数学分析和预测未来的登革热爆发时间变化的接触率使用机器学习方法
Md Shahidul Islam1, Pabel Shahrear2, Goutam Saha3
1Department of Computer Science and Engineering, Green University of Bangladesh, Kanchon, 1460, Bangladesh; Department of Mathematics, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh; Department of Computer Science and Engineering, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh.
Computers in biology and medicine
|June 13, 2024
概括
一个新的数学模型预测孟加拉国登革热爆发,蚊子感染在7月达到峰值,人类病例在9月. 机器学习预测2024年登革热病例将增加25%.
科学领域:
- 流行病学 流行病学
- 数学建模的数学建模
- 公共卫生 公共卫生
背景情况:
- 登革热在孟加拉国是一个重大的公共卫生挑战.
- 了解登革热爆发动态对于有效的控制策略至关重要.
研究的目的:
- 开发和验证一种用于分析孟加拉国登革热传播动态的新型数学模型.
- 使用数学和机器学习方法预测潜在的登革热疫情.
主要方法:
- 为了分析登革热的动态,开发了一种结合状函数的数学模型.
- 使用下一代矩阵方法来评估无疾病和特有平衡点.
- 机器学习,特别是先知模型,被用来预测未来的疫情.
主要成果:
- 该模型准确地预测了从5月中旬到10月下旬的登革热爆发模式,与政府数据保持一致.
- 蚊子感染的峰值预计在7月份,而人类感染的峰值预计在9月下旬.
- 机器学习模型预测,与2023年相比,在2024年登革热病例将增加25%.
结论:
- 这项研究为了解和管理孟加拉国登革热爆发提供了重大进展.
- 开发的模型和预测工具可以帮助公共卫生官员做好准备和干预.
- 建议在未来的登革热监测中继续监测和应用这些模型.
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