Background: Health systems responsiveness is the key to addressing infectious disease threats such as pandemics. The paper outlines an assessment of health systems resilience by exploring the association of health systems and Global Health Security (GHS) parameters with case load and mortality resulting from COVID-19 across 203 countries using an ecological design. Methodology: Correlation analysis was performed to assess the relationship of each of the indicators with COVID 19 cases and deaths per million population. Stepwise multiple regression models were developed to determine the predictors of COVID-19 cumulative cases and deaths per million population separately. Results: Global health security indicators seemed to have a strong association when analyzed individually but those did not necessarily translate into less burden of cases or deaths in the multivariable analysis. The predictors of cumulative deaths per million population included general government expenditure on health as a proportion of general government expenditure, responsiveness of the system to prevent the emergence and release of pathogens and governance related voice and accountability. Conclusion: To conclude, health financing parameters and preventive activities with regard to emergence of pathogens were better predictors of cumulative COVID-19 cases and deaths per million population compared to other health systems and global health security indicators.Key messages• Health financing parameters are better predictors of cumulative COVID-19 cases and deaths per million population compared to other health systems and global health security indicators.• More robust and valid indicators are required to assess the performance of health system.

Abstract: Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach

Objectives: COVID-19 mortality rates differ across countries. We aimed to construct a model that predicts mortality worldwide, by including only country-level socioeconomic and health system indicators and excluding variables related to short-term measures for pandemic management. Methods: COVID-19 mortality data was collected from Johns Hopkins University resource center. Additional sources were public reports from the United Nations, the World Bank and the Heritage Foundation. We implemented multiple linear regression with backward elimination on the selected predictors.Results: The final model constructed on seven Independent variables, significantly predicted COVID-19 mortality rate by country (F-statistic: 29.2, p

Decision Science and Technology is a compilation of chapters written in honor of a remarkable man, Ward Edwards. Among Ward's many contributions are two significant accomplishments, either of which would have been enough for a very distinguished career. First, Ward is the founder of behavioral decision theory. This interdisciplinary discipline addresses the question of how people actually confront decisions, as opposed to the question of how they should make decisions. Second, Ward laid the groundwork for sound normative systems by noticing which tasks humans can do well and which tasks computers should perform. This volume, organized into five parts, reflects those accomplishments and more. The book is divided into four sections: `Behavioral Decision Theory' examines theoretical descriptions and empirical findings about human decision making. `Decision Analysis' examines topics in decision analysis.`Decision in Society' explores issues in societal decision making. The final section, `Historical Notes', provides some historical perspectives on the development of the decision theory. Within these sections, major, multi-disciplinary scholars in decision theory have written chapters exploring some very bold themes in the field, as an examination of the book's contents will show. The main reason for the health of the Decision Analysis field is its close links between theory and applications that have characterized it over the years. In this volume, the chapters by Barron and Barrett; Fishburn; Fryback; Keeney; Moreno, Pericchi, and Kadane; Howard; Phillips; Slovic and Gregory; Winkler; and, above all, von Winterfeldt focus on those links. Decision science originally developed out of concern with real decision problems; and applied work, such as is represented in this volume, will help the field to remain strong.

Neutrophils regulate immune responses during homeostasis as well as disease pathogenesis. Especially, the neutrophils extracellular traps largely contribute to necroinflammation. This book highlights the role of neutrophils and neutrophils extracellular traps in various sterile and non-sterile, acute and chronic inflammatory conditions affecting both human and animal health.

Health at a Glance provides a comprehensive set of indicators on population health and health system performance across OECD members and key emerging economies. This edition has a special focus on the health impact of COVID-19 in OECD countries, including deaths and illness caused by the virus, adverse effects on access and quality of care, and the growing burden of mental ill-health.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.