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Abstract Background Coronaviruses pose a serious threat to global health as evidenced by Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and COVID-19. SARS Coronavirus (SARS-CoV), MERS Coronavirus (MERS-CoV), and the novel coronavirus, previously dubbed 2019-nCoV, and now officially named SARS-CoV-2, are the causative agents of the SARS, MERS, and COVID-19 disease outbreaks, respectively. Safe vaccines that rapidly induce potent and long-lasting virus-specific immune responses against these infectious agents are urgently needed. The coronavirus spike (S) protein, a characteristic structural component of the viral envelope, is considered a key target for vaccines for the prevention of coronavirus infection. Methods We first generated codon optimized MERS-S1 subunit vaccines fused with a foldon trimerization domain to mimic the native viral structure. In variant constructs, we engineered immune stimulants (RS09 or flagellin, as TLR4 or TLR5 agonists, respectively) into this trimeric design. We comprehensively tested the pre-clinical immunogenicity of MERS-CoV vaccines in mice when delivered subcutaneously by traditional needle injection, or intracutaneously by dissolving microneedle arrays (MNAs) by evaluating virus specific IgG antibodies in the serum of vaccinated mice by ELISA and using virus neutralization assays. Driven by the urgent need for COVID-19 vaccines, we utilized this strategy to rapidly develop MNA SARS-CoV-2 subunit vaccines and tested their pre-clinical immunogenicity in vivo by exploiting our substantial experience with MNA MERS-CoV vaccines. Findings Here we describe the development of MNA delivered MERS-CoV vaccines and their pre-clinical immunogenicity. Specifically, MNA delivered MERS-S1 subunit vaccines elicited strong and long-lasting antigen-specific antibody responses. Building on our ongoing efforts to develop MERS-CoV vaccines, promising immunogenicity of MNA-delivered MERS-CoV vaccines, and our experience with MNA fabrication and delivery, including clinical trials, we rapidly designed and produced clinically-translatable MNA SARS-CoV-2 subunit vaccines within 4 weeks of the identification of the SARS-CoV-2 S1 sequence. Most importantly, these MNA delivered SARS-CoV-2 S1 subunit vaccines elicited potent antigen-specific antibody responses that were evident beginning 2 weeks after immunization. Interpretation MNA delivery of coronaviruses-S1 subunit vaccines is a promising immunization strategy against coronavirus infection. Progressive scientific and technological efforts enable quicker responses to emerging pandemics. Our ongoing efforts to develop MNA-MERS-S1 subunit vaccines enabled us to rapidly design and produce MNA SARS-CoV-2 subunit vaccines capable of inducing potent virus-specific antibody responses. Collectively, our results support the clinical development of MNA delivered recombinant protein subunit vaccines against SARS, MERS, COVID-19, and other emerging infectious diseases.

The ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19. One sentence summary: The spread of COVID-19 in China was driven by human mobility early on and mitigated substantially by drastic control measures implemented since the end of January.

AbstractBackgroundCoronavirus disease 2019 (COVID-19), caused by the novel coronavirus SARS-CoV-2, has led to significant global mortality and morbidity. Until now, no treatment has proven to be effective in COVID-19. To explore whether the use of remdesivir, initially an experimental broad-spectrum antiviral, is effective in the treatment of hospitalized patients with COVID-19, we conducted a systematic review and meta-analysis of randomized, placebo-controlled trials investigating its use.MethodsA rapid search of the MEDLINE and EMBASE medical databases was conducted for randomized controlled trials. A systematic approach was used to screen, abstract, and critically appraise the studies. Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method was applied to rate the certainty and quality of the evidence reported per study.ResultsTwo RCTs studies were identified (n=1,299). A fixed-effects meta-analysis revealed reductions in mortality (RR=0.69, 0.49 to 0.99), time to clinical improvement (3.95 less days, from 3.86 days less to 4.05 less days), serious adverse events (RR=0.77, 0.63 to 0.94) and all adverse events (RR=0.87, 0.79 to 0.96).ConclusionIn this rapid systematic review, we present pooled evidence from the 2 included RCT studies that reveal that remdesivir has a modest yet significant reduction in mortality and significantly improves the time to recovery, as well as significantly reduced risk in adverse events and serious adverse events. It is more than likely that as an antiviral, remdesivir is not sufficient on its own and may be suitable in combination with other antivirals or treatments such as convalescent plasma. Research is ongoing to clarify and contextual these promising findings.

Psychiatric patients are at high risk for contracting COVID-19, and inpatient psychiatric units face substantial risks of institutional outbreaks. Here, the authors describe an algorithm for testing and triage in a large psychiatric facility designed to prevent local COVID-19 transmission. The algorithm is based on expert opinion and clinical experience between March and April of 2020, during which the institution cared for 47 COVID-19 positive psychiatric inpatients. The implementation of the algorithm is designed to mitigate COVID-19 transmission, preserve the safest and least restrictive treatment environment for psychiatric inpatients, and provide a model adaptable to other institutional settings.

