Investigation on the Control of COVID-19 in Wuhan: Number of Infections Outside Hospitals and the Reproduction Number.

COVID-19 is erupting globally, and Wuhan successfully controlled it within a month. Infections arose from infectious persons outside hospitals. After data revision, data-based and model-based analyses were implemented, and the conclusions are as follows. The incubation period of most infected people may be 6-7 days. The number of infectious persons outside hospitals in Wuhan on January 20, 2020 was about 10000 and reached more than 20000 on the day of Lockdown; it exceeded 72000 on February 4. Both data-based and model-based analyses gave out the evolution of the reproduction number, which was over 2.5 in early January, went down to 1.62 in late January and 1.20 in early February, with a sudden drop to less than 0.5 due to the strict Stay-at-home management after February 11. Strategies of Stay-at-home, Safe-protective measures, and Ark hospitals were the main contributions to control COVID-19 in Wuhan. In Wuhan, 2 inflection points of COVID-19, exactly correspond to February 5 and February 15, the 2 days when Ark hospitals were introduced, and the complete implementation of Stay-at-home. Based on the expression of the reproduction number, group immunity is also discussed. It shows that only when the group immunization rate is over 75% can COVID-19 be under control; group immunity would be full infection and the total deaths will be 220000 for a city as big as Wuhan. Sensitivity analysis suggests that 30% of people staying at home in combination with better behavior changes, such as social-distancing and frequent handwashing, can effectively contain COVID-19. However, only when this proportion is over 60% can the controlled effect and efficiency like Wuhan be obtained.

Novel Data-Based Model for Future Epidemiology

Epidemiological studies aim at predicting the outbreak of diseases as epidemics and pandemics. This goal is often realized using closed form expressions that significantly utilize systems of differential equations. These systems of equations are often derived by groups of researchers working in global collaborative efforts. However, groups of researchers can experience a high workload in scenarios when there is large number of non-active participating researchers in a collaborative study. In addition, data explosion and increased availability can also undermine the efforts of hardworking researchers. This is because of the high velocity and variety associated with big data availability. Hence, an approach that helps researchers to address these challenges is required. The discussion in this research proposes a suitable solution in this regard. The proposed solution introduces the notion of cognitive epidemiology and epidemiological crawlers in a novel computing framework. In the proposed computing framework, crawlers and existing closed form expressions used in epidemiological studies interact. Prior to this interaction, epidemiological closed form expressions are formatted in a manner to enable the incorporation of intelligence capability. The research presents execution paths and discusses the incorporation alongside the integration of the proposed mechanism in a manner suitable for integration with the internet. © 2022 IEEE.

Implementation of a Mathematics Formative Assessment Online Tool Before and During Remote Learning

ASSISTments is a free online learning tool for improving students’ mathematics achievement by providing immediate feedback and hints to students, detailed information on how students performed to teachers, and instructional suggestions for teachers to use. Researchers at the Friday Institute for Educational Innovation conducted an intrinsic, longitudinal multiple-case study of 7th-grade mathematics teachers’ implementation of ASSISTments and its impact on their instruction before and during the COVID-19 pandemic. The study examined teachers’ use of ASSISTments in three instructional contexts: in- person only, remote only, and both in-person and remote. Our findings indicate that teachers in all contexts changed their instructional practices for homework review and for determining whether their students had understood lessons. Teachers used the ASSISTments auto-generated reports to focus their homework reviews, based on their students’ performance, and to provide instructional interventions and/or re-teaching. They also used the instructional suggestions provided by the ASSISTments platform to plan lessons to re-teach concepts or to review prior instruction with their students. © 2022, Springer Nature Switzerland AG.

Predicting Organization Performance Changes: A Sequential Data-Based Framework.

The business environment is increasingly uncertain due to the rapid development of disruptive information technologies, the changing global economy, and the COVID-19 pandemic. This brings great uncertainties to investors to predict the performance changes and risks of companies. This research proposes a sequential data-based framework that aggregates data from multiple sources including both structured and unstructured data to predict the performance changes. It leverages data generated from the early risk warning system in China stock market to measure and predict organization performance changes based on the risk warning status changes of public companies. Different from the models in existing literature that focus on the prediction of risk warning of companies, our framework predicts a portfolio of organization performance changes, including business decline and recovery, thus helping investors to not only predict public company risks, but also discover investment opportunities. By incorporating sequential data, our framework achieves 92.3% macro-F1 value on real-world data from listed companies in China, outperforming other static models.

