Epidemiologic Methods最新文献_第3页

Incidence moments: a simple method to study the memory and short term forecast of the COVID-19 incidence time-series 发病矩:一种研究COVID-19发病时间序列记忆和短期预测的简单方法

Q3 Mathematics

Epidemiologic Methods

Pub Date : 2022-02-01 DOI: 10.1515/em-2021-0029

Mauricio Canals L, Andrea Canals C, Cristóbal Cuadrado N

Abstract Objectives The ability to predict COVID-19 dynamic has been very low, reflected in unexpected changes in the number of cases in different settings. Here the objective was to study the temporal memory of the reported daily incidence time series and propose a simple model for short-term forecast of the incidence. Methods We propose a new concept called incidence moments that allows exploring the memory of the reported incidence time series, based on successive products of the incidence and the reproductive number that allow a short term forecast of the future incidence. We studied the correlation between the predictions of and the reported incidence determining the best predictor. We compared the predictions and observed COVID-19 incidences with the mean arctangent absolute percentage error (MAAPE) analyses for the world, 43 countries and for Chile and its regions. Results The best predictor was the third moment of incidence, determining a short temporal prediction window of 15 days. After 15 days the absolute percentage error of the prediction increases significantly. The method perform better for larger populations and presents distortions in contexts of abrupt changes in incidence. Conclusions The epidemic dynamics of COVID 19 had a very short prediction window, probably associated with an intrinsic chaotic behavior of its dynamics. The incident moment modeling approach could be useful as a tool whose simplicity is appealing, since it allows rapid implementation in different settings, even with limited epidemiological technical capabilities and without requiring a large amount of computational data.

摘要目的预测新冠肺炎动态的能力一直很低，反映在不同环境下病例数的意外变化上。本文的目的是研究报告的每日发病率时间序列的时间记忆，并提出一个简单的发病率短期预测模型。我们提出了一个新的概念，即发生率矩，它可以基于发生率和繁殖数的连续乘积来探索已报道的发病率时间序列的记忆，从而可以短期预测未来的发病率。我们研究了预测和报告发病率之间的相关性，确定了最佳预测因子。我们将预测和观察到的COVID-19发病率与全球、43个国家和智利及其地区的平均反正切绝对百分比误差(MAAPE)分析进行了比较。结果最佳预测因子是发病的第三时刻，确定了15天的短时间预测窗口。15天后，预测的绝对百分比误差显著增加。该方法在较大的人群中表现更好，并且在发病率突变的背景下呈现扭曲。结论2019冠状病毒病流行动力学预测窗口很短，可能与其动力学固有的混沌性有关。事件矩建模方法可能是一种有用的工具，它的简单性很吸引人，因为它可以在不同的环境下快速实施，即使流行病学技术能力有限，也不需要大量的计算数据。

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引用次数: 1

Numerical modelling of coronavirus pandemic in Peru 秘鲁冠状病毒大流行的数值模拟

Q3 Mathematics

Epidemiologic Methods

Pub Date : 2022-02-01 DOI: 10.1515/em-2020-0026

C. Jiménez, M. Merma

Abstract Objectives The main objective of this research is to demonstrate the effectiveness of non-pharmaceutical interventions (social isolation and quarantine) and of vaccination. Methods The SIR epidemiological numerical model has been revised to obtain a new model (SAIRDQ), which involves additional variables: the population that died due to the disease (D), the isolated (A), quarantined population (Q) and the effect of vaccination. We have obtained the epidemiological parameters from the data, which are not constant during the evolution of the pandemic, using an iterative approximation method. Results Analysis of the data of infected and deceased suggest that the evolution of the coronavirus epidemic in Peru has arrived at the end of the second wave (around October 2021). We have simulated the effect of quarantine and vaccination, which are effective measures to reduce the impact of the pandemic. For a variable infection and isolation rate, due to the end of the quarantine, the death toll would be around 200 thousand; if the isolation and quarantine were relaxed since March 01, 2021, there could be more than 280 thousand deaths. Conclusions Without non-pharmaceutical interventions and vaccination, the number of deaths would be much higher than 280 thousand.

本研究的主要目的是证明非药物干预措施(社会隔离和检疫)和疫苗接种的有效性。方法对SIR流行病学数值模型进行修正，得到一个新模型(SAIRDQ)，该模型增加了因病死亡人群(D)、隔离人群(a)、隔离人群(Q)和疫苗接种效果等变量。我们使用迭代逼近法从数据中获得流行病学参数，这些参数在大流行的演变过程中不是恒定的。结果对感染病例和死亡病例数据的分析表明，秘鲁冠状病毒疫情的演变已经到达第二波结束(2021年10月左右)。我们模拟了隔离和接种疫苗的效果，这是减少大流行影响的有效措施。在感染和隔离率可变的情况下，由于隔离的结束，死亡人数将在20万人左右;如果从2021年3月1日起放松隔离检疫，可能会有超过28万人死亡。结论如果没有非药物干预和疫苗接种，死亡人数将远远高于28万人。

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引用次数: 0

The impact of test positivity on surveillance with asymptomatic carriers 检测阳性对无症状感染者监测的影响

Q3 Mathematics

Epidemiologic Methods

Pub Date : 2022-02-01 DOI: 10.1101/2022.06.10.22276234

M. Gaspari

Abstract Objectives Recent studies show that Test Positivity Rate (TPR) gains a better correlation than incidence with the number of hospitalized patients in COVID-19 pandemic. Nevertheless, epidemiologists remain sceptical concerning the widespread use of this metric for surveillance, and indicators based on known cases like incidence rate are still preferred despite the large number of asymptomatic carriers, which remain unknown. Our aim is to compare TPR and incidence rate, to determine which of the two has the best characteristics to predict the trend of hospitalized patients in the COVID-19 pandemic. Methods We perform a retrospective study considering 60 outbreak cases, using global and local data from Italy in different waves of the pandemic, in order to detect peaks in TPR time series, and peaks in incidence rate, finding which of the two indicators has the best ability to anticipate peaks in patients admitted in hospitals. Results On average, the best TPR-based approach anticipates the incidence rate of about 4.6 days (95 % CI 2.8, 6.4), more precisely the average distance between TPR peaks and hospitalized peaks is 17.6 days (95 % CI 15.0, 20.4) with respect to 13.0 days (95 % CI 10.4, 15.8) obtained for incidence. Moreover, the average difference between TPR and incidence rate increased to more than 6 days in the Delta outbreak during summer 2021, where presumably the percentage of asymptomatic carriers was larger. Conclusions We conclude that TPR should be used as the primary indicator to enable early intervention, and for predicting hospital admissions in infectious diseases with asymptomatic carriers.

【摘要】目的近期研究表明，2019冠状病毒病(COVID-19)大流行期间，检测阳性率(TPR)与住院人数的相关性优于发病率。然而，流行病学家仍然对广泛使用这一指标进行监测持怀疑态度，尽管大量无症状携带者仍然未知，但基于发病率等已知病例的指标仍然是首选。我们的目的是比较TPR和发病率，确定两者中哪一个最能预测2019冠状病毒病大流行期间住院患者的趋势。方法对60例暴发病例进行回顾性研究，利用意大利在不同流行波中的全球和当地数据，以检测TPR时间序列的峰值和发病率的峰值，找出这两个指标中哪一个最能预测住院患者的峰值。结果平均而言，基于TPR的最佳方法预计发病率约为4.6天(95 % CI 2.8, 6.4)，更准确地说，TPR峰值与住院高峰之间的平均距离为17.6天(95 % CI 15.0, 20.4)，而发病率为13.0天(95 % CI 10.4, 15.8)。此外，2021年夏季三角洲疫情中，TPR和发病率之间的平均差异增加到6天以上，无症状携带者的比例可能更大。结论TPR应作为早期干预的主要指标，用于预测无症状感染者的住院率。

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引用次数: 0

Selection bias and multiple inclusion criteria in observational studies 观察性研究中的选择偏倚和多重纳入标准

