The effects of genetic and modifiable risk factors on brain regions vulnerable to ageing and disease.

We have previously identified a network of higher-order brain regions particularly vulnerable to the ageing process, schizophrenia and Alzheimer's disease. However, it remains unknown what the genetic influences on this fragile brain network are, and whether it can be altered by the most common modifiable risk factors for dementia. Here, in ~40,000 UK Biobank participants, we first show significant genome-wide associations between this brain network and seven genetic clusters implicated in cardiovascular deaths, schizophrenia, Alzheimer's and Parkinson's disease, and with the two antigens of the XG blood group located in the pseudoautosomal region of the sex chromosomes. We further reveal that the most deleterious modifiable risk factors for this vulnerable brain network are diabetes, nitrogen dioxide - a proxy for traffic-related air pollution - and alcohol intake frequency. The extent of these associations was uncovered by examining these modifiable risk factors in a single model to assess the unique contribution of each on the vulnerable brain network, above and beyond the dominating effects of age and sex. These results provide a comprehensive picture of the role played by genetic and modifiable risk factors on these fragile parts of the brain.

Estimation of SARS-CoV-2 antibody prevalence through serological uncertainty and daily incidence.

Serology tests for SARS-CoV-2 provide a paradigm for estimating the number of individuals who have had an infection in the past (including cases that are not detected by routine testing, which has varied over the course of the pandemic and between jurisdictions). Such estimation is challenging in cases for which we only have limited serological data and do not take into account the uncertainty of the serology test. In this work, we provide a joint Bayesian model to improve the estimation of the sero-prevalence (the proportion of the population with SARS-CoV-2 antibodies) through integrating multiple sources of data, priors on the sensitivity and specificity of the serological test, and an effective epidemiological dynamics model. We apply our model to the Greater Vancouver area, British Columbia, Canada, with data acquired during the pandemic from the end of January to May 2020. Our estimated sero-prevalence is consistent with previous literature but with a tighter credible interval.

Le dépistage sérologique du SRAS­CoV­2 permet d'estimer le nombre de personnes qui ont déjà été infectées (y compris les cas qui ne sont pas détectés au moyen de tests de dépistage réguliers, qui ont varié au cours de la pandémie et d'une province ou d'un territoire à l'autre). Une telle estimation est difficile lorsqu'il existe peu de données sérologiques et que l'incertitude du test sérologique n'est pas prise en compte. Nous proposons dans ce travail un modèle bayésien conjoint visant à améliorer l'estimation de la séroprévalence (la proportion de la population avec des anticorps SRAS­CoV­2) en intégrant de multiples sources de données, des lois a priori sur la sensibilité et la spécificité du test sérologique, et un modèle efficace des dynamiques épidémiologiques. Nous appliquons ce modèle à des données recueillies dans la région métropolitaine de Vancouver (Colombie­Britannique, Canada) pendant la pandémie de fin janvier à mai 2020. Notre estimation de la séroprévalence est cohérente avec la littérature antérieure tout en ayant un intervalle de crédibilité plus précis.

Long time frames to detect the impact of changing COVID-19 measures, Canada, March to July 2020.

BackgroundMany countries have implemented population-wide interventions to control COVID-19, with varying extent and success. Many jurisdictions have moved to relax measures, while others have intensified efforts to reduce transmission.AimWe aimed to determine the time frame between a population-level change in COVID-19 measures and its impact on the number of cases.MethodsWe examined how long it takes for there to be a substantial difference between the number of cases that occur following a change in COVID-19 physical distancing measures and those that would have occurred at baseline. We then examined how long it takes to observe this difference, given delays and noise in reported cases. We used a susceptible-exposed-infectious-removed (SEIR)-type model and publicly available data from British Columbia, Canada, collected between March and July 2020.ResultsIt takes 10 days or more before we expect a substantial difference in the number of cases following a change in COVID-19 control measures, but 20-26 days to detect the impact of the change in reported data. The time frames are longer for smaller changes in control measures and are impacted by testing and reporting processes, with delays reaching ≥ 30 days.ConclusionThe time until a change in control measures has an observed impact is longer than the mean incubation period of COVID-19 and the commonly used 14-day time period. Policymakers and practitioners should consider this when assessing the impact of policy changes. Rapid, consistent and real-time COVID-19 surveillance is important to minimise these time frames.