相关实验视频

Updated: Jan 12, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

气候因素及其对皮肤莱什曼病的影响:使用ARIMA模型预测患者数量

Behrad Pourmohammadi¹, Fatemeh Shahsavan², Fatemeh Paknazar³

¹Department of Parasitology, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran.

Parasitology international

|November 1, 2025

概括

气候因素显著影响皮肤莱什曼病 (CL) 发病率. 这项研究使用了气候数据和ARIMA模型来预测CL病例,强调需要及时对砂进行控制.

科学领域:

流行病学流行病学
环境健康环境健康
载体传播疾病载体传播疾病

背景情况:

皮肤莱什曼病 (Cutaneous Leishmaniasis,CL) 是一种由沙传播的动物性疾病.
了解影响CL发生率的因素对于控制策略至关重要.
达姆干县经历了CL疫情,需要进行本地化研究.

研究的目的:

调查气候因素对Damghan县CL发病率的影响.
使用统计模型预测未来的CL患者数量.
为公众健康干预提供 CL 预防和控制的信息.

主要方法:

从Damghan县卫生网络收集的月度和年度CL病例数据 (2012-2021年).
获取相关的气候数据 (气压,湿度,温度,降水,风速,日照时间).
采用SPSS和R软件进行统计分析,包括ARIMA模型用于预测.

主要成果:

在CL发生率和气压,相对湿度,日照时间,最高,平均和最低温度,降水量和最大风速之间发现了显著的相关性.
阿里马模型预测2023年约有370例CL病例,2024年约有180例.
季节性和年度气候变化被确定为CL发病率的主要驱动因素.

结论:

气候变化与皮肤莱什曼病传播动态密切相关.
建议在夏季之前采取主动的沙控制措施,以减轻疫情的爆发.
预测模型有助于公共卫生规划用于CL预防和管理.

关键词:

在ARIMA模型模型.气候参数气候参数皮肤上的莱什曼病.预测预测预测

更多相关视频

Cutaneous Leishmaniasis in the Dorsal Skin of Hamsters: a Useful Model for the Screening of Antileishmanial Drugs

Cutaneous Leishmaniasis in the Dorsal Skin of Hamsters: a Useful Model for the Screening of Antileishmanial Drugs

Published on: April 21, 2012

In Vivo Infection with Leishmania amazonensis to Evaluate Parasite Virulence in Mice

In Vivo Infection with Leishmania amazonensis to Evaluate Parasite Virulence in Mice

Published on: February 20, 2020

相关概念视频

What is Climate?

What is Climate?

Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis: