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1. Networked Epidemiology for COVID-19

   Chen, J; Levin, S; Eubank, S; Mortveit, H; Venkatramanan, S; Vullikanti, A; Marathe, M (June 2020, Siam news)

   Computational epidemiology aims to develop computer models and decision support systems that understand, predict, and control a disease’s spatiotemporal diffusion throughout a population. Researchers can use these models to forecast an epidemic’s future course, allocate scarce resources and assess depletion of current resources, infer disease parameters, and evaluate various interventions. Individual behavior and public policy are critical in understanding and controlling infectious diseases, and computational techniques provide a potentially powerful study tool. The COVID-19 pandemic has had significant social, health, economic, and political ramifications worldwide, and its impact will undoubtedly continue to grow in the coming months.Here we outline an approach to support the COVID-19 response with examples that are rooted in network science and data-driven modeling. 

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2. Historically Underrepresented Graduate Students’ Experiences During the COVID-19 Pandemic

   https://doi.org/10.1111/fare.12574  

   Walsh, B. A.; Woodliff, T. A.; Lucero, J.; Harvey, S.; Burnham, M. M.; Bowser, T. L.; Aguirre, M.; Zeh, D. W. (October 2021, Family relations)

   Objective: The purpose of this study was to understand the experiences of historically underrepresented graduate students, more than half of whom were enrolled in science, technology, engineering, and mathematics (STEM) disciplines, during the COVID-19 pandemic. This focus group study represents an initial stage in developing an intervention for historically underrepresented graduate students and their families. Background: Underrepresentation of graduate students of color in STEM has been attributed to a myriad of factors, including a lack of support systems. Familial support is an endorsed reason for persisting in graduate school. It is unclear what historically underrepresented graduate students’ experiences are during uncertain times, such as a pandemic. Method: Focus groups were conducted online using a videoconferencing platform during the COVID-19 pandemic. Five focus groups included: historically underrepresented doctoral students (n =5), historically underrepresented master’s students (n = 6), academic faculty (n = 7), administrator, administrative faculty, and academic faculty (n = 6), and families of historically underrepresented doctoral students (n = 6). Data were analyzed using thematic analysis. Results: Historically underrepresented graduate students experienced difficulties in accessing resources, adjustments to home and family life, amplification of existing nonfinancial issues, and expressed both fears of and hopes for the future. Conclusion: The COVID-19 pandemic exacerbated existing inequalities in access to resources as well as nonfinancial family support. Implications: This study may help normalize historically underrepresented graduate students’ experiences during the COVID-19 pandemic. The findings include ideas for informing families about graduate school that can enlighten family support efforts for historically underrepresented graduate students and their families. 

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3. Models for Assessing Strategies for Improving Hospital Capacity for Handling Patients during a Pandemic

   https://doi.org/10.1017/dmp.2022.12  

   Shahverdi, Bahar; Miller-Hooks, Elise; Tariverdi, Mersedeh; Ghayoomi, Hadi; Prentiss, David; Kirsch, Thomas D. (January 2022, Disaster Medicine and Public Health Preparedness)

   Abstract Objective: The aim of this study was to investigate the performance of key hospital units associated with emergency care of both routine emergency and pandemic (COVID-19) patients under capacity enhancing strategies. Methods: This investigation was conducted using whole-hospital, resource-constrained, patient-based, stochastic, discrete-event, simulation models of a generic 200-bed urban U.S. tertiary hospital serving routine emergency and COVID-19 patients. Systematically designed numerical experiments were conducted to provide generalizable insights into how hospital functionality may be affected by the care of COVID-19 pandemic patients along specially designated care paths, under changing pandemic situations, from getting ready to turning all of its resources to pandemic care. Results: Several insights are presented. For example, each day of reduction in average ICU length of stay increases intensive care unit patient throughput by up to 24% for high COVID-19 daily patient arrival levels. The potential of 5 specific interventions and 2 critical shifts in care strategies to significantly increase hospital capacity is also described. Conclusions: These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, collaborate with other health care facilities, or request external support, thereby increasing the likelihood that arriving patients will find an open staffed bed when 1 is needed. 

