一种用于预测巴西登革热病例概率流行频段的统计模型
Laís Picinini Freitas1, Danielle Andreza da Cruz Ferreira2, Raquel Martins Lana3
1Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil.
Infectious Disease Modelling
|August 20, 2025
概括
一个新的贝叶斯模型预测巴西的登革热病例将提前52周发生. 概率流行病带有助于监测疫情,根据病例数量将季节分类为典型或非典型.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数学建模的数学建模
背景情况:
- 登革热是巴西的一个重要载体传播疾病,对公共卫生构成重大挑战.
- 登革热的负担不断增加,需要先进的模型来准备和应对疾病.
- 巴西卫生部寻求模拟解决方案来管理登革热疫情.
研究的目的:
- 为所有巴西卫生区开发贝叶斯预测模型,以提前52周预测登革热病例.
- 为有效的登革热爆发监测和评估,创建概率性流行病频段.
- 为了分类流行病的严重程度和偏离历史模式.
主要方法:
- 使用历史登革热病例数据开发了贝叶斯预测模型.
- 该模型预测了118个卫生区的每周病例,预计将持续52周.
- 使用百分位数定义了概率性流行病带,并根据历史发生进行解释.
主要成果:
- 该模型准确地捕获了验证季节 (2022-2023,2023-2024) 的流行病曲线形状.
- 2022-2023赛季被归类为"相当高,非典型",有1,436,034例病例.
- 2023-2024赛季是"非常高,非常不典型"的,有6,454,020例,超过了90%的百分比.
- 2024年至2025年的预测估计中位数为1,526,523例.
结论:
- 概率流行病带是监测登革热爆发的有效工具.
- 该模型有助于对当前疫情与历史模式进行前性比较.
- 这种方法有助于评估正在进行的登革热流行病的严重程度和非典型性.
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