Previous meta-analyses were conducted during the initial phases of the COVID-19 pandemic, which utilized a smaller pool of data. The current meta-analysis aims to provide additional (and updated) evidence related to the psychological impact among healthcare workers. The search strategy was developed by a medical librarian and bibliographical databases, including Medline, Embase, CINAHL, PsycINFO, and Scopus were searched for studies examining the impact of the COVID-19 pandemic on the psychological health of healthcare workers. Articles were screened by three reviewers. Heterogeneity among studies was assessed by I2 statistic. The random-effects model was utilized to obtain the pooled prevalence. A subgroup analysis by region, gender, quality of study, assessment methods, healthcare profession, and exposure was performed. Publication bias was assessed by Funnel plot and Egger linear regression test. Sixty-five studies met the inclusion criteria and the total sample constituted 79,437 participants. The pooled prevalence of anxiety, depression, stress, post-traumatic stress syndrome, insomnia, psychological distress, and burnout was 34.4%, 31.8%, 40.3%, 11.4%, 27.8%, 46.1%, and 37.4% respectively. The subgroup analysis indicated higher anxiety and depression prevalence among females, nurses, and frontline responders than males, doctors, and second-line healthcare workers. This study highlights the need for designing a targeted intervention to improve resilience and foster post-traumatic growth among frontline responders.

The emergence of the COVID-19 disease as a global pandemic caused major challenges and strained busy operating room environments. This required institutions to rethink current system functioning and urgently develop safe medical practices and protocols.To use a novel approach combining simulation-based clinical system testing with rapid cycle deliberate practice concepts for identifying latent safety threats presented by newly developed operating room COVID-19 protocols and collecting frontline staff recommendations for mitigation.This study design combined a training/education approach with probing the systems function. The primary outcomes were the number of latent safety threats and staff evaluations of this approach for feasibility and utility on immediate and four-month post surveys. Participants started the simulation which took place in the operating room, in the assistant role before graduating to the primary airway manager. Simulation staff members observed the simulations and noted whether elements in the protocols/checklists were followed and whether latent safety threats were present using an observation form. Solutions to latent safety threats were sought during the debriefing period.This approach identified 17 latent safety threats not foreseen during the planning stages and allowed for corrections to the protocols prior to impacting patient outcomes. Post-simulation surveys indicated that the program was well received and all who responded agreed that it was worth the time it took. Fifty-seven percent of respondents to four-month follow-up survey reported using the work products to care for an actual COVID-19 patient.This study demonstrated a flexible methodology that effectively integrated simulation-based training and systems tests to train staff and detect latent safety threats in the new workflows and provide recommendations for mitigation. While COVID was the specific prompt, this approach can be applicable in diverse clinical settings for training medical staff, testing system function, and mitigating potential latent safety threats.

The risk of SARS-CoV-2 infection is particularly high for healthcare workers during the pandemic. Home care workers visit many different households per shift. Encounters with mostly elderly patients and their relatives increase the potential for the undetected spread of SARS-CoV-2. In order to gain insight into the seroprevalence of SARS-CoV-2 antibodies and possible transmission risks in outpatient care, this follow-up study was conducted with nursing services in Hamburg. The aim was to estimate the dynamics of seroprevalence in this occupational group over a 12-month period, to identify occupation-specific risk factors, and to collect information on the vaccination status of the surveyed nursing staff. Antibody testing for SARS-CoV-2 IgG against the S1 domain (EUROIMUN Analyser I® Lübeck, Germany) was performed on participating healthcare workers with patient contact at a total of four time points within one year from July 2020 to October 2021 (baseline, follow-up after three, six and twelve months). The data were mostly analysed descriptively. Differences in IgG titres were analysed using variance analysis methods, particularly Tukey’s range test. The seroprevalence was 1.2% (8/678) at baseline and 1.5% (9/581) at the three-month follow-up (T1). At the second follow-up (T2) after six months, vaccination against SARS-CoV-2 was available from January 2021 onwards. The prevalence rate of positive IgG antibodies relative to the S1 domain of the spike protein test among unvaccinated individuals was 6.5%. At (T3) after twelve months (July to October 2021), 482 participants were enrolled, and 85.7% of the workers were considered fully vaccinated at this time point, while 51 individuals were unvaccinated. The prevalence was 13.7% (7/51). In our study, a low seroprevalence was found among home care workers, which was lower than in our studies conducted in the clinical setting. Therefore, it can be assumed that the occupational risk of infection is rather low for both the nursing staff and the patients/clients cared for in the outpatient setting. The good provision of protective equipment and the high vaccination rate of the staff probably had a positive influence.

There is a growing need for models that are interpretable and have reduced energy and computational cost (e.g., in health care analytics and federated learning). Examples of algorithms to train such models include logistic regression and boosting. However, one challenge facing these algorithms is that they provably suffer from label noise; this has been attributed to the joint interaction between oft-used convex loss functions and simpler hypothesis classes, resulting in too much emphasis being placed on outliers. In this work, we use the margin-based $\alpha$-loss, which continuously tunes between canonical convex and quasi-convex losses, to robustly train simple models. We show that the $\alpha$ hyperparameter smoothly introduces non-convexity and offers the benefit of "giving up" on noisy training examples. We also provide results on the Long-Servedio dataset for boosting and a COVID-19 survey dataset for logistic regression, highlighting the efficacy of our approach across multiple relevant domains. Comment: To appear in AISTATS 2023

The pandemic creates unprecedented challenges to society and to health care systems around the world. Like all crises, these provide a unique opportunity to rethink the fundamental limiting assumptions and institutional inertia of our established systems. These inertial assumptions have obscured deeply rooted problems in health care and deflected attempts to address them. As hospitals begin to welcome all patients back, they should resist the temptation to go back to business as usual. Instead, they should retain the more deliberative, explicit, and transparent ways of thinking that have informed the development of crisis standards of care. The key lesson to be learned from those exercises in rational deliberation is that justice must be the ethical foundation of all standards of care. Justice demands that hospitals take a safety-net approach to providing services that prioritizes the most vulnerable segments of society, continue to expand telemedicine in ways that improve access without exacerbating disparities, invest in community-based care, and fully staff hospitals and clinics on nights and weekends.