Statistical modeling of Dengue transmission dynamics with environmental factors

IF 1.5 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computational Statistics & Data Analysis Pub Date : 2024-11-08 DOI:10.1016/j.csda.2024.108080

Lengyang Wang , Mingke Zhang

引用次数: 0

Abstract

Dengue fever is one of the most common mosquito-borne infectious diseases in tropical regions. Understanding the dynamics of dengue transmission can help provide timely early warnings, thereby reducing mortality. However, previous studies have failed to simulate faithfully dengue dynamics and answer questions pertinent to outbreaks. By incorporating environmental factors into a time-series-susceptible-infectious-recovered (TSIR) model, a new substantive model, to analyze their impact on transmission, is proposed. The newly proposed environmental-time-series-susceptible-infectious-recovered (ETSIR) model can highlight statistically their significance on dengue transmission, thus providing deeper insight into the transmission and addressing several epidemiological puzzles.

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利用环境因素建立登革热传播动态统计模型

登革热是热带地区最常见的蚊媒传染病之一。了解登革热的传播动态有助于及时发出预警，从而降低死亡率。然而，以往的研究未能忠实地模拟登革热的动态，也未能回答与疫情爆发相关的问题。通过将环境因素纳入时间序列-易感-感染-恢复（TSIR）模型，提出了一个新的实质性模型，以分析环境因素对传播的影响。新提出的环境-时间序列-易感-感染-恢复（ETSIR）模型可以从统计学角度突出环境因素对登革热传播的重要性，从而更深入地了解登革热的传播，并解决一些流行病学难题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Statistics & Data Analysis 数学-计算机：跨学科应用

CiteScore

3.70

自引率

5.60%

发文量

167

审稿时长

60 days

期刊介绍： Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]

期刊最新文献

Editorial Board Efficient Bayesian functional principal component analysis of irregularly-observed multivariate curves Statistical modeling of Dengue transmission dynamics with environmental factors Analysis of order-of-addition experiments A goodness-of-fit test for functional time series with applications to Ornstein-Uhlenbeck processes