Willumsenmcmanus4466

The false negative rate of the diagnostic RT-PCR test for SARS-CoV-2 has been reported to be substantially high. Due to limited availability of testing, only a non-random subset of the population can get tested. Hence, the reported test counts are subject to a large degree of selection bias. We consider an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model under both selection bias and misclassification. We derive closed form expression for the basic reproduction number under such data anomalies using the next generation matrix method. We conduct extensive simulation studies to quantify the effect of misclassification and selection on the resultant estimation and prediction of future case counts. Finally we apply the methods to reported case-death-recovery count data from India, a nation with more than 5 million cases reported over the last seven months. We show that correcting for misclassification and selection can lead to more accurate prediction of case-counts (and death counts) using the observed data as a beta tester. The model also provides an estimate of undetected infections and thus an under-reporting factor. For India, the estimated under-reporting factor for cases is around 21 and for deaths is around 6. We develop an R-package (SEIRfansy) for broader dissemination of the methods.

Smoking impairs lung immune functions and damages upper airways, increasing risks of contracting and severity of infectious diseases.

We searched PubMed and Embase for studies published from January 1-May 25, 2020. We included studies reporting smoking behavior of COVID-19 patients and progression of disease, including death. We used a random effects meta-analysis and used meta-regression and lowess regressions to examine relationships in the data.

We identified 47 peer-reviewed papers with a total of 31,871 COVID-19 patients, 5,759 (18.1%) experienced disease progression and 5,734 (18.0%) with a history of smoking. Among smokers, 29.2% experienced disease progression, compared with 21.1% of non-smokers. The meta-analysis confirmed an association between smoking and COVID-19 progression (OR 1.56, 95% CI 1.32-1.83, p=0.001). Smoking was associated with increased risk of death from COVID-19 (OR 1.19, 95% CI 1.05-1.34, p=0.007). We found no significant difference (p=0.432) between the effects of smoking on COVID-19 disease progression between adjusted and unadjusted analyses, suggesting that smoking is an independent risk factor for COVID-19 disease progression. We also found the risk of having COVID-19 progression among younger adults (p=0.023), with the effect most pronounced among people under about 45 years old.

Smoking is an independent risk for having severe progression of COVID-19, including mortality. The effects seem to be higher among young people. Smoking prevention and cessation should remain a priority for the public, physicians, and public health professionals during the COVID-19 pandemic.

