Policymaking for complex challenges such as pandemics necessitates the consideration of intricate implications across multiple domains and scales. Computational models can support policymaking, but a single model is often insufficient for such multidomain and scale challenges. Multi-models comprising several interacting computational models at different scales or relying on different modeling paradigms offer a potential solution. Such multi-models can be assembled from existing computational models (i.e., integrated modeling) or be designed conceptually as a whole before their computational implementation (i.e., integral modeling). Integral modeling is particularly valuable for novel policy problems, such as those faced in the early stages of a pandemic, where relevant models may be unavailable or lack standard documentation. Designing such multi-models through an integral approach is, however, a complex task requiring the collaboration of modelers and experts from various domains. In this collaborative effort, modelers must precisely define the domain knowledge needed from experts and establish a systematic procedure for translating such knowledge into a multi-model. Yet, these requirements and systematic procedures are currently lacking for multi-models that are both multiscale and multi-paradigm. We address this challenge by introducing a procedure for developing multi-models with an integral approach based on clearly defined domain knowledge requirements derived from literature. We illustrate this procedure using the case of school closure policies in the Netherlands during the COVID-19 pandemic, revealing their potential implications in the short and long term and across the healthcare and educational domains. The requirements and procedure provided in this article advance the application of integral multi-modeling for policy support in multiscale and multidomain contexts.

The COVID-19 pandemic has sparked numerous discussions on social media platforms, with users sharing their views on topics such as mask-wearing and vaccination. To facilitate the evaluation of neural models for stance detection and premise classification, we organized the Social Media Mining for Health (SMM4H) 2022 Shared Task 2. This competition utilized manually annotated posts on three COVID-19-related topics: school closures, stay-at-home orders, and wearing masks. In this paper, we extend the previous work and present newly collected data on vaccination from Twitter to assess the performance of models on a different topic. To enhance the accuracy and effectiveness of our evaluation, we employed various strategies to aggregate tweet texts with claims, including models with feature-level (early) fusion and dual-view architectures from SMM4H 2022 leaderboard. Our primary objective was to create a valuable dataset and perform an extensive experimental evaluation to support future research in argument mining in the health domain.

Global problems, such as pandemics and climate change, require rapid international coordination and diffusion of policy. These phenomena are rare however, with one notable example being the international policy response to the COVID-19 pandemic in early 2020. Here we build an agent-based model of this rapid policy diffusion, where countries constitute the agents and with the principal mechanism for diffusion being peer mimicry. Since it is challenging to predict accurately the policy diffusion curve, we utilize data assimilation, that is an ``on-line'' feed of data to constrain the model against observations. The specific data assimilation algorithm we apply is a particle filter because of its convenient implementation, its ability to handle categorical variables and because the model is not overly computationally expensive, hence a more efficient algorithm is not required. We find that the model alone is able to predict the policy diffusion relatively well with an ensemble of at least 100 simulation runs. The particle filter however improves the fit to the data, reliably so from 500 runs upwards, and increasing filtering frequency results in improved prediction.

They reviewed 11 databases that were searched from inception to Sep-tember 2020, and machine learning was applied for screening articles.

The COVID-19 pandemic has transformed our lives in more ways than one. It has not just alerted us to the vulnerabilities of our health systems but also how ill-equipped our education systems are to cope with disruptions of this scale. When the pandemic forced schools to shut down and learners had to completely switch to online learning systems, the transition was anything but smooth. As part of the coordinated global education response to the COVID-19 pandemic, UNESCO, UNICEF and the World Bank conducted a Survey on National Education Responses to COVID-19 school closures. According to this joint report, 108 countries reported missing an average of 47 days of in-person instruction due to school closures - the equivalent to approximately one quarter of a regular school year – a long gap in the life of a student.

Achievement tests for elementary and junior high school children across Japan showed no correlation between the percentages of correct answers and the lengths of coronavirus school closures, education ministry data showed Tuesday. Gaps in the average percentages of correct answers between the prefectures were also small. The tests for elementary school sixth-graders and junior high school third-graders were carried out in May after the cancellation last year due to blanket school closures triggered by the COVID-19 crisis. The tests measured achievement in Japanese language and arithmetic for elementary school students and in Japanese and mathematics for junior high school students. Some 1.97 million students at about 29,000 public and private schools participated, covering almost all public schools and about half of private schools in Japan.

Amazon has revealed it is expanding its range of free online STEM (science, technology, engineering and mathematics) activities to keep children educate and entertained over the Christmas holidays. The tech company's selection of activities includes a new game called Cyber Robotics Challenge. This three-hour long event tasks a youngster with using maths to ensure a friend's birthday present gets delivered by an Amazon fulfilment centre robot. Amazon has also expanded its popular educational platform Maths4All to include secondary school-level activities as well as those geared towards younger pupils. Maths4All offers hundreds of worksheets on Kindle and Fire Tablets and maths challenges via Alexa.

Parents file lawsuit against New York City; councilman Joe Borelli with insight. Sound science, like the coronavirus itself, is apolitical. Most everything else this year -- including decisions on whether to close schools -- is not. As the pandemic enters its deadliest phase to date, government leaders and school districts are having to make extraordinarily difficult decisions about whether to continue in-person learning amid record communitywide surges in cases, hospitalizations and deaths. New York City's decision to close schools indefinitely, and the decision in my home state of New Jersey to allow school districts to keep them open, offers a stark contrast in how the two states with the highest death rates for COVID-19 are managing this crisis.

As researchers around the world work to find answers to so many questions about the Coronavirus, two things have been happening that I have noticed. One, there has been an increase in the use of artificial intelligence in the medical field, in particular with tracking the onset of Coronavirus and using AI to explore trends and devise solutions to some of the challenges that we are facing as we deal with the COVID-19 pandemic. Second, AI has also become a more common topic of discussion in the world of education, with resources shared for how to learn more about AI and many online course providers seeing an increase in enrollment in their AI programs. Why do we need to pay more attention to AI now? There are statistics predicting that artificial intelligence in U.S. education will grow by 47.5% from 2017-2021.

What do you picture when you think of a robot? That's the first question asked by "The Robot Doctor" -- a new series created by Carnegie Mellon University educators, RobotWits, the Pennsylvania Rural Robotics Initiative and WQED. Airing on PBS stations across Pennsylvania, the eight-episode program is geared toward high school students who may lack access to a computer during school closures, and who live in underresourced areas with limited STEM opportunities. "We're going to explore how robots solve the problems that allow them to be useful in the world. We'll do this with nothing more than the math concepts you may already know: geometry, trigonometry, basic algebra and a few concepts from physics," Jonathan Butzke says in the first episode. Butzke, an alumnus of CMU's Robotics Institute, hosts the show and is lead robotics researcher for RobotWits.