Search

As part of a multiyear project that monitored illness-related school closures, we conducted systematic daily online searches during July 27, 2020-June 30, 2022, to identify public announcements of COVID-19-related school closures (COVID-SCs) in the United States lasting >1 day. We explored the temporospatial patterns of COVID-SCs and analyzed associations between COVID-SCs and national COVID-19 surveillance data. COVID-SCs reflected national surveillance data: correlation was highest between COVID-SCs and both new PCR test positivity (correlation coefficient [r] = 0.73, 95% CI 0.56-0.84) and new cases (r = 0.72, 95% CI 0.54-0.83) during 2020-21 and with hospitalization rates among all ages (r = 0.81, 95% CI 0.67-0.89) during 2021-22. The numbers of reactive COVID-SCs during 2020-21 and 2021-22 greatly exceeded previously observed numbers of illness-related reactive school closures in the United States, notably being nearly 5-fold greater than reactive closures observed during the 2009 influenza (H1N1) pandemic.

The COVID-19 patient data for composite outcome prediction often comes with class imbalance issues, i.e., only a small group of patients develop severe composite events after hospital admission, while the rest do not. An ideal COVID-19 composite outcome prediction model should possess strong imbalanced learning capability. The model also should have fewer tuning hyperparameters to ensure good usability and exhibit potential for fast incremental learning. Towards this goal, this study proposes a novel imbalanced learning approach called Imbalanced maximizing-Area Under the Curve (AUC) Proximal Support Vector Machine (ImAUC-PSVM) by the means of classical PSVM to predict the composite outcomes of hospitalized COVID-19 patients within 30 days of hospitalization. ImAUC-PSVM offers the following merits: (1) it incorporates straightforward AUC maximization into the objective function, resulting in fewer parameters to tune. This makes it suitable for handling imbalanced COVID-19 data with a simplified training process. (2) Theoretical derivations reveal that ImAUC-PSVM has the same analytical solution form as PSVM, thus inheriting the advantages of PSVM for handling incremental COVID-19 cases through fast incremental updating. We built and internally and externally validated our proposed classifier using real COVID-19 patient data obtained from three separate sites of Mayo Clinic in the United States. Additionally, we validated it on public datasets using various performance metrics. Experimental results demonstrate that ImAUC-PSVM outperforms other methods in most cases, showcasing its potential to assist clinicians in triaging COVID-19 patients at an early stage in hospital settings, as well as in other prediction applications.

The COVID-19 pandemic has exposed socioeconomic vulnerabilities around the world. After fighting the coronavirus for more than 1 and a half years now, the countries are recovering from the epidemic with the help of cutting-edge medical research. The policymakers are implementing stimulus packages for post-pandemic economic recovery. However, sustainable "green recovery" plans are yet to get adequate attention. Sustainable investment in green industries can create green jobs, promote a low-carbon economy, and foster long-lasting economic growth in the post-pandemic world. COVID-19 affected countries with emerging economies call for even more focus on such investments. In Bangladesh, the bicycle industry - a growing low-carbon industry - has been showing promising potential for growth since the beginning of the pandemic. Both the local and global markets of Bangladeshi bicycles have seen substantial growth during the epidemic. In this paper, we analyze the potential of the Bangladeshi bicycle industry as an effective green recovery driver. We conduct semi-structured interviews with relevant experts and professionals, analyze their opinions, and perform a "strengths, weaknesses, opportunities, and threats (SWOT)" analysis. The analysis reveals valuable insights regarding post-pandemic sustainable economic and environmental recovery which will be beneficial to the policymakers of Bangladesh and similar developing countries., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores., Methods: This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis., Results: Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores., Conclusion: The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice., (© 2023. The Author(s).)

