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菲律宾登革热复发的空间分布分析和比较预测 (2025-2027):全国性研究
Kenny Oriel Aranas Olana1,2, Napaphat Poprom3, Pallop Siewchaisakul3
1Doctor of Public Health Program, Faculty of Public Health, Chiang Mai University, Chiang Mai, Thailand.
Transboundary and emerging diseases
|October 24, 2025
概括
神经网络自回归 (NNAR) 最好预测菲律宾未来的登革热病例. 这项研究比较了多种预测模型,确定NNAR在预测年度登革热爆发和为公共卫生战略提供信息方面优越.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 登革热在流行地区构成了重大公共卫生挑战.
- 准确预测登革热的趋势和疫情对于有效的疾病控制至关重要.
- 时间序列预测方法被广泛用于预测传染病模式.
研究的目的:
- 确定菲律宾登革热病例的空间分布,趋势和季节性.
- 为了比较用于登革热预测的各种时间序列模型的预测准确度.
- 为了生成菲律宾3年登革热病例预测.
主要方法:
- 利用菲律宾所有省份的2017年至2024年的每月登革热病例数据.
- 与季节性自回归集成移动平均 (SARIMA),神经网络自回归 (NNAR),随机森林 (RF),长短期记忆 (LSTM),TBATS和先知模型进行比较.
- 使用培训和测试数据集评估模型性能,生成3年预测.
主要成果:
- 分析了1,903,425例登革热病例,平均每月发病率为每10万人口17.66±15.97.
- 确定了定期的季节性登革热流行病,从7月到9月达到顶峰.
- NNAR模型表现出卓越的预测准确性,预测从2025年到2027年平均每年有444,678例病例.
结论:
- 在全国范围内,NNAR是预测菲律宾登革热病例的最有效模型.
- 这项研究是首次将SARIMA,RF,LSTM,TBATS和Prophet等先进模型应用于菲律宾国家登革热预测.
- 通过先进的疾病预测,这些发现为加强登革热监测,预防和控制策略提供了宝贵的见解.
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