Q3 Mathematics

Epidemiologic Methods

Pub Date : 2022-01-01 DOI: 10.1515/em-2022-0108

Stina Zetterstrom, I. Waernbaum

Abstract Objectives Spurious associations between an exposure and outcome not describing the causal estimand of interest can be the result of selection of the study population. Recently, sensitivity parameters and bounds have been proposed for selection bias, along the lines of sensitivity analysis previously proposed for bias due to unmeasured confounding. The basis for the bounds is that the researcher specifies values for sensitivity parameters describing associations under additional identifying assumptions. The sensitivity parameters describe aspects of the joint distribution of the outcome, the selection and a vector of unmeasured variables, for each treatment group respectively. In practice, selection of a study population is often made on the basis of several selection criteria, thereby affecting the proposed bounds. Methods We extend the previously proposed bounds to give additional guidance for practitioners to construct i) the sensitivity parameters for multiple selection variables and ii) an alternative assumption free bound, producing only logically feasible values. As a motivating example we derive the bounds for causal estimands in a study of perinatal risk factors for childhood onset Type 1 Diabetes Mellitus where selection of the study population was made by multiple inclusion criteria. To give further guidance for practitioners, we provide a data learner in R where both the sensitivity parameters and the assumption-free bounds are implemented. Results The assumption-free bounds can be both smaller and larger than the previously proposed bounds and can serve as an indicator of settings when the former bounds do not produce feasible values. The motivating example shows that the assumption-free bounds may not be appropriate when the outcome or treatment is rare. Conclusions Bounds can provide guidance in a sensitivity analysis to assess the magnitude of selection bias. Additional knowledge is used to produce values for sensitivity parameters under multiple selection criteria. The computation of values for the sensitivity parameters is complicated by the multiple inclusion/exclusion criteria, and a data learner in R is provided to facilitate their construction. For comparison and assessment of the feasibility of the bound an assumption free bound is provided using solely underlying assumptions in the framework of potential outcomes.

研究对象的选择可能导致暴露和结果之间的虚假关联，而不是描述感兴趣的因果估计。最近，针对选择偏倚提出了敏感性参数和界限，这与之前针对未测量混杂引起的偏倚提出的敏感性分析是一致的。边界的基础是研究人员指定了在附加识别假设下描述关联的敏感性参数的值。敏感性参数分别描述了每个治疗组的结果联合分布、未测量变量的选择和向量的各个方面。在实践中，研究人群的选择通常是根据几个选择标准进行的，从而影响了建议的界限。我们扩展了先前提出的边界，为从业者提供额外的指导，以构建i)多个选择变量的敏感性参数和ii)一个替代假设自由边界，只产生逻辑上可行的值。作为一个有启发性的例子，我们在一项关于儿童发病1型糖尿病围产期危险因素的研究中推导出因果估计的界限，其中研究人群的选择是通过多个纳入标准进行的。为了给从业者提供进一步的指导，我们在R中提供了一个数据学习器，其中实现了灵敏度参数和无假设边界。结果无假设边界可以比先前提出的边界更小或更大，并且可以在先前的边界不能产生可行值时作为设置的指标。激励的例子表明，当结果或治疗罕见时，无假设边界可能不合适。结论界限可以为敏感性分析评估选择偏倚的程度提供指导。额外的知识用于在多个选择标准下产生灵敏度参数的值。灵敏度参数值的计算因多个纳入/排除标准而变得复杂，在R中提供了一个数据学习器来方便它们的构建。为了比较和评估边界的可行性，在潜在结果的框架中仅使用基本假设提供了假设自由边界。

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引用次数: 2

Methodological proposal for constructing a classifier algorithm in clinical diagnostics of diseases using Bayesian methods 基于贝叶斯方法构建疾病临床诊断分类算法的方法学建议

Q3 Mathematics

Epidemiologic Methods

Pub Date : 2022-01-01 DOI: 10.1515/em-2021-0020

José Rafael Tovar Cuevas, Andrés Camilo Méndez Alzate, Diana María Caicedo Borrero, Juan David Díaz Mutis, Lizeth Fernanda Suárez Mensa, Lyda Elena Osorio Amaya

