SEASONALITY OF METEOROLOGICAL FACTORS INFLUENCING THE COVID-19 ERA IN COASTAL AND INLAND REGIONS OF BANGLADESH

We aim to explore the seasonal influences of meteorological factors on COVID-19 era over two distinct locations in Bangladesh using a generalized linear model (GLM) and wavelet analysis. GLM model findings show that summer humidity drives COVID-19 transmission to coastal and inland locations. During the summer in the coastal area, a 1 °C earth's skin temperature increase causes a 41.9% increase in COVID (95% CL 86.32%-2.54%) transmission compared to inland. Relative humidity was recorded as the highest at 73.97% (95% CL, 99.3%, and 48.63%) for the coastal region, while wind speed and precipitation reduced confirmed cases by -38.62% and -22.15%, respectively. Wavelet analysis showed that coastal meteorological parameters were more coherent with COVID-19 than inland ones. The outcomes of this study are consistent with subtropical climate regions. Seasonality and climatic similarity should address to estimate COVID-19 trends. High societal concern and strong public health measures may decrease meteorological effect on COVID-19. [ FROM AUTHOR] Copyright of Geocarto International is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Impacts of Three Educational Technologies on Algebraic Understanding in the Context of COVID-19.

The current study investigated the effectiveness of three distinct educational technologies-two game-based applications (From Here to There and DragonBox 12+) and two modes of online problem sets in ASSISTments (an Immediate Feedback condition and an Active Control condition with no immediate feedback) on Grade 7 students' algebraic knowledge. More than 3,600 Grade 7 students across nine in-person and one virtual schools within the same district were randomly assigned to one of the four conditions. Students received nine 30-minute intervention sessions from September 2020 to March 2021. Hierarchical linear modeling analyses of the final analytic sample (N = 1,850) showed significantly higher posttest scores for students who used From Here to There and DragonBox 12+ compared to the Active Control condition. No significant difference was found for the Immediate Feedback condition. The findings have implications for understanding how game-based applications can affect algebraic understanding, even within pandemic pressures on learning.

Circular Economy and Sustainable Development Goals 3 and 17: The Case of the PlastiCity Ecosystem

Plastic pollution is one of the most severe environmental and human health threats. Based on a linear model, our current economic system uses plastics as a primary resource to make products such as plastic bags and bottles. However, these products are not recycled into secondary resources. Instead, they are thrown away when they become unusable. In contrast, the circular economy considers plastic waste as an opportunity to create social, economic and environmental value. This model uses plastic waste as a raw material to produce new items. This research demonstrates that the circular economy contributes to Sustainable Development Goals 3 and 17 using the results of action and observatory research within the PlastiCity project. As part of PlastiCity, partners developed new products made from recycled plastic such as recycled face shields. This chapter describes our efforts in developing a business case for recycled face shields and deploying the PlastiCity ecosystem to improve collaboration and partnerships. This study suggests that the development of an ecosystem can facilitate collaboration between stakeholders in the plastic value chain and hence contribute to implementing circular business models. This research also demonstrates how the circular economy can respond rapidly to health-related societal challenges, such as the unavailability of personal protective equipment during the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Projecting the Potential Impact of COVID-19 School Closures on Academic Achievement

As the COVID-19 pandemic upended the 2019–2020 school year, education systems scrambled to meet the needs of students and families with little available data on how school closures may impact learning. In this study, we produced a series of projections of COVID-19-related learning loss based on (a) estimates from absenteeism literature and (b) analyses of summer learning patterns of 5 million students. Under our projections, returning students are expected to start fall 2020 with approximately 63 to 68% of the learning gains in reading and 37 to 50% of the learning gains in mathematics relative to a typical school year. However, we project that losing ground during the school closures was not universal, with the top third of students potentially making gains in reading.

Epidemic Outbreak Prediction with Ensemble of Deep Learning Models

The ability of today's technology has proved it's significance and dire need in the world yet again, with COVID-19 being a global pandemic. Various techniques are being incorporated and researches being conducted everyday in order to mitigate this pandemic. Forecasting of COVID-19 cases is one such task in machine learning which is being researched intensively to develop reliable forecasting models.In the proposed work, we have forecasted the number of COVID-19 confirmed,recovered and death cases globally using time series data with machine learning and deep learning ensemble models. The purpose of this study is to prove that ensemble of several week learners that we have developed can result in a better performing model. Deep learning models always tend to perform better than machine learning and traditional linear models due to their non-linearity. Our study concludes that deep learning ensemble model achieves better performance than the machine learning ensemble (Random forest) and the individual base learners used in ensemble model itself in COVID-19 forecasting. © 2022 IEEE.