Objectives: The study aimed to determine the seroprevalence, the fraction of asymptomatic infections, and risk factors of SARS-CoV-2 infections among the Forcibly Displaced Myanmar Nationals (FDMNs)., Design: It was a population-based two-stage cross-sectional study at the level of households., Setting: The study was conducted in December 2020 among household members of the FDMN population living in the 34 camps of Ukhia and Teknaf Upazila of Cox's Bazar district in Bangladesh., Participants: Among 860 697 FDMNs residing in 187 517 households, 3446 were recruited for the study. One individual aged 1 year or older was randomly selected from each targeted household., Primary and Secondary Outcome Measures: Blood samples from respondents were tested for total antibodies for SARS-CoV-2 using Wantai ELISA kits, and later positive samples were validated by Kantaro kits., Results: More than half (55.3%) of the respondents were females, aged 23 median (IQR 14-35) years and more than half (58.4%) had no formal education. Overall, 2090 of 3446 study participants tested positive for SARS-CoV-2 antibody. The weighted and test adjusted seroprevalence (95% CI) was 48.3% (45.3% to 51.4%), which did not differ by the sexes. Children (aged 1-17 years) had a significantly lower seroprevalence 38.6% (95% CI 33.8% to 43.4%) compared with adults (58.1%, 95% CI 55.2% to 61.1%). Almost half (45.7%, 95% CI 41.9% to 49.5%) of seropositive individuals reported no relevant symptoms since March 2020. Antibody seroprevalence was higher in those with any comorbidity (57.8%, 95% CI 50.4% to 64.5%) than those without (47.2%, 95% CI 43.9% to 50.4%). Multivariate logistic regression analysis of all subjects identified increasing age and education as risk factors for seropositivity. In children (≤17 years), only age was significantly associated with the infection., Conclusions: In December 2020, about half of the FDMNs had antibodies against SARS-CoV-2, including those who reported no history of symptoms. Periodic serosurveys are necessary to recommend appropriate public health measures to limit transmission., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Pre-emptive school closures are frontline community mitigation measures recommended by the US Centers for Disease Control and Prevention (CDC) for implementation during severe pandemics. This study describes the spatiotemporal patterns of publicly announced school closures implemented in response to the coronavirus disease 2019 (COVID-19) pandemic and assesses how public K-12 districts adjusted their methods of education delivery and provision of subsidized meals. During February 18-June 30, 2020, we used daily systematic media searches to identify publicly announced COVID-19-related school closures lasting ≥1 day in the United States (US). We also collected statewide school closure policies from state government websites. Data on distance learning and subsidized meal programs were collected from a stratified sample of 600 school districts. The first COVID-19-associated school closure occurred on February 27, 2020 in Washington state. By March 30, 2020, all but one US public school districts were closed, representing the first-ever nearly synchronous nationwide closure of public K-12 schools in the US. Approximately 100,000 public schools were closed for ≥8 weeks because of COVID-19, affecting >50 million K-12 students. Of 600 districts sampled, the vast majority offered distance learning (91.0%) and continued provision of subsidized meal programs (78.8%) during the closures. Despite the sudden and prolonged nature of COVID-19-associated school closures, schools demonstrated flexibility by implementing distance learning and alternate methods to continue subsidized meal programs., Competing Interests: Ferdous Jahan is employed by Kāpili Services. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Older adults living in memory care facilities are vulnerable to more than just COVID-19; they are especially harmed from social distancing guidelines, as social isolation and loneliness have important medical consequences in this population. COVID-19 has changed the way we perceive the built environment, and almost all public spaces are now adopting new design strategies to create safe indoor and outdoor environments. Eight interdisciplinary, evidence-based spatial design interventions and action plans are explored in this article with the aim of redesigning future memory care facilities to combat social isolation and loneliness in older adults during this unprecedented time and beyond.

The present study aimed to systematically analyze the impact of COVID-19-related social distancing requirements on older adults living in long-term care facilities (LTCFs) and to synthesize the literature into thematic action plans to minimize the adverse effects of social isolation. The search included articles published between December 2019 and August 2020 across four databases. The inclusion criteria were used to screen for studies that reported on social isolation and loneliness due to the COVID-19 pandemic in older adults living in LTCFs. This rapid review identified 29 relevant studies and synthesized them into four thematic action plans: technological advancement, remote communication, therapeutic care/stress management, and preventive measures. These thematic action plans and cost-effective strategies can be immediately adopted and used as a resource for all LTCF administrators, healthcare design professionals, and researchers in battling current COVID-19-related issues, and improving social interaction in older adults living in care facilities.