Sensitivity of ecosystem parameters to simulated satellite ocean color data using a coupled physical-biological model of the North Atlantic

A means of assimilating simulated satellite ocean color data with a coupled physical-biological model of the North Atlantic Ocean is implemented, allowing the relative sensitivities of different biological parameters to those data to be investigated. The model consists of an eddy-permitting general circulation model derived from the WOCE Community Modeling Effort and a nitrogen-based, four-compartment NPZD marine ecosystem model. Many of the parameters in marine ecosystem models are poorly known and via assimilation, we hope to better constrain their values. The control parameters chosen for the variational assimilation are the model parameters involved in parameterizations of recycling as these are the most poorly known. Simulated observations are taken while following several floats seeded in varying dynamical biogeochemical provinces of the North Atlantic model domain over a six-month period. Twin experimental results show that, for the given functional forms of growth, mortality and grazing, the following parameters can be successfully recovered from simulated satellite ocean color data: nitrate and detrital recycling parameters in the trade wind domain, zooplankton parameters at higher latitudes (westerly wind and polar domains), and the phytoplankton mortality rate in all regions. By simultaneously assimilating ocean color data in different biological provinces, it becomes possible to successfully constrain all ecosystem parameters at once

Epidemiology of seasonal coronaviruses: establishing the context for the emergence of coronavirus disease 2019

Public health preparedness for coronavirus (CoV) disease 2019 (COVID-19) is challenging in the absence of setting-specific epidemiological data. Here we describe the epidemiology of seasonal CoVs (sCoVs) and other cocirculating viruses in the West of Scotland, United Kingdom. We analyzed routine diagnostic data for >70 000 episodes of respiratory illness tested molecularly for multiple respiratory viruses between 2005 and 2017. Statistical associations with patient age and sex differed between CoV-229E, CoV-OC43, and CoV-NL63. Furthermore, the timing and magnitude of sCoV outbreaks did not occur concurrently, and coinfections were not reported. With respect to other cocirculating respiratory viruses, we found evidence of positive, rather than negative, interactions with sCoVs. These findings highlight the importance of considering cocirculating viruses in the differential diagnosis of COVID-19. Further work is needed to establish the occurrence/degree of cross-protective immunity conferred across sCoVs and with COVID-19, as well as the role of viral coinfection in COVID-19 disease severity

Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness

Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness

Early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II) : protocol for an observational study using linked Scottish national data

Introduction Following the emergence of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019 and the ensuing COVID-19 pandemic, population-level surveillance and rapid assessment of the effectiveness of existing or new therapeutic or preventive interventions are required to ensure that interventions are targeted to those at highest risk of serious illness or death from COVID-19. We aim to repurpose and expand an existing pandemic reporting platform to determine the attack rate of SARS-CoV-2, the uptake and effectiveness of any new pandemic vaccine (once available) and any protective effect conferred by existing or new antimicrobial drugs and other therapies. Methods and analysis A prospective observational cohort will be used to monitor daily/weekly the progress of the COVID-19 epidemic and to evaluate the effectiveness of therapeutic interventions in approximately 5.4 million individuals registered in general practices across Scotland. A national linked dataset of patient-level primary care data, out-of-hours, hospitalisation, mortality and laboratory data will be assembled. The primary outcomes will measure association between: (A) laboratory confirmed SARS-CoV-2 infection, morbidity and mortality, and demographic, socioeconomic and clinical population characteristics; and (B) healthcare burden of COVID-19 and demographic, socioeconomic and clinical population characteristics. The secondary outcomes will estimate: (A) the uptake (for vaccines only); (B) effectiveness; and (C) safety of new or existing therapies, vaccines and antimicrobials against SARS-CoV-2 infection. The association between population characteristics and primary outcomes will be assessed via multivariate logistic regression models. The effectiveness of therapies, vaccines and antimicrobials will be assessed from time-dependent Cox models or Poisson regression models. Self-controlled study designs will be explored to estimate the risk of therapeutic and prophylactic-related adverse events. Ethics and dissemination We obtained approval from the National Research Ethics Service Committee, Southeast Scotland 02. The study findings will be presented at international conferences and published in peer-reviewed journals

