Biostatistics and Epidemiology

In this paper, the Bayesian structural time series model (BSTS) is used to analyze and predict total confirmed cases who infected COVID-19 in the United States from February 28, 2020 through April 6, 2020 using the collect data from CDC (Center of Disease Control) in the United States. It includes variables of days, total confirmed cases, confirmed cases daily, death cases daily, and fatality rates. The author exploits the flexibility of Local Linear Trend, Seasonality, Contemporaneous covariates of dynamic coefficients in the Bayesian structural time series models. In addition, Causal Impact function in R programming is applied to analyze the model and read report of model. The results of the model show that the total confirmed cases who infected COVID-19 will be still most likely to increase straightly, the total numbers infected COVID-19 would be broken through 600,000 in the United States in near future (in the subsequent months). And then arrive at the peak around mid-May 2020. Also, the model suggests that the probability of variable Recovered cases daily is 0.07.

Liming Xie

Biostatistics & Epidemiology

The analysis and forecasting COVID-19 cases in the United States using Bayesian structural time series models

使用贝叶斯结构时间序列模型分析和预测美国COVID-19病例

Emerging Infectious Disease of Coronavirus 2019 is a catastrophe of human beings. Controlling, monitoring, and forecasting the COVID-19 outbreak is very important for the authorities to make a decision on launching the policy for suppressing it. Reproduction numbers of epidemiology models and forecasting models have been developed to control and monitor the COVID-19 outbreak via policies of authorities, such as social distancing and wearing mask. Thus, the purposes of this research are to estimate the reproduction number of susceptible infectious recovered models and forecast the number of total COVID-19 cases every day based on discrete logistic models. Moreover, chaos analysis and spreading power of the number of COVID-19 cases by day are investigated with regard to COVID-19 situation in India, Bangladesh, and Myanmar. The results showed that its spread was highest in India and the transmission potential of COVID-19 in Myanmar is higher compared to India and Bangladesh.

Rapin Sunthornwat

Yupaporn Areepong

Reproduction number, discrete forecasting model, and chaos analytics for Coronavirus Disease 2019 outbreak in India, Bangladesh, and Myanmar

新型冠状病毒肺炎在印度、孟加拉国和缅甸的传播数量、离散预测模型及混沌分析

The novel coronavirus SARS-CoV-2 was first identified in China in December 2019. In just over five months, the virus affected over 4 million people and caused about 300,000 deaths. This study aimed to model new COVID-19 cases in Italian regions using a new curve. A new empirical curve is proposed to model the number of new cases of COVID-19. It resembles a known exponential growth curve, which has a straight line as an exponent, but in the growth curve proposed, the exponent is a logistic curve multiplied for a straight line. This curve shows an initial phase, the expected exponential growth, then rises to the maximum value and finally reaches zero. We characterized the epidemic growth patterns for the entire Italian nation and each of the 20 Italian regions. The estimated growth curve has been used to calculate the expected time of the beginning, the time related to peak, and the end of the epidemics. Our analysis explores the development of the outbreaks in Italy and the impact of the containment measures. Data obtained are useful to forecast future scenarios and the possible end of the epidemic.

Davide Sisti

Ettore Rocchi

Sara Peluso

Stefano Amatori

Margherita Carletti

A new regression model for the forecasting of COVID-19 outbreak evolution: an application to Italian data

一种用于预测COVID-19疫情演变的新回归模型：以意大利数据为例的应用

Causal inference with observational data is a central goal in many fields. Propensity score methods are design-based approaches that try to ensure covariate balance without using information from the outcome variables. Analysis-based approaches, such as the Bayesian Additive Regression Tree and the Causal Forest, bypass the issue of covariate balance, and directly model the outcomes. We use a Monte Carlo simulation to study the performance of these two types of approaches. Some of the simulation scenarios involve large number of covariates relative to the number of observations. We find that the analysis-based approaches can yield very poor performance, without any warning about not enough overlap between the covariate distributions for the treated and control groups. In contrast, the propensity score methods provide warning about not enough overlap, but such warning could be overly-cautious when there is enough overlap.

Junni L. Zhang

A comparative study of design-based and analysis-based approaches to causal inference with observational data

基于设计和基于分析的因果推断方法比较研究

Viral surveillance is an essential task in public health that yields specific data, such as biological and epidemiological characteristics, crucial in the fight against viruses. We have recently developed an online platform for monitoring of Tick-Borne Encephalitis Virus, TBEV Analyzer, equipped with phylogenetic analysis and geographic map visualization functions. The phylogenetic analysis employs the clusteron approach, which has been earlier proposed by one of the authors of this paper. The analysis is based on a genetic pattern and provides three-fold hierarchical information about subtype, phylogenetic lineage, and clusteron. A clusteron is a group of TBEV strains with identical amino acid sequences of the glycoprotein E fragment, as a rule phylogeographically close, and having a certain type of territorial distribution. We enhance the algorithms and quality of visualization by integrating our platform with the GenBank database, adding a heat map distribution, and providing more details in the analysis report. To demonstrate the performance of the upgraded system, we analyze recent strains published in GenBank and compare them with the results of the manual analysis performed by a specialist. The results show that the system can rapidly and accurately determine query strain characteristics. This suggests possible applications in the field of public health.

