Estimation of COVID-19 spread curves integrating global data and borrowing information

Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19

Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models

Global sensitivity analysis aims at quantifying the impact of input variability onto the variation of the response of a computational model. It has been widely applied to deterministic simulators, for which a set of input parameters has a unique corresponding output value. Stochastic simulators, however, have intrinsic randomness due to their use of (pseudo)random numbers, so they give different results when run twice with the same input parameters but non-common random numbers. Due to this random nature, conventional Sobol' indices, used in global sensitivity analysis, can be extended to stochastic simulators in different ways. In this paper, we discuss three possible extensions and focus on those that depend only on the statistical dependence between input and output. This choice ignores the detailed data generating process involving the internal randomness, and can thus be applied to a wider class of problems. We propose to use the generalized lambda model to emulate the response distribution of stochastic simulators. Such a surrogate can be constructed without the need for replications. The proposed method is applied to three examples including two case studies in finance and epidemiology. The results confirm the convergence of the approach for estimating the sensitivity indices even with the presence of strong heteroskedasticity and small signal-to-noise ratio

Multi-contact Walking Pattern Generation based on Model Preview Control of 3D COM Accelerations

We present a multi-contact walking pattern generator based on preview-control of the 3D acceleration of the center of mass (COM). A key point in the design of our algorithm is the calculation of contact-stability constraints. Thanks to a mathematical observation on the algebraic nature of the frictional wrench cone, we show that the 3D volume of feasible COM accelerations is a always a downward-pointing cone. We reduce its computation to a convex hull of (dual) 2D points, for which optimal O(n log n) algorithms are readily available. This reformulation brings a significant speedup compared to previous methods, which allows us to compute time-varying contact-stability criteria fast enough for the control loop. Next, we propose a conservative trajectory-wide contact-stability criterion, which can be derived from COM-acceleration volumes at marginal cost and directly applied in a model-predictive controller. We finally implement this pipeline and exemplify it with the HRP-4 humanoid model in multi-contact dynamically walking scenarios

Structured Psychosocial Stress and Therapeutic Intervention: Toward a Realistic Biological Medicine

Using generalized 'language of thought' arguments appropriate to interacting cognitive modules, we explore how disease states can interact with medical treatment, including, but not limited to, drug therapy. The feedback between treatment and response creates a kind of idiotypic 'hall of mirrors' generating a pattern of 'efficacy', 'treatment failure', and 'adverse reactions' which will, from a Rate Distortion perspective, embody a distorted image of externally-imposed structured psychosocial stress. This analysis, unlike current pharmacogenetics, does not either reify 'race' or blame the victim by using genetic structure to place the locus-of-control within a group or individual. Rather, it suggests that a comparatively simple series of questions to identify longitudinal and cross-sectional stressors may provide more effective guidance for specification of individual therapy than complicated genotyping strategies of dubious meaning. These latter are likely to be both very expensive and utterly blind to the impact of structured psychosocial stress -- a euphemism for various forms of racism and ethnic cleansing -- which, we contend, is often a principal determinant of treatment outcome at both individual and community levels of organization. We propose, to effectively address 'health disparities' between populations, and in contrast to current biomedical ideology based on a simplistic genetic determinism, a richer program of biological medicine reflecting Lewontin's 'triple helix' of genes, environment, and development, a program more in concert with the realities of a basic human biology marked by hypersociality unusual in vertibrates. Aggressive social, economic, and other policies of affirmative action to redress the persisting burdens of history would be an integral component of any such project

Pathways to Economic Outcomes and the Impact of Health: Comparing Hispanic and Non-Hispanic Adults after Foster Care

Abstract This study examines the financial outcomes in adulthood of Hispanics (N = 87) and White (Non-Hispanic, N = 498) persons placed in foster care during childhood. It uses the Casey Family Programs National Alumni Study (CFPNAS) database. Path models including predictors such as gender, education, having a partner, preparation for leaving care, and problem characteristics yielded predominantly similar effects for Hispanic and White Non-Hispanic respondents. The direct effect of physical and mental health conditions such as physical or learning disability, visual or hearing impairments, or DSM disorders more strongly predicted negative outcomes for White (Non-Hispanic) respondents than for Hispanic ones

Modeling neurocognitive and neurobiological recovery in addiction

This book focuses on "what to know" and "how to apply" information, prioritizing novel principles and delineating cutting-edge assessment, phenotyping and treatment tools