Abstract Objectives To develop a methodological proposal to build clinical classifiers using information about signs and symptoms reported by the patient in initial the consultation and laboratory test results. Methods The proposed methodology considers procedures typical of the Bayesian paradigm of statistics as predictive probabilities and the sequential use of the Bayes formula. Additionally, some procedures belonging to classical statistics, such as Youden’s index and ROC curves, are applied. The method assumes two possible scenarios; when the patient only reports the signs and symptoms and the physician does not have access to information from laboratory tests. The other one is when the physician, besides the patient’s information, knows the blood test results. The method is illustrated using data from patients diagnosed with dengue. Results The performance of the proposed method depends of the set of signs and symptoms and the laboratory tests considered by the doctor as good indicators of presence of the sick in the individual. Conclusions The classifier can be used as a screening tool in scenarios where there is no extensive experience treating sick individuals, or economic and social conditions do not allow laboratory methods or gold standard procedures to complete the diagnosis.

【摘要】目的提出一种方法建议，利用患者在初次会诊时报告的体征和症状信息和实验室检查结果建立临床分类器。方法提出的方法考虑了贝叶斯统计范式的典型过程，如预测概率和贝叶斯公式的顺序使用。此外，还应用了一些经典统计方法，如约登指数和ROC曲线。该方法假设了两种可能的情况;当患者只报告体征和症状，医生无法获得实验室检查的信息时。另一种情况是，医生除了知道病人的信息外，还知道验血结果。用诊断为登革热的患者的数据说明了这种方法。结果所提出的方法的性能取决于一组体征和症状和实验室测试被医生认为是良好的指标存在的疾病在个人。结论该分类器可作为筛查工具，在没有丰富的治疗病人经验，或经济和社会条件不允许实验室方法或金标准程序完成诊断的情况下使用。

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引用次数: 0

Identification of time delays in COVID-19 data 识别COVID-19数据的时间延迟

Q3 Mathematics

Epidemiologic Methods

Pub Date : 2021-11-26 DOI: 10.1515/em-2022-0117

N. Guglielmi, E. Iacomini, Alex Viguerie

Abstract Objective COVID-19 data released by public health authorities is subject to inherent time delays. Such delays have many causes, including delays in data reporting and the natural incubation period of the disease. We develop and introduce a numerical procedure to recover the distribution of these delays from data. Methods We extend a previously-introduced compartmental model with a nonlinear, distributed-delay term with a general distribution, obtaining an integrodifferential equation. We show this model can be approximated by a weighted-sum of constant time-delay terms, yielding a linear problem for the distribution weights. Standard optimization can then be used to recover the weights, approximating the distribution of the time delays. We demonstrate the viability of the approach against data from Italy and Austria. Results We find that the delay-distributions for both Italy and Austria follow a Gaussian-like profile, with a mean of around 11 to 14 days. However, we note that the delay does not appear constant across all data types, with infection, recovery, and mortality data showing slightly different trends, suggesting the presence of independent delays in each of these processes. We also found that the recovered delay-distribution is not sensitive to the discretization resolution. Conclusions These results establish the validity of the introduced procedure for the identification of time-delays in COVID-19 data. Our methods are not limited to COVID-19, and may be applied to other types of epidemiological data, or indeed any dynamical system with time-delay effects.

