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Students spend much of their time in classrooms. In the face of the COVID-19 pandemic, understanding and reducing infectious disease transmission risk in indoor spaces such as classrooms is top priority. This research employed a spreadsheet-based disease transmission risk model to estimate the transmission risk of the SARS-CoV-2 virus that causes COVID-19 under varying classroom scenarios and in specific classes and classrooms at the University of Wyoming (UW). The conditions assessed include the effect of masking on disease transmission risk, how infection risk varies with community prevalence and ventilation rates, how class sizes affect COVID-19 risk, and how COVID-19 disease varies with population immunity. As COVID-19 spread, UW facilities dramatically increased building ventilation rates to help reduce the spread of the air-borne virus that causes COVID-19 by increasing air turnover (while also filtering the air) which my results suggest was a highly effective strategy. A secondary objective of this study was to survey carbon dioxide (CO2) concentrations in indoor public areas in Laramie, Wyoming. The highest carbon dioxide concentration found was in a bar at night (1290 ppm) and the lowest was in a grocery store during the day (779 ppm). Bars had the highest overall concentrations of CO2 at all times of the day. By contrast, measures taken in the UW classrooms sampled as part of the primary study showed a maximum CO2 concentration of 802 ppm even when fully occupied and at the end of a class period, illustrating the effectiveness of UW’s ventilation systems.
The COVID-19 was a major threat to public health around the world from the beginning of COVID-19 pandemic. The U.S. was one of the countries with the most COVID-19 cases. Despite the mitigation efforts to control the disease at both local and national levels, the number of COVID-19 cases in the U.S. remained high throughout the pandemic. This study focused on Cook County in Illinois. During the COVID-19 pandemic, Cook County was one of the counties with the highest COVID-19 cases in the U.S. This study described the spatial and temporal dynamics of COVID-19 risk in two-week periods from August 2020 to December 2020 in Cook County. This study also assessed the impact of neighborhood socioeconomic and demographic on COVID-19 incidence. The Bayesian spatio-temporal model was used to produce COVID-19 risk maps and to evaluate covariates' effects. The results show the spatial heterogeneity in COVID-19 risk from time to time, with the risk peaked in the first weeks of November. Over different time points, some parts of the county exhibited constant COVID-19 high-risk levels. Among these high-risk areas, many of them were majority-Hispanic neighborhoods in Chicago (i.e., Chicago west side) and Cook County suburbs (i.e., Franklin Park and Elgin). The model summary shows that the percentage of Hispanic population, health insurance coverage, and public transit commuters were associated with COVID-19 incidence. The posterior median and the 95% credible interval for the relative risk of a 1% increase in the percentage of Hispanic population was 1.009 (1.007, 1.011), indicating that a 1% increase in the percentage of Hispanic population corresponds to an increase in COVID-19 risk of 0.9%. The corresponding relative risk for a 1% increase in health insurance was 1.015 (1.006, 1.025), while for a 1% increase in the percentage of public transit commuters, the relative risk was 0.991 (0.987, 0.995). This study's findings highlight the importance of integrating the geographical information system into disease routine surveillance programs and transforming routinely collected health data into critical information. This information can be used to identify risk factors that could be addressed by allocating resources or implementing health policies.
There is limited research available on COVID-19 outbreaks in K-12 schools, and many schools will be looking for guidance on safely keeping schools open for the 2021-2022 school year. The primary purpose of this study was to examine the relationship between learning modality and time to assess whether there was a relationship between community level transmission and time to COVID-19 outbreak in schools. This study used data from the Washington State Department of Health (DOH) School Outbreak Assessment of Policies and Practices (SOAPP) survey. This is a retrospective survey designed to learn about school outbreaks and to assess the effectiveness of certain COVID-19 mitigation measures in reducing transmission in the K-12 school setting. A time-to-event analysis using the Cox Proportional Model was conducted in order to compare the time to the first outbreak between different learning modalities. Time to outbreak was compared between outbreaks in 100% remote, remote with exceptions, hybrid, and traditional in-person settings. Secondary analyses included community transmission of COVID-19 in the Cox Proportional Model. : The time-to-event analysis for the first aim did not find an association between learning modality and time to first COVID-19 outbreak. This The Cox Proportional Hazards model for the second aim met all assumptions and flagged hybrid learning modality and low transmission rate as significantly associated with time to outbreak. The p-value for hybrid learning modality was 0.008 with a hazard ratio of 4.46 (95% CI: 1.86, 10.70), indicating a strong relationship between a hybrid learning modality and increased risk of an outbreak. The p-value for low transmission rate was 0.0015 with a hazard ratio of 3.73 (95% CI: 1.65, 8.40), indicating a strong relationship between a low transmission rate and shorter time to outbreak. Further research is needed to draw conclusions about the relationships between learning modality, community level transmission of COVID-19 and outbreaks in K-12 schools in Washington state.
In 1918-1919 influenza raged around the globe in the worst pandemic in recorded history. Focusing on those closest to the crisis--patients, families, communities, public health officials, nurses and doctors--this book explores the epidemic in the United States.
