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美国COVID-19和流感场景建模中心:提供长期预测以指导政策

Sara L Loo¹, Emily Howerton², Lucie Contamin³

¹Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA.

|January 7, 2024

概括

COVID-19场景建模中心提供了关键的多个月预测,以指导流行病规划. 这项协作努力综合了建模数据,并为未来的公共卫生计划提供了蓝图.

科学领域:

流行病学流行病学
公共卫生公共卫生
计算生物学计算生物学

背景情况:

由于COVID-19的流行,需要协调,多模型的预测,以有效的公共卫生决策.
许多预测模型出现了,需要合成和运行来指导流行病应对.
高度的不确定性是流行病的特征,强调了对可靠预测的需求.

研究的目的:

描述COVID-19场景建模中心 (SMH) 的建立和运行.
详细说明多年来生成和传播COVID-19预测的过程.
为未来在公共卫生紧急情况中的合作建模努力提供蓝图.

主要方法:

召开和维护一个开放的,多年的协作建模中心.
产生17轮COVID-19场景和多个月的预测.
扩大中心,包括2022-23赛季的流感预测.

主要成果:

成功生成了美国COVID-19负担的多个月运营预测.
将结果传播给公共卫生专业人员和公众.
证明了协调建模用于指导流行病规划的实用性.

结论:

医疗卫生中心成功地综合了各种模拟努力,为公共卫生政策提供了信息.
确定了对公共卫生决策的关键影响.
学到的经验教训为未来的协作建模和政策接口提供了指导.

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