摘要目的公共卫生部门发布的新冠肺炎疫情数据存在固有的时间差。这种延迟有许多原因，包括数据报告的延迟和疾病的自然潜伏期。我们开发并引入了一个数值程序来从数据中恢复这些延迟的分布。方法利用一般分布的非线性分布延迟项，对先前引入的分区模型进行扩展，得到一个积分微分方程。我们证明了该模型可以用常数时滞项的加权和来近似，从而产生一个关于分布权重的线性问题。然后可以使用标准优化来恢复权重，近似时间延迟的分布。我们用意大利和奥地利的数据证明了这种方法的可行性。我们发现，意大利和奥地利的延迟分布都遵循高斯分布，平均约为11至14天。然而，我们注意到，延迟并不是在所有数据类型中都是恒定的，感染、恢复和死亡率数据显示出略有不同的趋势，这表明在这些过程中存在独立的延迟。我们还发现恢复的延迟分布对离散化分辨率不敏感。结论该方法可有效识别COVID-19数据中的时滞。我们的方法不仅限于COVID-19，而且可以应用于其他类型的流行病学数据，或者实际上任何具有时滞效应的动态系统。

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引用次数: 2

A guide to value of information methods for prioritising research in health impact modelling. 关于确定健康影响建模研究优先次序的信息方法价值指南。

Q3 Mathematics

Epidemiologic Methods

Pub Date : 2021-11-15 eCollection Date: 2021-01-01 DOI: 10.1515/em-2021-0012

Christopher Jackson, Robert Johnson, Audrey de Nazelle, Rahul Goel, Thiago Hérick de Sá, Marko Tainio, James Woodcock

Health impact simulation models are used to predict how a proposed policy or scenario will affect population health outcomes. These models represent the typically-complex systems that describe how the scenarios affect exposures to risk factors for disease or injury (e.g. air pollution or physical inactivity), and how these risk factors are related to measures of population health (e.g. expected survival). These models are informed by multiple sources of data, and are subject to multiple sources of uncertainty. We want to describe which sources of uncertainty contribute most to uncertainty about the estimate or decision arising from the model. Furthermore, we want to decide where further research should be focused to obtain further data to reduce this uncertainty, and what form that research might take. This article presents a tutorial in the use of Value of Information methods for uncertainty analysis and research prioritisation in health impact simulation models. These methods are based on Bayesian decision-theoretic principles, and quantify the expected benefits from further information of different kinds. The expected value of partial perfect information about a parameter measures sensitivity of a decision or estimate to uncertainty about that parameter. The expected value of sample information represents the expected benefit from a specific proposed study to get better information about the parameter. The methods are applicable both to situationswhere the model is used to make a decision between alternative policies, and situations where the model is simply used to estimate a quantity (such as expected gains in survival under a scenario). This paper explains how to calculate and interpret the expected value of information in the context of a simple model describing the health impacts of air pollution from motorised transport. We provide a general-purpose R package and full code to reproduce the example analyses.

健康影响模拟模型用于预测拟议的政策或情景将如何影响人口健康结果。这些模型代表了典型的复杂系统，描述了场景如何影响疾病或伤害风险因素（如空气污染或身体不活动）的暴露，以及这些风险因素如何与人群健康指标（如预期生存率）相关。这些模型由多个数据来源提供信息，并受到多个不确定性来源的影响。我们想描述哪些不确定性来源对模型产生的估计或决策的不确定性贡献最大。此外，我们想决定进一步的研究应该集中在哪里，以获得进一步的数据来减少这种不确定性，以及研究可能采取的形式。本文介绍了在健康影响模拟模型中使用信息价值方法进行不确定性分析和研究优先级的教程。这些方法基于贝叶斯决策理论原理，并从不同类型的进一步信息中量化预期收益。关于参数的部分完全信息的期望值测量决策或估计对该参数的不确定性的敏感性。样本信息的期望值表示从特定的拟议研究中获得更好的参数信息的预期收益。这些方法既适用于模型用于在替代政策之间做出决策的情况，也适用于模型仅用于估计数量（如场景下的预期生存收益）的情况。本文解释了如何在描述机动交通空气污染对健康影响的简单模型的背景下计算和解释信息的期望值。我们提供了一个通用的R包和完整的代码来重现示例分析。

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引用次数: 0

Development and application of an evidence-based directed acyclic graph to evaluate the associations between metal mixtures and cardiometabolic outcomes 基于证据的有向无环图的开发和应用，以评估金属混合物与心脏代谢结果之间的关联