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4. Student interest, concerns, and information-seeking behaviors related to COVID-19

   https://doi.org/10.1186/s43031-022-00053-2  

   Elsner, Jamie N.; Sadler, Troy D.; Zangori, Laura; Friedrichsen, Patricia J.; Ke, Li (April 2022, Disciplinary and Interdisciplinary Science Education Research)

   Abstract COVID-19 creates an opportunity for science classrooms to relate content about viruses to students’ personal experiences with the pandemic. Previous researchers have shown that students are interested in crisis situations like disease outbreaks; however, they primarily acquire information about these events through internet sources which are often biased. We argue that it is important to understand student interest, concerns, and information-seeking behaviors related to COVID-19 to support science classroom learning and engagement about the virus and other potential outbreaks. We surveyed 224 high school students and analyzed their responses to six open-ended questions. We found that students expressed the most interest in topics related to the origin of COVID-19 and vaccines. Their greatest concerns included contracting the virus or someone they know contracting the virus and vaccine distribution. Of our sample, only 6.7% reported using their teachers as their source of COVID-19 information. Science classrooms have the potential to pique students’ situational interest by discussing COVID-19 topics that are important to students, which can increase their academic performance, content knowledge, attention, and engagement in learning about viruses. Moreover, classroom instruction about COVID-19 by teachers has shown to alleviate students’ stress and anxiety. We provide key areas of student interest about COVID-19 to help educators address students’ questions and improve curricular resources on viral pandemics. 

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5. Predictive Models of Mortality for Hospitalized Patients With COVID-19: Retrospective Cohort Study

   https://doi.org/10.2196/21788  

   Wang, Taiyao; Paschalidis, Aris; Liu, Quanying; Liu, Yingxia; Yuan, Ye; Paschalidis, Ioannis Ch (January 2020, JMIR Medical Informatics)

   null (Ed.)

   Background The novel coronavirus SARS-CoV-2 and its associated disease, COVID-19, have caused worldwide disruption, leading countries to take drastic measures to address the progression of the disease. As SARS-CoV-2 continues to spread, hospitals are struggling to allocate resources to patients who are most at risk. In this context, it has become important to develop models that can accurately predict the severity of infection of hospitalized patients to help guide triage, planning, and resource allocation. Objective The aim of this study was to develop accurate models to predict the mortality of hospitalized patients with COVID-19 using basic demographics and easily obtainable laboratory data. Methods We performed a retrospective study of 375 hospitalized patients with COVID-19 in Wuhan, China. The patients were randomly split into derivation and validation cohorts. Regularized logistic regression and support vector machine classifiers were trained on the derivation cohort, and accuracy metrics (F1 scores) were computed on the validation cohort. Two types of models were developed: the first type used laboratory findings from the entire length of the patient’s hospital stay, and the second type used laboratory findings that were obtained no later than 12 hours after admission. The models were further validated on a multicenter external cohort of 542 patients. Results Of the 375 patients with COVID-19, 174 (46.4%) died of the infection. The study cohort was composed of 224/375 men (59.7%) and 151/375 women (40.3%), with a mean age of 58.83 years (SD 16.46). The models developed using data from throughout the patients’ length of stay demonstrated accuracies as high as 97%, whereas the models with admission laboratory variables possessed accuracies of up to 93%. The latter models predicted patient outcomes an average of 11.5 days in advance. Key variables such as lactate dehydrogenase, high-sensitivity C-reactive protein, and percentage of lymphocytes in the blood were indicated by the models. In line with previous studies, age was also found to be an important variable in predicting mortality. In particular, the mean age of patients who survived COVID-19 infection (50.23 years, SD 15.02) was significantly lower than the mean age of patients who died of the infection (68.75 years, SD 11.83; P<.001). Conclusions Machine learning models can be successfully employed to accurately predict outcomes of patients with COVID-19. Our models achieved high accuracies and could predict outcomes more than one week in advance; this promising result suggests that these models can be highly useful for resource allocation in hospitals. 

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