systems and demonstrate that the development of multipathogen models is essential to assess the true public health burden of influenza, and help improve planning and evaluation of control measures. Finally, we identify the public health needs, surveillance, modeling and biological challenges, and propose avenues of research for the coming years. Author Summary Influenza is a major pathogen responsible for important morbidity and mortality burdens worldwide. Mathematical models of influenza virus acquisition have been critical to understanding its epidemiology and planning public health strategies of infection control. It is increasingly clear that microbes do not act in isolation but potentially interact within the host. Hence, studying influenza alone may lead to masking effects or misunderstanding information on its transmission and severity. Herein, we review the literature on bacterial and viral species that interact with the influenza virus, interaction mechanisms, and mathematical modeling studies integrating interactions. We report evidence that, beyond the classic secondary bacterial infections, many pathogenic bacteria and viruses probably interact with influenza. Public health relevance of pathogen interactions is detailed, showing how potential misreading or a narrow outlook might lead to mistaken public health decisionmaking. We describe the role of mechanistic transmission models in investigating this complex system and obtaining insight into interactions between influenza and other pathogens. Finally, we highlight benefits and challenges in modeling, and speculate on new opportunities made possible by taking a broader view: including basic science, clinical relevance and public health

Transmission routes of respiratory viruses among humans

Respiratory tract infections can be caused by a wide variety of viruses. Airborne transmission via droplets and aerosols enables some of these viruses to spread efficiently among humans, causing outbreaks that are difficult to control. Many outbreaks have been investigated retrospectively to study the possible routes of inter-human virus transmission. The results of these studies are often inconclusive and at the same time data from controlled experiments is sparse. Therefore, fundamental knowledge on transmission routes that could be used to improve intervention strategies is still missing. We here present an overview of the available data from experimental and observational studies on the transmission routes of respiratory viruses between humans, identify knowledge gaps, and discuss how the available knowledge is currently implemented in isolation guidelines in health care settings

Hurdles in Vaccine Development against Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) infection is a major cause of severe respiratory disease in infants and young children worldwide and also forms a serious threat for the elderly. Vaccination could significantly relieve the burden of the RSV disease. However, unfortunately there is no licensed vaccine available so far. This is partly due to disastrous outcome of a clinical trial of formalin-inactivated RSV (FI-RSV) in children in 1960s; leading to enhanced respiratory disease upon natural infection. These findings contributed significantly to the delay of RSV vaccine development. Other key obstacles in development of RSV vaccine such as a peak of severe disease at 2–3 months of age, challenging biochemical behavior of key vaccine antigens and dependence on animal models that may not truly reflect human disease processes. These challenges could be overcome through maternal immunization, structure-based engineering of vaccine antigens, the design of a novel platform for safe infant immunization, and the development of improved animal models. Currently, several vaccine candidates are in pre-clinical and clinical trials targeting the diverse age groups; young children or older adults from the infection or can reduce incidence, mortality and morbidity among the RSV infected individuals