Prediction of COVID-19 Confirmed Cases Combining The LSTM Model and Evolutionary Strategy

In the global response to the COVID-19 epidemic, a reasonable prediction of the number of infections is a significant reference to reveal the trend of the outbreak and help governments take appropriate action. In this paper, we propose a new ES-LSTM model to predict the growth rate of the number of new infections per day and use a feature processor to address interventions in time series to quantify the impact of interventions to slow the spread of the outbreak. The evolutionary strategy is used to handle the problem that different interventions have different impacts on outbreak prevention and control, as well as optimize model weight to improve the accuracy of prediction results. Experimental results demonstrate that compared to the Linear model, CNN model, and the LSTM model, the MAE of the algorithm is enhanced by 72.9%, 27.6%, and 26.3%, and the RMSE is improved by 74.15%, 31.4%, and 29.5% respectively. © 2022 IEEE.

A dimension reduction in neural network using copula matrix

In prediction analysis, there may exist some nonlinear relations between the exploratory variables, which are not captured by traditional correlation-based linear models such as multiple regression, principal component regression, and so on. In this work, we employ a copula matrix to extract principal components of a set of variables which are pair-wisely associated with a copula. By estimating the pairwise copula and its corresponding parameter(s), we suggest an optimization method to extract principal components from a matrix which contains some pairwise measures of association. We use these components as inputs of an artificial neural network to make a more accurate prediction. We test our proposed method using a simulation study and use it to carry out a more accurate prediction in an AIDS as well as a COVID-19 dataset. To increase the reliability of results, we employ a cross-validation technique. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Dynamic Linear Model for Urban Essential Traffic Congestion and Emissions

Congestion is a phenomenon that impacts most cities in the world. Due to car emissions, it is a significant source of pollution. Even though mobility restrictions can reduce congestion and emissions, essential activities still need cars. With lockdown measures during the global pandemic of Covid-19, measuring essential traffic data has been made possible. This paper concerns analysis and modelling of such essential traffic. It appears that congestion dynamics of essential traffic exhibits dynamics than can be represented with a linear model. This paper introduces such a model and provide a method to jointly estimate the parameters and the model input. The model is validated with data collected in Johannesburg, South Africa. Copyright © 2021 The Authors.

Prediction of COVID-19 Cases in India Using Parametric Curve

Due to novel coronavirus (COVID-19), the world is facing a pandemic situation. Human lifestyle changed drastically during this pandemic period, and everyone is badly affected and do not know when the situation is going to be normal. Though the virus is under control, still there is a uncertainty and unpredictable situation exist not only in India but also all over the world. So it is very important to predict the COVID cases as early as possible so that the best precautionary measure can be taken. In this study, we have designed a parametric estimation curve using linear, exponential, and logistic model for forecasting new cases on 30 days ahead. From experimentation, it is found that the logistic model performs better than the linear and exponential model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Level of Depression of College Students with Binary Logistic Regression Model Approximation in Covid-19 times

The objective of this research is to detect the level of depression that university students have because of Covid-19 using the binary logistic regression model and comparing with the linear model, it is of non-experimental design, cross-sectional descriptive type, with quantitative approach, The 9-item Patient Health Questionnaire (PHQ-9) was used with a population of 2185 respondents carried out during the month of May, the population is made up of university students from the Piura region chosen randomly, voluntarily and anonymously, resulting that the PHQ-9 measurement instrument is very good with a Cronbach's Alpha = 0. 885 and McDonald's W = 0.886, with 69.9% depression in university students, concentrated in 4 levels of depression, mild 39.7%, moderate 17.8%, severe 7% and very severe 5.3%. It was concluded that the PHQ-9 depression measurement instrument is adequate to measure depression in university students. In the discussion, it was possible to model the equation with the binomial logistic regression model, which results in better approximations than the linear model;this model is adequate to measure the level of depression in university students in the Piura region. © 2021 IEEE.

