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.

The Frequency of Routine Blood Sampling and Patient Outcomes Among Maintenance Hemodialysis Recipients.

RATIONALE & OBJECTIVE: Surveillance blood work is routinely performed in maintenance hemodialysis (HD) recipients. Although more frequent blood testing may confer better outcomes, there is little evidence to support any particular monitoring interval. STUDY DESIGN: Retrospective population-based cohort study. SETTING & PARTICIPANTS: All prevalent HD recipients in Ontario, Canada, as of April 1, 2011, and a cohort of incident patients commencing maintenance HD in Ontario, Canada, between April 1, 2011, and March 31, 2016. EXPOSURE: Frequency of surveillance blood work, monthly versus every 6 weeks. OUTCOMES: The primary outcome was all-cause mortality. Secondary outcomes were major adverse cardiovascular events, all-cause hospitalization, and episodes of hyperkalemia. ANALYTICAL APPROACH: Cox proportional hazards with adjustment for demographic and clinical characteristics was used to evaluate the association between blood testing frequency and all-cause mortality. Secondary outcomes were evaluated using the Andersen-Gill extension of the Cox model to allow for potential recurrent events. RESULTS: 7,454 prevalent patients received care at 17 HD programs with monthly blood sampling protocols (n=5,335 patients) and at 8 programs with blood sampling every 6 weeks (n=2,119 patients). More frequent monitoring was not associated with a lower risk for all-cause mortality compared to blood sampling every 6 weeks (adjusted HR, 1.16; 95% CI, 0.99-1.38). Monthly monitoring was not associated with a lower risk for any of the secondary outcomes. Results were consistent among incident HD recipients. LIMITATIONS: Unmeasured confounding; limited data for center practices unrelated to blood sampling frequency; no information on frequency of unscheduled blood work performed outside the prescribed sampling interval. CONCLUSIONS: Monthly routine blood testing in HD recipients was not associated with a lower risk for death, cardiovascular events, or hospitalizations as compared with testing every 6 weeks. Given the health resource implications, the frequency of routine blood sampling in HD recipients deserves careful reassessment.

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine.

OBJECTIVE: The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction to a knowledge representation and machine-learning tool for risk estimation in medical science known as Bayesian networks (BNs). STUDY DESIGN: In this article we review how BNs are compact and intuitive graphical representations of joint probability distributions (JPDs) that can be used to conduct causal reasoning and risk estimation analysis and offer several advantages over regression-based methods. We discuss how BNs represent a different approach to risk estimation in that they are graphical representations of JPDs that take the form of a network representing model random variables and the influences between them, respectively. METHODS: We explore some of the challenges associated with traditional risk prediction methods and then describe BNs, their construction, application, and advantages in risk prediction based on examples in cancer and heart disease. RESULTS: Risk modeling with BNs has advantages over regression-based approaches, and in this article we focus on three that are relevant to health outcomes research: (1) the generation of network structures in which relationships between variables can be easily communicated; (2) their ability to apply Bayes's theorem to conduct individual-level risk estimation; and (3) their easy transformation into decision models. CONCLUSIONS: Bayesian networks represent a powerful and flexible tool for the analysis of health economics and outcomes research data in the era of precision medicine.

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.

Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method.

Objectives. Coronary artery disease (CAD) is the leading cause of death and disease burden worldwide, causing 1 in 7 deaths in the United States alone. Risk prediction models that can learn the complex causal relationships that give rise to CAD from data, instead of merely predicting the risk of disease, have the potential to improve transparency and efficacy of personalized CAD diagnosis and therapy selection for physicians, patients, and other decision makers. Methods. We use Bayesian networks (BNs) to model the risk of CAD using the Z-Alizadehsani data set-a published real-world observational data set of 303 Iranian patients at risk for CAD. We also describe how BNs can be used for incorporation of background knowledge, individual risk prediction, handling missing observations, and adaptive decision making under uncertainty. Results. BNs performed on par with machine-learning classifiers at predicting CAD and showed better probability calibration. They achieved a mean 10-fold area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.04, which was comparable with the performance of logistic regression with L1 or L2 regularization (AUC: 0.92 ± 0.06), support vector machine (AUC: 0.92 ± 0.06), and artificial neural network (AUC: 0.91 ± 0.05). We describe the use of BNs to predict with missing data and to adaptively calculate prognostic values of individual variables under uncertainty. Conclusion. BNs are powerful and versatile tools for risk prediction and health outcomes research that can complement traditional statistical techniques and are particularly useful in domains in which information is uncertain or incomplete and in which interpretability is important, such as medicine.