GrowthPredict:一个工具箱和基于教程的基础教程,用于使用现象学增长模型拟定和预测增长轨迹
Gerardo Chowell1, Amanda Bleichrodt2, Sushma Dahal2
1Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA. gchowell@gsu.edu.
Scientific reports
|January 18, 2024
概括
本研究介绍了GrowthPredict,这是一个Matlab工具箱,用于使用动态模型实时预测疾病爆发等增长过程. 它为研究人员和政策制定者提供了可访问的工具,以预测轨迹和量化不确定性.
科学领域:
- 数学生物学 数学生物学
- 应用统计学应用统计学
- 流行病学 流行病学
背景情况:
- 准确的增长过程的短期预测,如疾病爆发,需要可访问的动态建模工具,量化不确定性.
- 现有的用户友好工具箱用于使用现象学增长模型实时预测时间序列轨迹是有限的.
研究的目的:
- 介绍并说明 GrowthPredict,这是一个 MATLAB 工具箱,用于使用基于普通微分方程的增长模型来拟合和预测时间序列数据.
- 为数学生物学,应用统计学和传染病建模领域的学生和研究人员提供一个用户友好的资源.
主要方法:
- 开发了GrowthPredict,这是一个Matlab工具箱,实现现象学动态增长模型 (指数式,通用增长,Gompertz,通用后勤,Richards).
- 嵌入功能用于时间序列预测,通过参数引导量化不确定性,以及在各种模型和数据条件中进行性能评估.
- 利用公开可用的数据,包括美国的 (mpox) 疫情,进行演示和验证.
主要成果:
- GrowthPredict提供了一个灵活且易于访问的平台,用于适应和预测各种增长轨迹.
- 该工具箱可以生成实时的短期预测,并量化不确定性,这对于决策至关重要.
- 通过示例和教程视频展示了GrowthPredict的实用性,突出了其在疾病爆发分析中的应用.
结论:
- GrowthPredict提供了一个有价值的,用户友好的资源,用于使用简单的动态增长模型来描述和预测时间序列数据.
- 该工具箱促进了有关控制策略和干预对传染过程的影响评估的知情政策决策.
- 增强对自然和社会增长现象进行实时,不确定性量化预测的能力.
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