Teachers' Time Use and Affect Before and After COVID-19 School Closures

Several large-scale survey efforts have attempted to understand teachers' experiences in the early months of the pandemic. Our study complements this literature by providing direct evidence of teachers' work prior to and after the onset of COVID-19. We leverage unique longitudinal time use and affect data on 131 teachers from one district across the 2019-2020 school year. Specifically, we provide a full accounting of teachers' instructional activities, their reports of their positive affect and negative affect while engaged in these activities, and the extent to which teachers' work experiences changed post-COVID. Our results suggest a large reduction in teachers' daily instructional minutes, which were replaced with increased planning, paperwork, and interactions with colleagues and parents. Teachers' overall positive and negative affect did not change post-COVID. But teachers' affective responses to specific work activities did. Post-COVID, we saw increases in teachers' positive affect when with students.

The Impact of COVID-19 on Students' Marks: A Bayesian Hierarchical Modeling Approach.

Due to COVID-19, universities across Canada were forced to undergo a transition from classroom-based face-to-face learning and invigilated assessments to online-based learning and non-invigilated assessments. This study attempts to empirically measure the impact of COVID-19 on students' marks from eleven science, technology, engineering, and mathematics (STEM) courses using a Bayesian linear mixed effects model fitted to longitudinal data. The Bayesian linear mixed effects model is designed for this application which allows student-specific error variances to vary. The novel Bayesian missing value imputation method is flexible which seamlessly generates missing values given complete data. We observed an increase in overall average marks for the courses requiring lower-level cognitive skills according to Bloom's Taxonomy and a decrease in marks for the courses requiring higher-level cognitive skills, where larger changes in marks were observed for the underachieving students. About half of the disengaged students who did not participate in any course assessments after the transition to online delivery were in special support.

Daily Stress Processes in a Pandemic: The Effects of Worry, Age, and Affect.

BACKGROUND AND OBJECTIVES: In March 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19) a pandemic. Given that such a global event might affect day-to-day stress processes, the current study examined individuals' daily stress reactivity and its moderators early in the COVID-19 pandemic. RESEARCH DESIGN AND METHODS: Two-level, multilevel models examined the daily relationship between perceived stress and negative affect, or stress reactivity, as well as the moderating effects of daily pandemic worry, age, and daily positive affect on this process. Participants included 349 individuals (age range = 26-89) from the Notre Dame Study of Health & Well-being who completed a 28-day, daily diary study at the beginning of the COVID-19 pandemic. RESULTS: Older individuals were less stress-reactive than younger individuals. Within individuals, however, stress reactivity was buffered by daily positive affect and exacerbated by daily pandemic worry. Finally, although daily positive affect buffered daily stress reactivity, this effect was weaker on days individuals were more worried about the COVID-19 pandemic. DISCUSSION AND IMPLICATIONS: The mobilization of positive emotion may be a promising avenue for buffering stress reactivity during the COVID-19 pandemic, although this may be limited on days individuals are particularly concerned about the pandemic.

Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson generalized linear modeling approach.

Novel coronavirus (SARS-CoV-2) causing COVID-19 disease has arisen to be a pandemic. Since there is a close association between other viral infection cases by epidemics and environmental factors, this study intends to unveil meteorological effects on the outbreak of COVID-19 across eight divisions of Bangladesh from March to April 2020. A compound Poisson generalized linear modeling (CPGLM), along with a Monte-Carlo method and random forest (RF) model, was employed to explore how meteorological factors affecting the COVID-19 transmission in Bangladesh. Results showed that subtropical climate (mean temperature about 26.6 °C, mean relative humidity (MRH) 64%, and rainfall approximately 3 mm) enhanced COVD-19 onset. The CPGLM model revealed that every 1 mm increase in rainfall elevated by 30.99% (95% CI 77.18%, - 15.20%) COVID-19 cases, while an increase of 1 °C of diurnal temperature (TDN) declined the confirmed cases by - 14.2% (95% CI 9.73%, - 38.13%) on the lag 1 and lag 2, respectively. In addition, NRH and MRH had the highest increase (17.98% (95% CI 22.5%, 13.42%) and 19.92% (95% CI: 25.71%, 14.13%)) of COVID-19 cased in lag 4. The results of the RF model indicated that TDN and AH (absolute humidity) influence the COVID-19 cases most. In the Dhaka division, MRH is the most vital meteorological factor that affects COVID-19 deaths. This study indicates the humidity and rainfall are crucial factors affecting the COVID-19 case, which is contrary to many previous studies in other countries. These outcomes can have policy formulation for the suppression of the COVID-19 outbreak in Bangladesh.