Vaccine effectiveness of live attenuated and trivalent inactivated influenza vaccination in 2010/11 to 2015/16:the SIVE II record linkage study

Background: There is good evidence of vaccine effectiveness in healthy individuals but less robust evidence for vaccine effectiveness in the populations targeted for influenza vaccination. The live attenuated influenza vaccine (LAIV) has recently been recommended for children in the UK. The trivalent influenza vaccine (TIV) is recommended for all people aged≥65 years and for those aged<65 years who are at an increased risk of complications from influenza infection (e.g. people with asthma). Objective: To examine the vaccine effectiveness of LAIV and TIV. Design: Cohort study and test-negative designs to estimate vaccine effectiveness. A self-case series study to ascertain adverse events associated with vaccination. Setting: A national linkage of patient-level general practice (GP) data from 230 Scottish GPs to the Scottish Immunisation & Recall Service, Health Protection Scotland virology database, admissions to Scottish hospitals and the Scottish death register. Participants: A total of 1,250,000 people. Interventions: LAIV for 2- to 11-year-olds and TIV for older people (aged≥65 years) and those aged<65 years who are at risk of diseases, from 2010/11 to 2015/16. Main outcome measures: The main outcome measures include vaccine effectiveness against laboratory-confirmed influenza using real-time reverse-transcription polymerase chain reaction (RT-PCR), influenza-related morbidity and mortality, and adverse events associated with vaccination. Results: Two-fifths (40%) of preschool-aged children and three-fifths (60%) of primary school-aged children registered in study practices were vaccinated. Uptake varied among groups [e.g. most affluent vs. most deprived in 2- to 4-year-olds, odds ratio 1.76, 95% confidence interval (CI) 1.70 to 1.82]. LAIV-adjusted vaccine effectiveness among children (aged 2-11 years) for preventing RT-PCR laboratoryconfirmed influenza was 21% (95% CI -19% to 47%) in 2014/15 and 58% (95% CI 39% to 71%) in 2015/16. No significant adverse events were associated with LAIV. Among at-risk 18- to 64-year-olds, significant trivalent influenza vaccine effectiveness was found for four of the six seasons, with the highest vaccine effectiveness in 2010/11 (53%, 95% CI 21% to 72%). The seasons with non-significant vaccine effectiveness had low levels of circulating influenza virus (2011/12, 5%; 2013/14, 9%). Among those people aged≥65 years, TIV effectiveness was positive in all six seasons, but in only one of the six seasons (2013/14) was significance achieved (57%, 95% CI 20% to 76%). Conclusions: The study found that LAIV was safe and effective in decreasing RT-PCR-confirmed influenza in children. TIV was safe and significantly effective in most seasons for 18- to 64-year-olds, with positive vaccine effectiveness in most seasons for those people aged≥65 years (although this was significant in only one season). Future work: The UK Joint Committee on Vaccination and Immunisation has recommended the use of adjuvanted injectable vaccine for those people aged≥65 years from season 2018/19 onwards. A future study will be required to evaluate this vaccine. Trial registration: Current Controlled Trials ISRCTN88072400

Quantifying the Observability of CO2 Flux Uncertainty in Atmospheric CO2 Records Using Products from Nasa's Carbon Monitoring Flux Pilot Project