Smoking is an independent risk for having severe progression of COVID-19, including mortality. The effects seem to be higher among young people. Smoking prevention and cessation should remain a priority for the public, physicians, and public health professionals during the COVID-19 pandemic.Background The COVID-19 epidemic of 2019-20 is due to the novel coronavirus SARS-CoV-2. Following first case description in December, 2019 this virus has infected over 10 million individuals and resulted in at least 500,000 deaths world-wide. The virus is undergoing rapid mutation, with two major clades of sequence variants emerging. buy Diphenyleneiodonium This study sought to determine whether SARS-CoV-2 sequence variants are associated with differing outcomes among COVID-19 patients in a single medical system. Methods Whole genome SARS-CoV-2 RNA sequence was obtained from isolates collected from patients registered in the University of Washington Medicine health system between March 1 and April 15, 2020. Demographic and baseline medical data along with outcomes of hospitalization and death were collected. Statistical and machine learning models were applied to determine if viral genetic variants were associated with specific outcomes of hospitalization or death. Findings Full length SARS-CoV-2 sequence was obtained 190 subjects wher major clade of virus include patient age and comorbid conditions.Pediatric COVID-19 following SARS-CoV-2 infection is associated with fewer hospitalizations and often milder disease than in adults. A subset of children, however, present with Multisystem Inflammatory Syndrome in Children (MIS-C) that can lead to vascular complications and shock, but rarely death. The immune features of MIS-C compared to pediatric COVID-19 or adult disease remain poorly understood. We analyzed peripheral blood immune responses in hospitalized SARS-CoV-2 infected pediatric patients (pediatric COVID-19) and patients with MIS-C. link2 MIS-C patients had patterns of T cell-biased lymphopenia and T cell activation similar to severely ill adults, and all patients with MIS-C had SARS-CoV-2 spike-specific antibodies at admission. A distinct feature of MIS-C patients was robust activation of vascular patrolling CX3CR1+ CD8 T cells that correlated with use of vasoactive medication. link3 Finally, whereas pediatric COVID-19 patients with acute respiratory distress syndrome (ARDS) had sustained immune activation, MIS-C patients displayed clinical improvement over time, concomitant with decreasing immune activation. Thus, non-MIS-C versus MIS-C SARS-CoV-2 associated illnesses are characterized by divergent immune signatures that are temporally distinct and implicate CD8 T cells in clinical presentation and trajectory of MIS-C.During early stages of the COVID-19 pandemic, forecasts provided actionable information about disease transmission to public health decision-makers. Between February and May 2020, experts in infectious disease modeling made weekly predictions about the impact of the pandemic in the U.S. We aggregated these predictions into consensus predictions. In March and April 2020, experts predicted that the number of COVID-19 related deaths in the U.S. by the end of 2020 would be in the range of 150,000 to 250,000, with scenarios of near 1m deaths considered plausible. The wide range of possible future outcomes underscored the uncertainty surrounding the outbreak's trajectory. Experts' predictions of measurable short-term outcomes had varying levels of accuracy over the surveys but showed appropriate levels of uncertainty when aggregated. An expert consensus model can provide important insight early on in an emerging global catastrophe.Background South Africa recently experienced a first peak in COVID-19 cases and mortality. Dexamethasone and remdesivir both have the potential to reduce COVID-related mortality, but their cost-effectiveness in a resource-limited setting with scant intensive care resources is unknown. Methods We projected intensive care unit (ICU) needs and capacity from August 2020 to January 2021 using the South African National COVID-19 Epi Model. We assessed cost-effectiveness of 1) administration of dexamethasone to ventilated patients and remdesivir to non-ventilated patients, 2) dexamethasone alone to both non-ventilated and ventilated patients, 3) remdesivir to non-ventilated patients only, and 4) dexamethasone to ventilated patients only; all relative to a scenario of standard care. We estimated costs from the healthcare system perspective in 2020 USD, deaths averted, and the incremental cost effectiveness ratios of each scenario. Results Remdesivir for non-ventilated patients and dexamethasone for ventilated patients was estimated to result in 1,111 deaths averted (assuming a 0-30% efficacy of remdesivir) compared to standard care, and save $11.5 million. The result was driven by the efficacy of the drugs, and the reduction of ICU-time required for patients treated with remdesivir. The scenario of dexamethasone alone to ventilated and non-ventilated patients requires additional $159,000 and averts 1,146 deaths, resulting in $139 per death averted, relative to standard care. Conclusions The use of dexamethasone for ventilated and remdesivir for non-ventilated patients is likely to be cost-saving compared to standard care. Given the economic and health benefits of both drugs, efforts to ensure access to these medications is paramount.Objective Children's hospitals frequently care for infants with various life-threatening airway anomalies. Management of these infants can be challenging given unique airway anatomy and potential malformations. Airway emergency management must be immediate and precise, often demanding specialized equipment and/or expertise. We developed a Neonatal-Infant Airway Safety Program to improve medical responses, communication, equipment usage and outcomes for infants requiring emergent airway interventions. Patients and Methods All patients admitted to our quaternary neonatal and infant intensive care unit (NICU) from 2008-2019 were included in this study. Our program consisted of a multidisciplinary airway response team, pager system, and emergency equipment cart. Respiratory therapists present at each emergency event recorded specialist response times, equipment utilization, and outcomes. A multidisciplinary oversite committee reviewed each incident. Results Since 2008, there were 159 airway emergency events in our NICU (~12 per year). Mean specialist response times decreased from 5.9±4.9 min (2008-2012, mean±SD) to 4.3±2.2 min (2016-2019, p=0.12), and the number of incidents with response times >5 min decreased from 28.8±17.8% (2008-2012) to 9.3±11.4% (2016-2019, p=0.04 by linear regression). As our program became more standardized, we noted better equipment availability and subspecialist communication. Few emergency situations (n=9, 6%) required operating room management. There were 3 patient deaths (2%). Conclusions Our airway safety program, including readily available specialists and equipment, facilitated effective resolution of airway emergencies in our NICU and multidisciplinary involvement enabled rapid and effective changes in response to COVID-19 regulations. A similar program could be implemented in other centers.Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning systems to detect COVID-19 from chest radiographs rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals.

Every year, Puerto Rico faces a hurricane season fraught with potentially catastrophic structural, emotional and health consequences. In 2017, Puerto Rico was hit by Hurricane Maria, the largest natural disaster to ever affect the island. Several studies have estimated the excess morbidity and mortality following Hurricane Maria in Puerto Rico, yet no study has comprehensively examined the underlying health system weaknesses contributing to the deleterious health outcomes.

A qualitative case study was conducted to assess the ability of the UPR health system to provide patient care in response to Hurricane Maria. An established five key resilience framework and inductive analysis was used to identify factors that affected health system resilience. Thirteen Emergency Medicine Physicians, Family Medicine Physicians, and Hospital Administrators in a University of Puerto Rico (UPR) Community Hospital were interviewed as part of our study.

Of the five key resiliency components, three domains were notably weak with respect to UPR resiliency.