Systematic biases in disease forecasting - The role of behavior change
Systematic biases in disease forecasting - The role of behavior change
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DOI：
10.1016/j.epidem.2019.02.004
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发表时间：
2019-06-01
期刊：
影响因子：
3.8
通讯作者：
Weitz, Joshua S.
中科院分区：
文献类型：
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作者：
Eksin, Ceyhun;Paarporn, Keith;Weitz, Joshua S.
在一个简单的易感染-感染-康复（SIR）模型中，感染病例增加的初始速度表明了疫情的长期轨迹。然而，在现实世界的疫情爆发期间，个人可能会改变他们的行为，并采取预防措施来降低感染风险。因此，最初的传播速度和最终病例数之间的关系可能变得脆弱。在这里，我们评估这一假设，通过比较从一个简单的SIR流行病模型与那些从修改后的SIR模型，其中个人减少接触作为当前或累积病例数的函数的动态。尽管两种模型的疾病传播的初始速度几乎相同，但具有行为变化的动力学表现出最终病例数显著减少。我们发现，这种差异在最终的大小预测取决于个人的行为变化。这些结果也提供了一个基本原理，将行为变化纳入迭代预测模型。因此，我们建议使用卡尔曼滤波器来更新模型，作为迭代预测的一部分。当地面实况爆发包括行为变化时，尽管重复观察，使用简单SIR模型的顺序预测表现不佳，而使用修改后的SIR模型的预测能够校正初始预测误差。这些发现突出了将行为变化纳入基线流行病和动态预测模型的价值。
In a simple susceptible-infected-recovered (SIR) model, the initial speed at which infected cases increase is indicative of the long-term trajectory of the outbreak. Yet during real-world outbreaks, individuals may modify their behavior and take preventative steps to reduce infection risk. As a consequence, the relationship between the initial rate of spread and the final case count may become tenuous. Here, we evaluate this hypothesis by comparing the dynamics arising from a simple SIR epidemic model with those from a modified SIR model in which individuals reduce contacts as a function of the current or cumulative number of cases. Dynamics with behavior change exhibit significantly reduced final case counts even though the initial speed of disease spread is nearly identical for both of the models. We show that this difference in final size projections depends critically in the behavior change of individuals. These results also provide a rationale for integrating behavior change into iterative forecast models. Hence, we propose to use a Kalman filter to update models with and without behavior change as part of iterative forecasts. When the ground truth outbreak includes behavior change, sequential predictions using a simple SIR model perform poorly despite repeated observations while predictions using the modified SIR model are able to correct for initial forecast errors. These findings highlight the value of incorporating behavior change into baseline epidemic and dynamic forecast models.