Q3 Mathematics

Epidemiologic Methods

Pub Date : 2021-03-08 DOI: 10.1101/2021.03.05.21252993

E. Riseberg, R. Melamed, K. James, T. Alderete, L. Corlin

Abstract Objectives Specifying causal models to assess relationships among metal mixtures and cardiometabolic outcomes requires evidence-based models of the causal structures; however, such models have not been previously published. The objective of this study was to develop and evaluate a directed acyclic graph (DAG) diagraming metal mixture exposure and cardiometabolic outcomes. Methods We conducted a literature search to develop the DAG of metal mixtures and cardiometabolic outcomes. To evaluate consistency of the DAG, we tested the suggested conditional independence statements using linear and logistic regression analyses with data from the San Luis Valley Diabetes Study (SLVDS; n=1795). We calculated the proportion of statements supported by the data and compared this to the proportion of conditional independence statements supported by 1,000 DAGs with the same structure but randomly permuted nodes. Next, we used our DAG to identify minimally sufficient adjustment sets needed to estimate the association between metal mixtures and cardiometabolic outcomes (i.e., cardiovascular disease, fasting glucose, and systolic blood pressure). We applied them to the SLVDS using Bayesian kernel machine regression, linear mixed effects, and Cox proportional hazards models. Results From the 42 articles included in the review, we developed an evidence-based DAG with 74 testable conditional independence statements (43 % supported by SLVDS data). We observed evidence for an association between As and Mn and fasting glucose. Conclusions We developed, tested, and applied an evidence-based approach to analyze associations between metal mixtures and cardiometabolic health.

目的建立因果模型来评估金属混合物与心脏代谢结果之间的关系，需要基于证据的因果结构模型;然而，这样的模型以前没有发表过。本研究的目的是开发和评估金属混合物暴露和心脏代谢结果的有向无环图(DAG)。方法通过文献检索，建立金属混合物的DAG与心脏代谢结果的关系。为了评估DAG的一致性，我们使用圣路易斯谷糖尿病研究(SLVDS)的数据进行线性和逻辑回归分析，测试了建议的条件独立陈述;n = 1795)。我们计算了数据支持的语句的比例，并将其与1,000个具有相同结构但随机排列节点的dag支持的条件独立语句的比例进行了比较。接下来，我们使用DAG来确定估算金属混合物与心脏代谢结果(即心血管疾病、空腹血糖和收缩压)之间关联所需的最低限度调整集。我们使用贝叶斯核机回归、线性混合效应和Cox比例风险模型将它们应用于SLVDS。从纳入的42篇文章中，我们建立了一个基于证据的DAG，包含74个可测试的条件独立语句(43 %由SLVDS数据支持)。我们观察到As和Mn与空腹血糖之间存在关联的证据。我们开发、测试并应用了一种基于证据的方法来分析金属混合物与心脏代谢健康之间的关系。

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引用次数: 3

Statistical modeling of COVID-19 deaths with excess zero counts 超零计数COVID-19死亡的统计建模

Q3 Mathematics

Epidemiologic Methods

Pub Date : 2021-02-01 DOI: 10.1515/em-2021-0007

S. Khedhiri

Abstract Objectives Modeling and forecasting possible trajectories of COVID-19 infections and deaths using statistical methods is one of the most important topics in present time. However, statistical models use different assumptions and methods and thus yield different results. One issue in monitoring disease progression over time is how to handle excess zeros counts. In this research, we assess the statistical empirical performance of these models in terms of their fit and forecast accuracy of COVID-19 deaths. Methods Two types of models are suggested in the literature to study count time series data. The first type of models is based on Poisson and negative binomial conditional probability distributions to account for data over dispersion and using auto regression to account for dependence of the responses. The second type of models is based on zero-inflated mixed auto regression and also uses exponential family conditional distributions. We study the goodness of fit and forecast accuracy of these count time series models based on autoregressive conditional count distributions with and without zero inflation. Results We illustrate these methods using a recently published online COVID-19 data for Tunisia, which reports daily death counts from March 2020 to February 2021. We perform an empirical analysis and we compare the fit and the forecast performance of these models for death counts in presence of an intervention policy. Our statistical findings show that models that account for zero inflation produce better fit and have more accurate forecast of the pandemic deaths. Conclusions This paper shows that infectious disease data with excess zero counts are better modelled with zero-inflated models. These models yield more accurate predictions of deaths related to the pandemic than the generalized count data models. In addition, our statistical results find that the lift of travel restrictions has a significant impact on the surge of COVID-19 deaths. One plausible explanation of the outperformance of zero-inflated models is that the zero values are related to an intervention policy and therefore they are structural.

