In this paper, we develop two fully parametric quantile regression models, based on power Johnson SB distribution Cancho et al. (2020), for modeling unit interval response at different quantiles. In particular, the conditional distribution is modelled by the power Johnson SB distribution. The maximum likelihood method is employed to estimate the model parameters. Simulation studies are conducted to evaluate the performance of the maximum likelihood estimators in finite samples. Furthermore, we discuss residuals and influence diagnostic tools. The effectiveness of our proposals is illustrated with two data set given by the mortality rate of COVID-19 in different countries.
Motivated by the case fatality rate (CFR) of COVID-19, in this paper, we develop a fully parametric quantile regression model based on the generalized three-parameter beta (GB3) distribution. Beta regression models are primarily used to model rates and proportions. However, these models are usually specified in terms of a conditional mean. Therefore, they may be inadequate if the observed response variable follows an asymmetrical distribution, such as CFR data. In addition, beta regression models do not consider the effect of the covariates across the spectrum of the dependent variable, which …
A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities,…
In this article, we deal with COVID-19 data to study the trend of the epidemic at the global situation. Choosing the mortality rate as an appropriate metric which measures the relative relation between the cumulative confirmed cases and death cases, we utilize the modified kernel estimator for random density function (Petersen and Muller, 2016) to obtain the density of the mortality rate, and apply Frechet change point detection (Dubey and Muller, 2020) to check if there is any significant change on the dynamic pattern of COVID-19 deaths. The analysis shows that the pattern of global COVID-19 …
F. Hoffmann-La Roche Ltd（罗氏制药公司）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ETH Zurich（苏黎世联邦理工学院）；John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）；MIT Critical Data, Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Harvard-MIT Health Sciences & Technology（哈佛-麻省理工健康科学与技术研究所医学工程与科学研究所计算生理学实验室MIT关键数据）；University Hospital of Tübingen（蒂宾根大学医院）；Kantonsspital Winterthur（温特图尔州立医院）
Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients. Due to the exponential growth of infections, many healthcare systems across the world are under pressure to care for increasing amounts of at-risk patients. Given the high number of infected patients, identifying patients with the highest mortality risk early is critical to enable effective intervention and optimal prioritisation of care. Here, we pre…
Widespread vaccination campaigns have changed the landscape for COVID-19, vastly altering symptoms and reducing morbidity and mortality. We estimate trends in mortality by month of admission and vaccination status among those hospitalised with COVID-19 in England between March 2020 to September 2021, controlling for demographic factors and hospital load. Among 259,727 hospitalised COVID-19 cases, 51,948 (20.0%) experienced mortality in hospital. Hospitalised fatality risk ranged from 40.3% (95% confidence interval 39.4-41.3%) in March 2020 to 8.1% (7.2-9.0%) in June 2021. Older individuals and…
We develop an air mobility index and use the newly developed Apple's driving trend index to evaluate the impact of COVID-19 on the crude oil price. We use quantile regression and stationary and non-stationary extreme value models to study the impact. We find that both the \textit{air mobility index} and \textit{driving trend index} significantly influence lower and upper quantiles as well as the median of the WTI crude oil price. The extreme value model suggests that an event like COVID-19 may push oil prices to a negative territory again as the air mobility decreases drastically during such p…
Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this paper we consider the problem of multi-period forecasting that aims to predict several horizons at once. We propose a novel approach that forces the prediction to be "smooth" across horizons and apply it to two tasks: point estimation via regression and interval prediction via quantile regression. This methodology was developed for real-time distributed COVID-19 forecasting. We illustrate the proposed technique with the CovidCast datas…
This article investigates whether time-varying quantile regression curves are the same up to the horizontal shift or not. The errors and the covariates involved in the regression model are allowed to be locally stationary. We formalize this issue in a corresponding non-parametric hypothesis testing problem, and develop an integrated-squared-norm based test (SIT) as well as a simultaneous confidence band (SCB) approach. The asymptotic properties of SIT and SCB under null and local alternatives are derived. Moreover, the asymptotic properties of these tests are also studied when the compared dat…
Coronavirus disease 2019 (COVID-19) is associated with a very high number of casualties in the general population. Assessing the exact magnitude of this number is a non-trivial problem, as relying only on officially reported COVID-19 associated fatalities runs the risk of incurring in several kinds of biases. One of the ways to approach the issue is to compare overall mortality during the pandemic with expected mortality computed using the observed mortality figures of previous years. In this paper, we build on existing methodology and propose two ways to compute expected as well as excess mor…
The ongoing COVID-19 pandemic has brought science to the fore of the public discourse and considering the complexity of the issues involved, with it also the challenge of effective and informative science communication. A particularly contentious topic, in that it is both highly emotional in and of itself, as well as in that it sits at the nexus of the decision-making process regarding the handling of the pandemic, which has effected lockdowns, social behaviour measures, business closures, and others, concerns the recording and the reporting of the disease mortality. To clarify a point which h…
Estimating the true mortality burden of COVID-19 for every country in the world is a difficult, but crucial, public health endeavor. Attributing deaths, direct or indirect, to COVID-19 is problematic. A more attainable target is the "excess deaths", the number of deaths in a particular period, relative to that expected during "normal times", and we estimate this for all countries on a monthly time scale for 2020 and 2021. The excess mortality requires two numbers, the total deaths and the expected deaths, but the former is unavailable for many countries, and so modeling is required for these c…
Mixed Time Series Quasi-Likelihood Models for Uncovering Covid-19 Viral Load and Mortality Dynamics
Loyola University Chicago（芝加哥洛约拉大学）；Southern Methodist University（南方卫理公会大学）；Queen Mary University of London（伦敦大学玛丽女王学院）；University College Dublin（都柏林大学学院）
Purpose: In this study we propose to identify significant economic, political, and social factors that helped contribute to the incidence and mortality of SARS-CoV-2 around the world during the first seven months of the COVID-19 pandemic. Methods: Retrospective analyses were conducted using data acquired by the World Health Organization regarding the number of new cases and deaths from 76 countries from January through July 2020. Hierarchical cluster and principal component analyses were used to categorize different countries based on their ability to control the outbreak based on number of ca…
Objective: We study the influence of local reopening policies on the composition of the infectious population and their impact on future hospitalization and mortality rates. Materials and Methods: We collected datasets of daily reported hospitalization and cumulative morality of COVID 19 in Houston, Texas, from May 1, 2020 until June 29, 2020. These datasets are from multiple sources (USA FACTS, Southeast Texas Regional Advisory Council COVID 19 report, TMC daily news, and New York Times county level mortality reporting). Our model, risk stratified SIR HCD uses separate variables to model the …
Introduction: Endpoint choice for randomized controlled trials of treatments for COVID-19 is complex. A new disease brings many uncertainties, but trials must start rapidly. COVID-19 is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks and can end in death. While improvement in mortality would provide unquestionable evidence about clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical. Furthermore, patient states in between "cure" and "death" represent meaningful distinctions.…
Autoregressive (AR) models are useful tools in time series analysis. Inferences under such models are distorted in the presence of measurement error, which is very common in practice. In this article, we establish analytical results for quantifying the biases of the parameter estimation in AR models if the measurement error effects are neglected. We propose two measurement error models to describe different processes of data contamination. An estimating equation approach is proposed for the estimation of the model parameters with measurement error effects accounted for. We further discuss fore…