SUPPLY CHAIN RESILIENCE MANAGEMENT USING PROCESS MINING

Recent events such as the Coronavirus Pandemic or the disruption of the Suez Canal have shown how vulnerable supply chains can be and have led to an increased focus on resilience analysis by companies. We believe that all the data needed to understand the resilience status of a supply chain and identify opportunities for improvement already exist within companies. Therefore, we provide an approach to guide decision makers in this regard. We propose to first perform a rough resilience analysis using a limited set of transactional data. This analysis is based on key resilience areas to identify vulnerable elements of the supply chain that should be further investigated in terms of specific entities, transport relations, and materials. Based on these elements, process mining becomes a promising approach to understand the underlying actions, problems, and possible bottlenecks and to reveal improvement strategies. © ECMS Ibrahim A. Hameed, Agus Hasan

Exploring the impact of online team-initiated problem solving professional development on teacher's perceptions of efficacy and problem-solving skills

Data-based decision making (DBDM) is an integral component of a multi-tiered system of support (MTSS) framework. The data guide critical decisions, such as instructional and intervention strategies, resource allocation, policy development, intensity of supports, and potential disability identification. Using data in a systematic manner via a problem-solving process and aligning solutions and implementation practices are necessary skills for today's educators;however, training and professional development can be limited and/or resource intensive. One possible solution is to deliver problem-solving PD in an online or eLearning format. This study used a quasi-experimental, pre-test post-test design to explore the impact of the Team-Initiated Problem Solving (TIPS) online professional development on individual teachers' self-assessment of their problem-solving skills, as well as their beliefs in both their personal teaching efficacy and the collective teaching efficacy of their colleagues within the school. Participants were 30 educators from four elementary schools within three states in the United States. The study included a treatment group (n = 17), all of whom were active team members in Tier 2 problem solving teams within their school site. It also included a comparison group (n = 13) of participants from the same schools who were not part of the problem-solving team. Data were collected via pre- and post-tests of the Teacher Sense of Efficacy Scale-Short Form, the Collective Teacher Efficacy Scale, and the TIPS Team Member Self-Assessment. A qualitative response was also included in the post-test to examine the impact the COVID-19 pandemic may have had on participant responses. Analyses were conducted to explore differences within each group, as well as between groups over time. Overall, there were positive changes in perceptions over time on all measures;however, differences were found to be not significant. Further, the COVID-19 pandemic had a large influence over the participants' responses;therefore, it is hard to definitively determine that the treatment condition contributed to the shift in perceptions. Recommendations for future research include repeating the study with a larger sample, focusing on Tier 1 problem solving teams, exploring differences between rural and urban settings, and evaluating the influence of coaching supports on outcomes. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic.

This study utilizes modeling and simulation to analyze coronavirus (COVID-19) infection trends depending on government policies. Two modeling requirements are considered for infection simulation: (1) the implementation of social distancing policies and (2) the representation of population movements. To this end, we propose an extended infection model to combine analytical models with discrete event-based simulation models in a hybrid form. Simulation parameters for social distancing policies are identified and embedded in the analytical models. Administrative districts are modeled as a fundamental simulation agent, which facilitates representing the population movements between the cities. The proposed infection model utilizes real-world data regarding suspected, infected, recovered, and deceased people in South Korea. As an application, we simulate the COVID-19 epidemic in South Korea. We use real-world data for 160 days, containing meaningful days that begin the distancing policy and adjust the distancing policy to the next stage. We expect that the proposed work plays a principal role in analyzing how social distancing effectively affects virus prevention and provides a simulation environment for the biochemical field.

A Data-Based Approach Using a Multi-Group SIR Model with Fuzzy Subsets: Application to the COVID-19 Simulation in the Islands of Guadeloupe.

In this paper, we propose a multi-group SIR to simulate the spread of COVID-19 in an island context. The multi-group aspect enables us to modelize transmissions of the virus between non-vaccinated individuals within an age group as well as between different age groups. In addition, fuzzy subsets and aggregation operators are used to account for the increased risks associated with age and obesity within these different groups. From a conceptual point of view, the model emphasizes the notion of Hospitalization which is the major stake of this pandemic by replacing the compartment R (Removed) by compartment H (Hospitalization). The experimental results were carried out using medical and demographic data from the archipelago, Guadeloupe (French West Indies) in the Caribbean. These results show that without the respect of barrier gestures, a first wave would concern the elderly then a second the adults and the young people, which conforms to the real data.

The Ockham's razor applied to COVID-19 model fitting French data.

This paper presents a data-based simple model for fitting the available data of the Covid-19 pandemic evolution in France. The time series concerning the 13 regions of mainland France have been considered for fitting and validating the model. An extremely simple, two-dimensional model with only two parameters demonstrated to be able to reproduce the time series concerning the number of daily demises caused by Covid-19, the hospitalizations, intensive care and emergency accesses, the daily number of positive tests and other indicators, for the different French regions. These results might contribute to stimulate a debate on the suitability of much more complex models for reproducing and forecasting the pandemic evolution since, although relevant from a mechanistic point of view, they could lead to nonidentifiability issues.