对美国COVID-19场景建模中心的评估,以在不确定性下为流行病响应提供信息
Emily Howerton1, Lucie Contamin2, Luke C Mullany3
1The Pennsylvania State University, University Park, PA, USA. ehowerton@psu.edu.
Nature communications
|November 20, 2023
概括
由于复杂的疾病动态,预测COVID-19等流行病具有挑战性. 美国COVID-19场景建模中心显示,预测可靠大约22周,集体模型的表现优于单个模型.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 由于固有的疾病系统动态,预测流行病轨迹是复杂的.
- 现实的长期预测需要明确定义的场景,具体说明关键驱动因素.
- 美国COVID-19场景建模中心 (SMH) 成立,提供SARS-CoV-2负担预测.
研究的目的:
- 评估美国COVID-19场景建模中心 (SMH) 预测的表现.
- 评估场景有效性和模型校准对预测准确性的影响.
- 与单个模型相比,确定集合建模方法的可靠性.
主要方法:
- 召集了多个建模团队,以生成国家和州级SARS-CoV-2负担预测.
- 分析了基于场景有效性和模型校准的投影性能.
- 利用线性意见库创建一个参与模型的集合.
- 组合模型性能与单个模型性能进行比较.
主要成果:
- 根据场景有效性和模型校准,SMH的投影性能差异很大.
- 场景平均保持了22周的准确性,然后因未预见的SARS-CoV-2变体而无效.
- 当场景假设是有效的时,整体模型始终优于单个模型.
- 预测区间覆盖率仍然接近目标水平.
结论:
- 美国COVID-19场景建模中心展示了合作中心在产生长期场景预测方面的价值.
- 整体建模为流行病预测提供了更可靠的方法,特别是在有效的场景假设下.
- 这些发现强调了需要适应性预测策略,以考虑疾病动态的演变和新变种的出现.
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