论文标题
嵌入式模型差异：Zika建模的案例研究
Embedded model discrepancy: A case study of Zika modeling
论文作者
论文摘要
流行病学系统的数学模型可以研究潜在疾病暴发的研究和预测。但是，常用模型通常是令人难以置信的复杂系统的高度简化表示。由于这些简化，随着时间的流逝，疾病的新病例或流行病的情况可能与可用数据不一致。在这种情况下，我们必须改善模型，尤其是如果我们计划根据可能影响人类健康和安全的决定做出决定，但是直接改善通常是我们无法实现的。在这项工作中，我们通过2016年巴西的寨卡病毒爆发的案例研究来探讨这个问题。我们提出了一个嵌入式差异操作员 - 对模型方程的修改，该方程需要适度有关该系统的信息，并通过所有相关数据进行校准。我们表明，新的富集模型表明，与实际数据的一致性大大增加了。此外，该方法足够通用，可以轻松地应用于流行病学中的许多其他数学模型。
Mathematical models of epidemiological systems enable investigation of and predictions about potential disease outbreaks. However, commonly used models are often highly simplified representations of incredibly complex systems. Because of these simplifications, the model output, of say new cases of a disease over time, or when an epidemic will occur, may be inconsistent with available data. In this case, we must improve the model, especially if we plan to make decisions based on it that could affect human health and safety, but direct improvements are often beyond our reach. In this work, we explore this problem through a case study of the Zika outbreak in Brazil in 2016. We propose an embedded discrepancy operator---a modification to the model equations that requires modest information about the system and is calibrated by all relevant data. We show that the new enriched model demonstrates greatly increased consistency with real data. Moreover, the method is general enough to easily apply to many other mathematical models in epidemiology.
