使用数据驱动的机器学习模型的手足口病爆发的季节性和气象驱动因素
Pakorn Lonlab1, Suparinthon Anupong2, Chalita Jainonthee3
1Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand.
Tropical medicine and infectious disease
|February 25, 2025
概括
机器学习模型确定了泰国手足口腔疾病 (HFMD) 爆发的高风险地区. XGBoost模型准确地预测了受温度和降雨影响的疫情,有助于公共卫生警告.
科学领域:
- 流行病学 流行病学
- 环境健康 环境健康
- 机器学习 机器学习
背景情况:
- 手足口腔疾病 (HFMD) 是一个主要的公共卫生问题,特别是在亚太地区的幼儿中.
- 在热带/亚热带地区,HFMD疫情与季节性变化和天气模式相关.
- 泰国在2011年至2022年期间报告了超过65万个HFMD病例.
研究的目的:
- 在泰国确定高风险和低风险的HFMD爆发地区.
- 评估用于HFMD爆发预测的机器学习模型.
- 了解气象因素对HFMD疫情的影响.
主要方法:
- 使用了后勤回归,SVM,决策树,随机森林,GBM和XGBoost模型.
- 评估模型性能,用于预测未见的数据.
- 确定了影响HFMD爆发的关键气象因素.
主要成果:
- 在预测HFMD爆发方面,XGBoost模型表现出卓越的性能.
- 确定了平均最高温度,平均降雨量和平均蒸汽压力作为重要因素.
- 在COVID-19大流行期间观察到高风险HFMD爆发月份/地区的减少.
结论:
- XGBoost模型有效地识别高风险HFMD区域,并了解影响因素.
- 该模型可以作为公共卫生官员的早期预警系统.
- 研究结果为缓解HFMD爆发提供了见解,包括在流行病期间.
相关概念视频
Steps in Outbreak Investigation
102
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:
102
Viral Mutations
32.1K
A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
32.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis
24
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
24


