用于预测2022年纽约市Mpox疫情的数学方法的性能分析
David Kaftan1, Hae-Young Kim1, Charles Ko2
1NYU Grossman School of Medicine, New York, New York, USA.
Journal of medical virology
|August 2, 2024
概括
使用指数增长模型在纽约市早期的mopox (麻疹) 病例预测是不准确的. 后来,一种易受-暴露-感染-恢复 (SEIR) 模型改善了预测,强调了需要更好的流行病学数据来应对早期爆发的需求.
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:
- 纽约市 (NYC) 是2022年中期美国mopox疫情的中心.
- 实时mopox病例预测被提供给纽约市卫生和心理卫生部,以应对疫情.
- 随着流行病的演变,预测方法适应了.
研究的目的:
- 评估纽约市mopox疫情期间不同预测模型的有效性.
- 与指数增长模型相比,回顾性评估SEIR模型是否可以改善早期爆发预测.
- 确定了解病例检测率对SEIR模型准确性的影响.
主要方法:
- 最初利用了指数增长模型,原因是未知的风险人口规模.
- 随着指数式增长放缓,转向易受感染-感染-恢复 (SEIR) 模型.
- 追溯分析了SEIR模型的性能,无论是否了解病例检测率.
主要成果:
- 早期的指数增长模型表现不佳,平均绝对误差 (MAE) 显著增加.
- 一旦有了对易感人群大小的见解 (7周的MAE:每周13.4例),SEIR模型准确地预测了疫情的剩余部分.
- 流行病学数据不足阻碍了早期的SEIR模型参数化.
结论:
- 指数式增长模型对于未知参数的疫情爆发的早期阶段是不够的.
- 当有足够的流行病学数据时,SEIR模型为mopox疫情预测提供了更高的准确性.
- 了解风险人群和病例检测率对于提高早期疫情预测准确度至关重要.
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