arXiv subjects

Jim McMenamin

Publications and source records attributed to Jim McMenamin.

2 recordsLinked to original sources

arXiv2023

A standardised protocol for assessment of relative SARS-CoV-2 variant severity, with application to severity risk for COVID-19 cases infected with Omicron BA.1 compared to Delta variants in six European countries

Several SARS-CoV-2 variants that evolved during the COVID-19 pandemic have appeared to differ in severity, based on analyses of single-country datasets. With decreased SARS-CoV-2 testing and sequencing, international collaborative studies will become increasingly important for timely assessment of the severity of newly emerged variants. The Joint WHO Regional Office for Europe and ECDC Infection Severity Working Group was formed to produce and pilot a standardised study protocol to estimate relative variant case-severity in settings with individual-level SARS-CoV-2 testing and COVID-19 outcome data during periods when two variants were co-circulating. To assess feasibility, the study protocol and its associated statistical analysis code was applied by local investigators in Denmark, England, Luxembourg, Norway, Portugal and Scotland to assess the case-severity of Omicron BA.1 relative to Delta cases. After pooling estimates using meta-analysis methods (random effects estimates), the risk of hospital admission (adjusted hazard ratio [aHR]=0.41, 95% CI 0.31-0.54), ICU admission (aHR=0.12, 95% CI 0.05-0.27), and death (aHR=0.31, 95% CI 0.28-0.35) was lower for Omicron BA.1 compared to Delta cases. The aHRs varied by age group and vaccination status. In conclusion, this study has demonstrated the feasibility of conducting variant severity analyses in a multinational collaborative framework. The results add further evidence for the reduced severity of the Omicron BA.1 variant.

q-bio.PE↗

arXiv2019

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. Although community ecology approaches have been applied to determine pathogen interactions at the within-host scale, methodologies enabling robust inference of the epidemiological impact of pathogen interactions are lacking. Here we developed a novel statistical framework to identify statistical covariances from the infection time-series of multiple pathogens simultaneously. Our framework extends Bayesian multivariate disease mapping models to analyse multivariate time series data by accounting for within- and between-year dependencies in infection risk and incorporating a between-pathogen covariance matrix which we estimate. Importantly, our approach accounts for possible confounding drivers of temporal patterns in pathogen infection frequencies, enabling robust inference of pathogen-pathogen interactions. We illustrate the validity of our statistical framework using simulated data and 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.

stat.ME↗