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.

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.

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.