Urban slum dwellers—especially in emerging-economy countries—are often poor, live in squalor, and suffer unnecessarily from disease, disability, premature death, and reduced life expectancy. Yet living in a city can and should be healthy. Slum Health exposes how and why slums can be unhealthy; reveals that not all slums are equal in terms of the hazards and health issues faced by residents; and suggests how slum dwellers, scientists, and social movements can come together to make slum life safer, more just, and healthier. Editors Jason Corburn and Lee Riley argue that valuing both new biologic and “street” science—professional and lay knowledge—is crucial for improving the well-being of the millions of urban poor living in slums.
Topics in Mathematical Modeling is an introductory textbook on mathematical modeling. The book teaches how simple mathematics can help formulate and solve real problems of current research interest in a wide range of fields, including biology, ecology, computer science, geophysics, engineering, and the social sciences. Yet the prerequisites are minimal: calculus and elementary differential equations. Among the many topics addressed are HIV; plant phyllotaxis; global warming; the World Wide Web; plant and animal vascular networks; social networks; chaos and fractals; marriage and divorce; and El Niño. Traditional modeling topics such as predator-prey interaction, harvesting, and wars of attrition are also included. Most chapters begin with the history of a problem, follow with a demonstration of how it can be modeled using various mathematical tools, and close with a discussion of its remaining unsolved aspects. Designed for a one-semester course, the book progresses from problems that can be solved with relatively simple mathematics to ones that require more sophisticated methods. The math techniques are taught as needed to solve the problem being addressed, and each chapter is designed to be largely independent to give teachers flexibility. The book, which can be used as an overview and introduction to applied mathematics, is particularly suitable for sophomore, junior, and senior students in math, science, and engineering.
Rigorous, detailed, and wide-ranging, University Finances is a unique and powerful resource.
Some people make photo albums, collect antiques, or visit historic battlefields. Others keep diaries, plan annual family gatherings, or stitch together patchwork quilts in a tradition learned from grandparents. Each of us has ways of communing with the past, and our reasons for doing so are as varied as our memories. In a sweeping survey, Roy Rosenzweig and David Thelen asked 1,500 Americans about their connection to the past and how it influences their daily lives and hopes for the future. The result is a surprisingly candid series of conversations and reflections on how the past infuses the present with meaning. Rosenzweig and Thelen found that people assemble their experiences into narratives that allow them to make sense of their personal histories, set priorities, project what might happen next, and try to shape the future. By using these narratives to mark change and create continuity, people chart the courses of their lives. A young woman from Ohio speaks of giving birth to her first child, which caused her to reflect upon her parents and the ways that their example would help her to become a good mother. An African American man from Georgia tells how he and his wife were drawn to each other by their shared experiences and lessons learned from growing up in the South in the 1950s. Others reveal how they personalize historical events, as in the case of a Massachusetts woman who traces much of her guarded attitude toward life to witnessing the assassination of John F. Kennedy on television when she was a child. While the past is omnipresent to Americans, "history" as it is usually defined in textbooks leaves many people cold. Rosenzweig and Thelen found that history as taught in school does not inspire a strong connection to the past. And they reveal how race and ethnicity affects how Americans perceive the past: while most white Americans tend to think of it as something personal, African Americans and American Indians are more likely to think in terms of broadly shared experiences--like slavery, the Civil Rights Movement, and the violation of Indian treaties." Rosenzweig and Thelen's conclusions about the ways people use their personal, family, and national stories have profound implications for anyone involved in researching or presenting history, as well as for all those who struggle to engage with the past in a meaningful way.
As liaison librarianship has evolved from a collections-centric to an engagement-centric model, liaisons have had to grapple with new and evolving competencies and skills that are focused on how to engage with diverse constituencies and stakeholders. But what does that mean practically? Liaison Engagement Success: A Practical Guide for Librarians will answer that question for academic liaison librarians, whether they are new to the profession or new to the liaison role. It offer specific proven strategies for engaging with user communities. Every community is different, and a liaison who takes up the tasks of engagement will need to be committed to building relationships, being flexible, and listening well, in order to understand the community’s needs and meet them. This book offers specific strategies for : Getting to know a user community Finding effective strategies for proactive outreach Collaborating with others for effective engagement Evaluating and assessing the engagement that is happening The book features practical tips and case studies for engagement with different disciplines in the humanities, social sciences, STEM, arts, professional disciplines, and with non-academic units.
This SpringerBrief discusses the characteristics of spatiotemporal movement data, including uncertainty and scale. It investigates three core aspects of Computational Movement Analysis: Conceptual modeling of movement and movement spaces, spatiotemporal analysis methods aiming at a better understanding of movement processes (with a focus on data mining for movement patterns), and using decentralized spatial computing methods in movement analysis. The author presents Computational Movement Analysis as an interdisciplinary umbrella for analyzing movement processes with methods from a range of fields including GIScience, spatiotemporal databases and data mining. Key challenges in Computational Movement Analysis include bridging the semantic gap, privacy issues when movement data involves people, incorporating big and open data, and opportunities for decentralized movement analysis arising from the internet of things. The interdisciplinary concepts of Computational Movement Analysis make this an important book for professionals and students in computer science, geographic information science and its application areas, especially movement ecology and transportation research.