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使用塞拉利昂气候变化预测疟疾病例

Saidu Wurie Jalloh1,2, Boniface Malenje3, Herbert Imboga3

  • 1Department of Mathematics (Data Science Option), Pan African University Institute for Basic Sciences Technology and Innovation, Kiambu, 00200, Juja, Kenya. wurie.saidu@students.jkuat.ac.ke.

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概括

人工神经网络 (ANN) 在塞拉利昂疟疾预测方面显著优于传统的季节性自回归集成移动平均 (SARIMA) 模型. ANN提供更准确的预测,对于有效的公共卫生干预疟疾至关重要.

关键词:
人工神经网络的人工神经网络疾病监测 疾病监测疟疾:疟疾是一种疾病.公共卫生 公共卫生季节性自回归集成移动平均线塞拉利昂 塞拉利昂时间序列预测时间序列预测

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科学领域:

  • 流行病学 流行病学
  • 机器学习 机器学习
  • 公共卫生 公共卫生

背景情况:

  • 疟疾仍然是塞拉利昂的一个重大公共卫生问题.
  • 准确的疟疾预测对于有效的干预策略至关重要.
  • 传统的季节性自回归集成移动平均 (SARIMA) 模型在捕捉复杂疾病模式方面存在局限性.

研究的目的:

  • 为了比较SARIMA和人工神经网络 (ANN) 模型对塞拉利昂疟疾病例的预测性能.
  • 评估气候变量对疟疾预测准确性的影响.
  • 确定在高负担环境中预测疟疾的最有效的建模方法.

主要方法:

  • 开发了一个基线SARIMA模型和一个SARIMAX模型,包括降水量,最大温度和平均相对湿度.
  • 使用历史疟疾病例数据训练了一个人工神经网络 (ANN) 模型.
  • 通过使用平均绝对百分比误差 (MAPE) 和确定系数,比较了SARIMA,SARIMAX和ANN模型的预测准确度.

主要成果:

  • 该ANN模型实现了最低的平均绝对百分比误差 (MAPE) 6.68%,超过了SARIMA (12.01%) 和SARIMAX (11.45%) 模型.
  • 在降水和疟疾病例之间发现了强烈的正相关性 (r = 0.68).
  • 该ANN模型在捕捉疟疾发病率中的复杂,非线性时间模式方面表现出卓越的能力.

结论:

  • 人工神经网络 (ANN) 在疟疾预测方面非常有效,即使没有明确的气候数据输入.
  • 机器学习方法,如ANN,为加强高负担地区疟疾控制策略提供了显著的价值.
  • 该研究强调了ANN在改善公共卫生决策中疾病预测的准确性和及时性的潜力.