A Bayesian approach to estimating COVID-19 incidence and infection fatality rates.

Naive estimates of incidence and infection fatality rates (IFR) of coronavirus disease 2019 suffer from a variety of biases, many of which relate to preferential testing. This has motivated epidemiologists from around the globe to conduct serosurveys that measure the immunity of individuals by testing for the presence of SARS-CoV-2 antibodies in the blood. These quantitative measures (titer values) are then used as a proxy for previous or current infection. However, statistical methods that use this data to its full potential have yet to be developed. Previous researchers have discretized these continuous values, discarding potentially useful information. In this article, we demonstrate how multivariate mixture models can be used in combination with post-stratification to estimate cumulative incidence and IFR in an approximate Bayesian framework without discretization. In doing so, we account for uncertainty from both the estimated number of infections and incomplete deaths data to provide estimates of IFR. This method is demonstrated using data from the Action to Beat Coronavirus erosurvey in Canada.

Introducing a New High-Resolution Handwritten Digits Data Set with Writer Characteristics.

The contributions in this article are two-fold. First, we introduce a new handwritten digit data set that we collected. It contains high-resolution images of handwritten digits together with various writer characteristics which are not available in the well-known MNIST database. The multiple writer characteristics gathered are a novelty of our data set and create new research opportunities. The data set is publicly available online. Second, we analyse this new data set. We begin with simple supervised tasks. We assess the predictability of the writer characteristics gathered, the effect of using some of those characteristics as predictors in classification task and the effect of higher resolution images on classification accuracy. We also explore semi-supervised applications; we can leverage the high quantity of handwritten digits data sets already existing online to improve the accuracy of various classifications task with noticeable success. Finally, we also demonstrate the generative perspective offered by this new data set; we are able to generate images that mimics the writing style of specific writers. The data set has unique and distinct features and our analysis establishes benchmarks and showcases some of the new opportunities made possible with this new data set. Supplementary Information: The online version contains supplementary material available at 10.1007/s42979-022-01494-2.

Capturing spatial dependence of COVID-19 case counts with cellphone mobility data.

Spatial dependence is usually introduced into spatial models using some measure of physical proximity. When analysing COVID-19 case counts, this makes sense as regions that are close together are more likely to have more people moving between them, spreading the disease. However, using the actual number of trips between each region may explain COVID-19 case counts better than physical proximity. In this paper, we investigate the efficacy of using telecommunications-derived mobility data to induce spatial dependence in spatial models applied to two Spanish communities' COVID-19 case counts. We do this by extending Besag York Mollié (BYM) models to include both a physical adjacency effect, alongside a mobility effect. The mobility effect is given a Gaussian Markov random field prior, with the number of trips between regions as edge weights. We leverage modern parametrizations of BYM models to conclude that the number of people moving between regions better explains variation in COVID-19 case counts than physical proximity data. We suggest that this data should be used in conjunction with physical proximity data when developing spatial models for COVID-19 case counts.

Retrospective study of non-natural manners of death in Ontario: Effects of the COVID-19 pandemic and related public health measures.

