用进化采样与预测框架预测登革热爆发的新方法
D Betteena Sheryl Fernando1, K Sheela Sobana Rani2
1Department of Artificial Intelligence and Data Science, St. Xavier's Catholic College of Engineering, Chunkankadai, Kanyakumari District, Tamil Nadu, India.
Journal of vector borne diseases
|August 16, 2025
概括
这项研究引入了一种新的登革热预测框架,通过解决数据不规则和缺失信息来提高准确性. 该模型对传染病的早期预警系统显示出希望.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 像登革热这样的危及生命的病毒性疾病在全球范围内正在增加.
- 登革热爆发数据往往不完整和不一致,阻碍了准确的预测.
- 可靠的预测模型对于有效的公共卫生干预至关重要.
研究的目的:
- 为准确预测登革热疫情发展一个创新的框架.
- 为了应对流行病学数据中时间和随机动态所带来的挑战.
- 提高传染病预测模型的可靠性.
主要方法:
- 这项研究结合了进化采样与预测 (ESP) 和一个MiniMax K-最接近邻居输入器.
- 一种新的火动态进化 (FDE) 方法被用于模型参数优化.
- 使用随机森林分类器捕获复杂的数据关系,通过十倍交叉验证进行评估.
主要成果:
- 拟议的模型在本地和巴西登革热数据集上展示了低的平均绝对误差 (MAE) 和根平均平方误差 (RMSE).
- 在本地数据集上,MAE达到22.1和RMSE达到46.37.
- 在巴西数据集上实现了48.36的MAE和86.76的RMSE,表明准确性和稳定性得到了改进.
结论:
- 开发的模型有效地处理了登革热爆发数据中的数据不规则和缺失值.
- 这些发现表明,登革热早期预警系统的潜力很大.
- 该框架可能适用于预测其他传染病.
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