Majid Forghani

Sergey Kovalev

Mikhail Bolkov

Michael Khachay

Pavel Vasev

TBEV analyzer platform for evolutionary analysis and monitoring tick-borne encephalitis virus: 2020 update

TBEV分析平台用于进化分析和监测蜱传脑炎病毒：2020年更新

Owing to the high uptake of vaccines and the low mortality of the Omicron variants, the Chinese government has shifted away from the ‘dynamic zero-COVID’ policy, which used to be effective for controlling the spread of the COVID-19 pandemic but with a high economic cost. This study proposes a Dynamic Self-Protection Intensity (DSPI) strategy, a liberalization policy can be used in the later stages of a pandemic. We investigate its impact on society particularly on the burden of medical needs. If citizens in Shenzhen have adopted the weak and strong DSPI strategies proposed in this paper at the beginning of the waiver of ‘dynamic zero-COVID’, it would effectively alleviate the run on medical resources by postponing the peak backwards to two or three lower peaks. This study also provides some insight into how to prevent and control future pandemic outbreaks.

Chao Liu

Haonan Long

Guanpeng Li

Pengzhen Chen

Zhen Zhang

Jie Huang

Bin Zhu

Xinxin Han

Yanqing Hu

Jian Qing Shi

Dongfeng Gu

Impact of dynamic self-protection intensity on the COVID-19 pandemic: a case study in Shenzhen based on medical resources

动态自保护强度对COVID-19大流行的影响：基于医疗资源的深圳案例研究

Yi Xia

Ao Yuan

Quantile set-regression with application in precision medicine

分位数集回归及其在精准医学中的应用

Identification and estimation of causal effects relies on the positivity assumption, which states that there should be some positive probability of having each treatment level of interest, regardless of covariate levels. Violations of positivity can lead to both non-identified target causal parameters, estimator bias and inflated variance. We discuss reasons for and responses to positivity violations when the causal effect of interest involves treatments or exposures at multiple time points. For illustration, we use a data example involving the cumulative effect of controlling arterial oxygen tension over time on death among ICU patients receiving invasive mechanical ventilation. We distinguish between practical (by chance) violations that relate to sample size and structural violations in which certain treatment levels occur with zero probability for certain covariate levels. We focus on responses that either redefines the target population, e.g. via trimming or redefines the intervention making it more dynamic or more stochastic. Supported by a simulation study, we illustrate how these responses help restore needed positivity but also modify the target causal parameter. We further introduce an inability to intervene variable and show how such a variable will often be a time-dependent confounder and essential when addressing structural positivity violations in longitudinal settings.

Aksel K.G. Jensen

Theis Lange

Olav L. Schjørring

Maya L. Petersen

Identification and responses to positivity violations in longitudinal studies: an illustration based on invasively mechanically ventilated ICU patients

纵向研究中对积极违规的识别与反应：基于有创机械通气ICU患者的实例分析

The omicron infection rate led many countries to lock down. The variant risk assessment and new ones have been a high uncertainty. In this study, the model training is used across all data. By this point in the modeling process, the optimal hyperparameters that gave the best performance on the outer loop validation dataset have already been identified. Specific validation data given for our measurements are taken daily and set some arguments to skip between validation data. The results showed 58 days for UK research at 9 areas in two months displayed that the Northwest Enn. It confirmed most infection cases: values in November 2021 were 11,305,9811 for n (confirmed cases), 86.8% of infection rate, confidence low was 86.14% and 87.40 % for high confidence high. Values for November 22, 2021, were 16,985, and 15,415 for high confirmed cases, and 94.4% of infection rate, confidence low was 93.99, and 94.83 for high, respectively.

Xiyu Deng

Xiaoyan Xie

Shanshan Hu

Analysis and prediction of omicron infection cases daily in the United Kingdom in November and December 2021

2021年11月和12月英国omicron感染病例的分析与预测

Shunichiro Orihara

Yurika Amamoto

Masataka Taguri

Simple and robust estimation of average treatment effects for the overlap population using model averaging

使用模型平均对重叠人群的平均处理效应进行简单且稳健的估计

Publication Information	Publisher: Taylor and Francis Ltd.，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2017，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%

Biostatistics and Epidemiology

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