BACKGROUND: The effects of the COVID-19 pandemic on non-natural manners of death in Ontario is not known. Understanding the indirect consequences of the pandemic and related public health measures (i.e. lockdown) fills a vital need to inform best practice in public health and guide policy decisions. METHODS: The Office of the Chief Coroner and the Ontario Forensic Pathology Service (OCC-OFPS) investigate sudden and unexpected deaths in the province of Ontario. The number of homicides, suicides, and accidental deaths (non-natural deaths=77,655) were extracted from the centralized Coroner's Information System database (total deaths=197,966), across four provincially defined stages of lockdown related to the COVID-19 pandemic (March 17 to December 31, 2020), and crude rates (per 100,000 people) were compared to the previous eleven years. FINDINGS: There was no major change to the rate of homicides during 2020 compared to 2009-2019 (RR 1⋅1, 95% CI 0⋅95-1⋅2; p=0⋅19; estimated annual effect=21 more deaths in 2020). The rate of suicides also did not show an overall major change in 2020 (RR 1⋅02, 95% CI 0⋅96-1⋅1; p=0⋅50; estimated annual effect=27 more deaths in 2020). However, during the first stage of lockdown (Stage 0), there was a decrease in the rate of suicides compared to all combinations of recent years from 2013 onwards (RRs 0⋅82-0⋅86, combined 95% CI 0⋅69-0⋅99; max p=0⋅039; estimated effect of 30 less deaths in Stage 0). There was an excess of over 1,500 accidental drug-related deaths that occurred during 2020 (RR 2⋅5, 95% CI 2⋅4-2⋅7; p<0⋅001). This finding held up to 'interrupted time series' robustness testing, indicating that 2020 had substantially more drug-related deaths, even when accounting for the linear increasing trend over time. Although motor vehicle collision associated fatalities appeared to decrease slightly in 2020 (RR 0⋅89, 95% CI 0⋅81-0⋅96; p=0⋅0039; estimated annual effect of 78 less deaths), we could not conclude any lockdown-associated effect, particularly when compared to 2019 (RR 0⋅26, 95% CI 0⋅75-1⋅1; p=0⋅26). INTERPRETATION: In Ontario, the short-term effects of the COVID-19 pandemic did not greatly increase homicide or suicide rates, nor decrease motor vehicle collision fatality rates; however, the longer-term impact of the pandemic remains to be elucidated and ongoing vigilance is warranted in the event that other trends emerge. Accidental drug-related fatalities substantially increased during all stages of the lockdown, marking an urgent need for consideration in policy. These results highlight the vital role of death investigation systems in providing high quality and timely data to inform public health recommendations.

An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group.

In this manuscript we analyze a data set containing information on children with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received and survival status were collected together with other covariates such as demographics and clinical measurements. Our main task is to explore the potential of machine learning (ML) algorithms in a survival analysis context in order to improve over the Cox Proportional Hazard (CoxPH) model. We discuss the weaknesses of the CoxPH model we would like to improve upon and then we introduce multiple algorithms, from well-established ones to state-of-the-art models, that solve these issues. We then compare every model according to the concordance index and the brier score. Finally, we produce a series of recommendations, based on our experience, for practitioners that would like to benefit from the recent advances in artificial intelligence.

A probable prehistoric case of meningococcal disease from San Francisco Bay: Next generation sequencing of Neisseria meningitidis from dental calculus and osteological evidence.

Next Generation Sequencing (NGS) of ancient dental calculus samples from a prehistoric site in San Francisco Bay, CA-SCL-919, reveals a wide range of potentially pathogenic bacteria. One older adult woman, in particular, had high levels of Neisseria meningitidis and low levels of Haemophilus influenzae, species that were not observed in the calculus from three other individuals. Combined with the presence of incipient endocranial lesions and pronounced meningeal grooves, we interpret this as an ancient case of meningococcal disease. This disease afflicts millions around the globe today, but little is known about its (pre)history. With additional sampling, we suggest NGS of calculus offers an exciting new window into the evolutionary history of these bacterial species and their interactions with humans.

Positional targets for lingual consonants defined using electromagnetic articulography.

The study examined the positional targets for lingual consonants defined using a point-parameterized approach with Wave (NDI, Waterloo, ON, Canada). The overall goal was to determine which consonants had unique tongue positions with respect to other consonants. Nineteen talkers repeated vowel-consonant-vowel (VCV) syllables that included consonants /t, d, s, z, , k, g/ in symmetrical vowel contexts /i, u, a/, embedded in a carrier phrase. Target regions for each consonant, characterized in terms of x,y,z tongue positions at the point of maximum tongue elevation, were extracted. Distances and overlaps were computed between all consonant pairs and compared to the distances and overlaps of their contextual targets. Cognates and postalveolar homorganics were found to share the location of their target regions. On average, alveolar stops showed distinctively different target regions than alveolar fricatives, which in turn showed different target region locations than the postalveolar consonants. Across talker variability in target locations was partially explained by differences in habitual speaking rate and hard palate characteristics.