ABSTRACT
Coronary heart disease (CHD) remains the leading cause of premature death worldwide. Better risk stratification tools and personalized care of patients are needed for reducing the morbidity and mortality of CHD and the associated economic burden. However, contemporary e-learning solutions lack personalization and shared decision making and as a result, overwhelm patients with large amounts of information. CoroPrevention is a multiyear, EU-funded Horizon 2020 research project aiming to shape and implement a personalized secondary prevention strategy for patients with established CHD. As a part of the project, new digital tools will also be validated. In this paper, we discuss the process of creating audio-visual content for the CoroPrevention mobile application during the challenging COVID-19 pandemic. © 2022 IEEE.
ABSTRACT
In 2019, the Ontario Ministry of Education announced a mandatory mathematics examination for all newly licensed teachers in the province. The following winter, after a brief pilot and a few months during COVID of testing, a court case declared the mathematics examination unconstitutional, and it has been paused since January 2021. This paper discusses the research-based evidence that has led to support from a mathematics standpoint for such an examination, as well as the court case that has changed the ability of the province to provide licensing guidelines. We provide our research conclusions on why such an examination might be needed and cautions for considering the reasons that led to the court overturning the proficiency test. © 2023, Ontario Institute for Studies in Education (OISE).
ABSTRACT
Policy-makers require data-driven tools to assess the spread of COVID-19 and inform the public of their risk of infection on an ongoing basis. We propose a rigorous hybrid model-and-data-driven approach to risk scoring based on a time-varying SIR epidemic model that ultimately yields a simplified color-coded risk level for each community. The risk score tt that we propose is proportional to the probability of someone currently healthy getting infected in the next 24 hours based on their locality. We show how this risk score can be estimated using another useful metric of infection spread, Rt, the time-varying average reproduction number which indicates the average number of individuals an infected person would infect in turn. The proposed approach also allows for quantification of uncertainty in the estimates of Rt and tt in the form of confidence intervals. Code and data from our effort have been open-sourced and are being applied to assess and communicate the risk of infection in the City and County of Los Angeles. © 2020 ACM.