基于个体的SEIR模型用于评价非药物干预的无模拟估计及其在爱荷华州COVID-19中的应用
Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in Iowa
摘要：
持续的COVID-19大流行压倒性地表明，需要准确评估实施新的或改变现有非药物干预措施的影响。由于无法通过传统的实验手段对这些在社会层面上实施的干预措施进行评估，公共卫生官员和其他决策者必须依靠统计和数学流行病学模型。非药物干预通常集中在人口成员之间的接触，但大多数流行病学模型依赖于均匀混合，这已多次被证明是一个不切实际的接触模式的代表。另一种方法是基于个体的模型（IBM），但这些方法通常是时间密集型的，并且实现起来计算成本高，需要高度的专业知识和计算资源。决策者往往需要在很短的时间内利用有限的资源了解可能的公共政策决定的影响。本文提出了一种估计算法的IBM设计，以评估非药物干预。通过利用递归关系，我们的方法可以快速计算预期的流行病学结果，即使是基于任何任意接触网络的大人群。我们利用我们的方法来评估放松目前的社交距离措施在美国爱荷华州，在不同的时间和不同程度的影响。\verb！R！补充材料中提供了我们方法的代码，从而允许其他人将我们的方法用于其他区域。
英文摘要：
The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need to accurately evaluate the effects of implementing new or altering existing nonpharmaceutical interventions. Since these interventions applied at the societal level cannot be evaluated through traditional experimental means, public health officials and other decision makers must rely on statistical and mathematical epidemiological models. Nonpharmaceutical interventions are typically focused on contacts between members of a population, and yet most epidemiological models rely on homogeneous mixing which has repeatedly been shown to be an unrealistic representation of contact patterns. An alternative approach is individual based models (IBMs), but these are often time intensive and computationally expensive to implement, requiring a high degree of expertise and computational resources. More often, decision makers need to know the effects of potential public policy decisions in a very short time window using limited resources. This paper presents an estimation algorithm for an IBM designed to evaluate nonpharmaceutical interventions. By utilizing recursive relationships, our method can quickly compute the expected epidemiological outcomes even for large populations based on any arbitrary contact network. We utilize our methods to evaluate the effects of relaxing current social distancing measures in Iowa, USA, at various times and to various degrees. \verb!R! code for our method is provided in the supplementary material, thereby allowing others to utilize our approach for other regions.
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