{"title":"利用环境因素建立登革热传播动态统计模型","authors":"Lengyang Wang , Mingke Zhang","doi":"10.1016/j.csda.2024.108080","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"203 ","pages":"Article 108080"},"PeriodicalIF":1.5000,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Statistical modeling of Dengue transmission dynamics with environmental factors\",\"authors\":\"Lengyang Wang , Mingke Zhang\",\"doi\":\"10.1016/j.csda.2024.108080\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":55225,\"journal\":{\"name\":\"Computational Statistics & Data Analysis\",\"volume\":\"203 \",\"pages\":\"Article 108080\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Statistics & Data Analysis\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167947324001646\",\"RegionNum\":3,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Statistics & Data Analysis","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167947324001646","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Statistical modeling of Dengue transmission dynamics with environmental factors
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.
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 [...]
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