Lessons Learned from CDC's Global COVID-19 Early Warning and Response Surveillance System.

Early warning and response surveillance (EWARS) systems were widely used during the early COVID-19 response. Evaluating the effectiveness of EWARS systems is critical to ensuring global health security. We describe the Centers for Disease Control and Prevention (CDC) global COVID-19 EWARS (CDC EWARS) system and the resources CDC used to gather, manage, and analyze publicly available data during the prepandemic period. We evaluated data quality and validity by measuring reporting completeness and compared these with data from Johns Hopkins University, the European Centre for Disease Prevention and Control, and indicator-based data from the World Health Organization. CDC EWARS was integral in guiding CDC's early COVID-19 response but was labor-intensive and became less informative as case-level data decreased and the pandemic evolved. However, CDC EWARS data were similar to those reported by other organizations, confirming the validity of each system and suggesting collaboration could improve EWARS systems during future pandemics.

"keep the Receipts:" The Political Economy of IMF Austerity during and after the Crisis Years of 2009 and 2020

In 2009, the International Monetary Fund (IMF) reformed its lending arrangements and conditionality. Thereafter, it has pursued "parsimony,"emphasizing headline fiscal adjustments rather than detailed budgetary changes. This paper analyzes the extent to which these reforms have resulted in changes to the overall austerity required by IMF agreements. We create a new variable measuring the level of fiscal consolidation required in each IMF program from 2001 through 2021 the IMF Fiscal Adjustment Indicator (IMF FAI). We explore whether IMF austerity eased after the financial crisis and the later COVID-19 pandemic. We also estimate the economic and political determinants that help explain varying levels of IMF austerity across IMF programs during this period. We find that IMF conditions were less austere for the years of 2009 and 2020, but quickly returned to their previous levels, echoing the IMF's advice to "keep the receipts"during crises. However, these temporary relaxations were not statistically significant, pointing to overarching continuity. We find that countries that were granted relatively more lenient conditionality were found to be those with closer relations with major shareholders of the IMF: Western Europe and the United States. In contrast, countries with close diplomatic relations with China face higher IMF austerity. © 2022 Walter de Gruyter GmbH, Berlin/Boston 2022.

Age-stratified utility of NT-proBNP testing as part of a blind Emergency Department shortness of breath orderset (Jan-Dec 2019)

Background: NT-proBNP was added to our emergency department (ED) triage blind 'shortness of breath (SOB) orderset' for presenters aged >70. Evidence-based thresholds for acute heart failure (HF) diagnosis are >900/1800 pg/ml for ages 50-75/>75 respectively (1.2);their utility in contemporary practice is uncertain. Purpose: To assess the relation between blind NT-proBNP testing in this setting and (1) coded discharge diagnosis stratified by age, and (2) all-cause mortality at medium-term follow-up. Methods:We retrieved all ED 'SOB' blood ordersets (1.1.2019-31.12.2019), including NT-proBNP, Hb, electrolytes, creatinine, troponin, CRP, d-dimer, and coded discharge diagnoses. Multivariate logistic regression models for all-cause survival (at 9.9.2021) were assessed. Results: There were 638 presentations (median age 76.1), unexpectedly including 198 <70 years. Modal and median lengths of stay were 0 and 1 day respectively. Stratified by age (<60, 60-69, 70-74, 75-79, 80-84, ≥85y), the proportion with HF coded as primary discharge diagnosis (5, 7, 9, 17, 18, 25% respectively) and all-comer all-cause mortality at 2.2±0.3 years (13, 42, 40, 48, 48, 49%) steadily increased (Table;orderset variables presented as median (inter-quartile range)). Median NT-proBNP was 3672, 2667, and 321 pg/ml when HF was in the primary, secondary, or neither coded discharge diagnosis field respectively;2.2-year-all-cause mortality was 54%, 60%, and 35%. In those with a primary HF discharge code, 77% of 349 presenters ≥75y and 88% of 231 aged 50-74 had NT-proBNP >1800/900 pg/ml respectively. In those without an HF code, 26% in both age cohorts had NT-proBNP >1800/900 pg/ml (dotted lines in Figure, panels A/B, respectively represent NT-proBNP thresholds). Independent predictors of all-cause mortality for patients with a primary or secondary HF code were ln(NT-proBNP) (OR 1.26, 95% CI 1-1.59) and serum Na+ (OR 0.93, 0.88-0.99);for patients without an HF code, these were serum K+ (1.87, 1.21-2.88), ln(NT-proBNP) (1.35, 1.15-1.58), ln(CRP) (1.18, 1.02-1.36), length of stay (1.08, 1.03-1.12), and age (1.03, 1.01-1.06). (Figure Presented) Conclusion: HF detection with NT-proBNP in a blind SOB orderset showed increasing sensitivity with age with the best specificity >75 years. Most presenters stayed ≤1 day, so blind testing at triage facilitates HF detection. NT-proBNP independently predicted 2.2-year-all-cause mortality irrespective of discharge HF coding. This is notable as the commonest non-HF causes of acute SOB are prognostically important at >70 years and follow-up occurred through the Covid-19 pandemic. The findings may reflect disease severity in patients without HF, but also suggest that discharge HF coding status does not identify all those with prognostically relevant HF.

CNN based transfer learning framework for classification of COVID-19 disease from chest X-ray

Today SARS-COVID-2 causes Novel Coronavirus diseases throughout in more than 150 countries all over the world. The quicker diagnosis is very crucial to reduce the outbreak of this diseases. The clinic al studies regarding this disease has shown that patients lungs are very much affected after the infection of coronavirus. Chest X-Ray, CT Scan are the most effective imaging approaches for identification of COVID 19 disease. Deep Learning approaches are one of the important approaches of machine learning that gives a critical analysis regarding for study of large amount of image datasets that can make some earlier impact of diseases. in recent years. To analyze the disease 1000 images are used for training and 150 images are used for testing the data from an online available standardized dataset of Kaggle. Here the images are taken as Covid and Non-Covid as the 2 class levels to classify the images using CNN. Here the activation function ReLU provides more than 90 percent of accuracy rates for classification and validation of COVID 19, diseases using CNN based deep learning model. The kernel sizes, other activation functions are varying and accordingly it changes the performance of system. This task concentrates on the approaches of classifying covid-19 infected patients appropriately. © 2021 IEEE.

Remote Patient Monitoring during pandemic caused by COVID-19 using Semantic Web Technologies

Data integration facilities along with decrease in the quantified operational health information occurs due to stuck in hospital admission . Exchanging of information resources has an immense significance due to 3 critical capabilities. These can be enlisted as information swap over through electronic connectivity, interoperability, and compromising under prescribed medication, in which automated systems are involved to solve pathological discrepancies. Along with all these concern about patient safety is taken care of by this startegy. The substantial awareness can be resulted in computational methods and tools which are quite capable for rendering guidance to health care services more resourcefully, which further leads the advancement and execution of intellectual systems within the therapeutic domain. Exchanging of data in the Web can be complies with Resource Description Framework (RDF) and ontology’s. RDF has the capability of capturing information content instead of considering the syntax. Inferential support and transformational approach is feasible by deploying RDF. Facilitation of data by evolution and merging phenomenon can be utilised by the involvement of RDF though schemas variation exists. Semantic interoperability is achieved either by applying standardisation or by translation. Though standardisation is quite acceptable but due to time annihilation transformation in lieu of modernisation is acceptable more. Strategically, realistic approach of semantic interoperability comprises both standards and translations. Conceptual classes, their interrelations, instances and axioms exist in Ontology. © 2021 Institute of Physics Publishing. All rights reserved.