NASAs Carbon Monitoring System (CMS) Flux Pilot Project (FPP) was designed to better understand contemporary carbon fluxes by bringing together state-of-the art models with remote sensing datasets. Here we report on simulations using NASAs Goddard Earth Observing System Model, version 5 (GEOS-5) which was used to evaluate the consistency of two different sets of observationally constrained land and ocean fluxes with atmospheric CO2 records. Despite the strong data constraint, the average difference in annual terrestrial biosphere flux between the two land (NASA Ames CASA and CASA-GFED) models is 1.7 Pg C for 2009-2010. Ocean models (NOBM and ECCO2-Darwin) differ by 35 in their global estimates of carbon flux with particularly strong disagreement in high latitudes. Based upon combinations of terrestrial and ocean fluxes, GEOS-5 reasonably simulated the seasonal cycle observed at northern hemisphere surface sites and by the Greenhouse gases Observing SATellite (GOSAT) while the model struggled to simulate the seasonal cycle at southern hemisphere surface locations. Though GEOS-5 was able to reasonably reproduce the patterns of XCO2 observed by GOSAT, it struggled to reproduce these aspects of AIRS observations. Despite large differences between land and ocean flux estimates, resulting differences in atmospheric mixing ratio were small, typically less than 5 ppmv at the surface and 3 ppmv in the XCO2 column. A statistical analysis based on the variability of observations shows that flux differences of these magnitudes are difficult to distinguish from natural variability, regardless of measurement platform

Seasonal influenza vaccine effectiveness in people with asthma: a national test-negative design case-control study

Financial support. The work was funded by the Chief Scientist Office of the Scottish Government under the grant (AUKCAR/14/03) and the NIHR–Health Technology Assessment (HTA) Programme (13/34/14) for the Seasonal Influenza Vaccination Effectiveness II (SIVE II) study. As principal investigator, C. R. S. received a grant for the SIVE-II project from the NIHR HTA. This work was carried out with the support of the Asthma UK Centre for Applied Research (AUK-AC-2012-01), the Farr Institute (MR/M501633/2), Health Data Research UK (an initiative funded by UK Research and Innovation, Department of Health and Social Care England and the devolved administrations and leading medical research charities), the European Union’s Horizon 2020 research and innovation programme (under grant agreement No 634446) and European Centre for Disease Prevention and Control (Influenza-Monitoring Vaccine Effectiveness). Acknowledgments. The authors thank and acknowledge all colleagues at the Asthma UK Centre for Applied Research for their support in this study. Disclaimer. The funding bodies had no role in the design of the study, review process, analysis, interpretation, or reporting of data. The views and opinions expressed herein are those of the authors and do not necessarily reflect those of the Health Technology Assessment Programme, National Institute for Health Research (NIHR), National Health Service, or the Department of Health. Potential conflicts of interest. The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.Peer reviewedPublisher PDFPublisher PD

End-of-season influenza vaccine effectiveness in adults and children, United Kingdom, 2016/17

Introduction The United Kingdom is in the fourth season of introducing a universal childhood influenza vaccine programme. The 2016/17 season saw early influenza A(H3N2) virus circulation with care home outbreaks and increased excess mortality particularly in those 65 years or older. Virus characterisation data indicated emergence of genetic clusters within the A(H3N2) 3C.2a group which the 2016/17 vaccine strain belonged to. Methods: The test-negative case-control (TNCC) design was used to estimate vaccine effectiveness (VE) against laboratory confirmed influenza in primary care. Results: Adjusted end-of-season vaccine effectiveness (aVE) estimates were 39.8% (95% confidence interval (CI): 23.1 to 52.8) against all influenza and 40.6% (95% CI: 19.0 to 56.3) in 18-64-year-olds, but no significant aVE in ≥ 65-year-olds. aVE was 65.8% (95% CI: 30.3 to 83.2) for 2-17-year-olds receiving quadrivalent live attenuated influenza vaccine. Discussion: The findings continue to provide support for the ongoing roll-out of the paediatric vaccine programme, with a need for ongoing evaluation. The importance of effective interventions to protect the ≥ 65-year-olds remains

Evaluating the effectiveness, impact and safety of live attenuated and seasonal inactivated influenza vaccination: protocol for the Seasonal Influenza Vaccination Effectiveness II (SIVE II) study

Funding This project was funded by the National Institute for Health Research Health Technology Assessment Programme (project number 13/34/14). EV was supported by the Chief Scientist Office of the Scottish Government under grant (AUKCAR/14/03). This work is carried out with the support of the Asthma UK Centre for Applied Research (AUK-AC-2012–2001) and the Farr Institute.Peer reviewedPublisher PD