摘要目的利用统计方法对COVID-19感染和死亡的可能轨迹进行建模和预测是当前最重要的课题之一。然而，统计模型使用不同的假设和方法，从而产生不同的结果。监测疾病进展的一个问题是如何处理多余的零计数。在本研究中，我们从拟合和预测COVID-19死亡的准确性方面评估了这些模型的统计经验性能。方法文献中提出了两种模型来研究计数时间序列数据。第一类模型基于泊松和负二项条件概率分布来解释数据的分散，并使用自动回归来解释响应的依赖性。第二类模型基于零膨胀混合自回归，也使用指数族条件分布。我们研究了这些基于自回归条件计数分布的计数时间序列模型的拟合优度和预测精度。我们使用突尼斯最近发布的在线COVID-19数据来说明这些方法，该数据报告了2020年3月至2021年2月的每日死亡人数。我们进行了实证分析，并比较了这些模型在存在干预政策的情况下对死亡人数的拟合和预测性能。我们的统计结果表明，考虑零通货膨胀的模型具有更好的拟合性，并且对大流行死亡的预测更准确。结论用零膨胀模型可以较好地模拟具有超零计数的传染病数据。这些模型对与大流行有关的死亡人数的预测比广义计数数据模型更准确。此外，我们的统计结果发现，取消旅行限制对COVID-19死亡人数激增产生了重大影响。对于零膨胀模型的优异表现，一个合理的解释是，零值与干预政策有关，因此它们是结构性的。

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引用次数: 9

Factors affecting the recovery of Kurdistan province COVID-19 patients: a cross-sectional study from March to June 2020 库尔德斯坦省新冠肺炎患者康复影响因素:2020年3 - 6月横断面研究

Q3 Mathematics

Epidemiologic Methods

Pub Date : 2021-02-01 DOI: 10.1515/em-2020-0041

Eghbal Zandkarimi

Abstract Objectives The Coronavirus disease 2019 (COVID-19) is a new viral disease of the coronavirus family that has a close relationship with SARS species. This study aims to identify factors affecting the recovery of COVID-19 patients in a population with a majority of Kurdish residents. Methods For this purpose, all clinical and demographic parameters were collected from patients with COVID-19 who were outpatients or hospitalized in Kurdistan province (located in western Iran) from March to June 2020. We used the binary logistic regression model to recognition affecting factors to recovery in the COVID-19. Results According to the results of this study, age, sex, coronary heart disease (CHD), cancer, and using antiviral drugs were associated with the chance of recovery. Conclusions Based on the findings of this study, it can be concluded that the chances of recovery of COVID-19 patients who are elderly or have underlying diseases such as CHD or cancer are low. On the other hand, viral drugs are effective in increasing the chances of recovery.

摘要目的2019冠状病毒病(COVID-19)是冠状病毒科的一种新型病毒性疾病，与SARS有密切的关系。本研究旨在确定影响以库尔德居民为主的人群中COVID-19患者康复的因素。方法收集2020年3月至6月在伊朗西部库尔德斯坦省(Kurdistan province)门诊或住院的COVID-19患者的所有临床和人口统计学参数。我们使用二元logistic回归模型识别影响COVID-19康复的因素。结果年龄、性别、冠心病(CHD)、癌症、使用抗病毒药物与康复机会相关。根据本研究结果，可以得出结论，老年或有冠心病、癌症等基础疾病的COVID-19患者康复的机会较低。另一方面，抗病毒药物在增加康复机会方面是有效的。

引用次数: 8