使用COVID-19数据对行为流行病模型的比较评估.
Nicolò Gozzi1,2, Nicola Perra2,3,4, Alessandro Vespignani1,2
1Institute for Scientific Interchange Foundation, Turin 10126, Italy.
概括
了解人类行为如何影响传染病的传播是复杂的. 这项研究比较了三个模型,没有找到单一的最佳方法,性能因疾病传播建模的数据和指标而异.
科学领域:
- 计算流行病学计算流行病学
- 传染病的数学建模 传染病的数学建模
- 行为科学和公共卫生
背景情况:
- 描述人类行为和传染病传播之间的反循环是计算流行病学中的一个重大挑战.
- 现有的行为流行病模型往往缺乏现实世界的数据校准和跨模型性能评估,以进行回顾性分析和预测.
研究的目的:
- 系统地比较三种机械行为流行病模型的性能.
- 在COVID-19的第一波期间,评估九个地理区域的模型性能和两个建模任务.
- 为将行为变化纳入流行病建模和预测提供指导.
主要方法:
- 三种机械行为流行病模型的比较:数据驱动行为反模型 (利用移动数据),分析行为反模型 (明确的行为部分) 和分析行为反模型 (非线性感染力).
- 在COVID-19的第一波期间,在九个地理区域进行系统性绩效评估和两个建模任务.
- 利用各种指标进行回顾性分析和样本外预测.
主要成果:
- 没有确定单一的最佳模型;性能因数据可用性,数据质量和所选择的性能指标而异.
- 数据驱动的行为反模型包含了大量的实时行为信息.
- 分析分支行为反模型经常在回顾性拟合和样本外预测中显示出优异或同等的性能.
结论:
- 行为流行病模型的选择影响了准确性和预测能力.
- 模型性能取决于数据质量,可用性和使用的特定评估指标.
- 未来的流行病建模应该仔细考虑和验证行为动态的整合.
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