Optimal Staged Reopening Schedule Based on ICU Capacity: A Model-Informed Strategy

Reopening plans and strategies remain to be a top priority issue, post a pandemic wave, for economic recovery with a relatively safe community. We use a transmission dynamics model, parameterized through model fitting to cumulative incidence data during different social distancing escalation phases, to identify the optimal timing of reopening based on social-distancing de-escalation in a population. We use the Province of Ontario, Canada as a case study. The optimization is subject to the constraint that a future COVID-19 outbreak will not lead to the need of acute and intensive care unit beds beyond their local public health capacity. We minimize the cost, the total number of contacts lost until we reach a certain targeted date. We illustrate this optimization process by considering a particular de-escalation strategy that simply ‘reverses’ the escalation steps. We consider several scenarios depending on the number of de-escalation phases (characterized by an increase in the daily number of contacts), and different lengths of the period between a date when the cost is evaluated until a targeted data when the constraint is to be removed (for example, the anticipated date when a mass COVID-19 vaccination or an effective treatment becomes available) so that normal contacts prior to COVID-19 pandemic can be resumed. In the case of Ontario, we conclude that resuming 80% of pre-pandemic activity level should not happen 2 months before the vaccine/drug treatment becomes available. We also show that improving contact tracing and diagnosis capacity has a significant effect on the reopening date, whereas increasing ICU beds capacity has only a minor effect. © 2022, Springer Nature Switzerland AG.

Analysis of Fashion Value and Emotion in Digital Environment Based on Analysis of Famous Korean Fashion YouTube Review Data

The fashion world is trying various changes due to the development of technology, the non-face-to-face environment caused by Covid-19, and the lifestyle change of the digital generation. It is a world where luxury brands promote and sell their products in a metaverse environment. Fashion influencers also use YouTube to give advice on styling and short content, and lead lifestyle changes. As a result, the values and sensibility of digital-based fashion are also changing. Therefore, this thesis researched related contents through Fashion YouTube. Fashion YouTube, which has the top 5 domestic subscribers and videos as of May 2021, was selected. I crawled their youtube data reviews with python. As a result of the analysis of the collected data with text-mining, past fashion discovered fashion and trends, but modern fashion further emphasized the philosophy and values of life based on their own lifestyles. Rather than changing fashion based on trends, fashion is a means of expressing individual values, emphasizing emotional details and expressing fashion changes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Scenario tree and adaptive decision making on optimal type and timing for intervention and social-economic activity changes to manage the COVID-19 pandemic

We introduce a novel approach to inform the re-opening plan followed by a post-pandemic lockdown by integrating a stochastic optimization technique with a disease transmission model. We assess Ontarios re-opening plans as a case-study. Taking into account the uncertainties in contact rates during different re-opening phases, we find the optimal timing for the upcoming re-opening phase that maximizes the relaxation of social contacts under uncertainties, while not overwhelming the health system capacity before the arrival of effective therapeutics or vaccines.