使用集成神经网络识别修正SIRD流行病模型参数的反问题
Inverse problem for parameters identification in a modified SIRD epidemic model using ensemble neural networks
AI总结：
本文提出用集成神经网络从7天数据中识别修正SIRD模型参数，以应对参数短期变化，并在罗马尼亚等国COVID-19死亡人数预测中验证了有效性。
摘要：
在本文中，我们提出了一种SIRD模型的参数识别方法，该模型是经典SIR模型的扩展，将死亡者作为一个单独类别加以考虑。此外，我们的模型包含一个参数，即真实感染总人数与官方统计中记录的感染人数之间的比值。由于诸多因素，如政府决策、多种变异株的流行、学校的开放与关闭，模型参数在长时间内保持不变的典型假设并不现实。因此，我们的目标是创建一种适用于短时间段的方法。在这一范围内，我们依靠此前7天的数据进行估计，然后使用识别出的参数进行预测。为了完成参数估计，我们提出使用一组神经网络集成的平均值。每个神经网络都基于一个数据库构建，该数据库通过以随机参数求解7天SIRD模型而得到。通过这种方式，网络从SIRD模型的解中学习参数。最后，我们使用该集成从罗马尼亚Covid19的真实数据中获取参数估计值，然后针对不同时间段（从10天到45天）展示死亡人数的预测。主要目标是将该方法应用于分析COVID-19在罗马尼亚的演变，但该方法也在匈牙利、捷克共和国和波兰等其他国家的数据上进行了示例验证，并取得了类似结果。这些结果由一个定理提供支撑，该定理保证我们可以从报告的数据中恢复模型参数。我们相信，这一方法可以作为一种通用工具，用于处理传染病的短期预测或其他仓室模型。
英文摘要：
In this paper, we propose a parameter identification methodology of the SIRD model, an extension of the classical SIR model, that considers the deceased as a separate category. In addition, our model includes one parameter which is the ratio between the real total number of infected and the number of infected that were documented in the official statistics. Due to many factors, like governmental decisions, several variants circulating, opening and closing of schools, the typical assumption that the parameters of the model stay constant for long periods of time is not realistic. Thus our objective is to create a method which works for short periods of time. In this scope, we approach the estimation relying on the previous 7 days of data and then use the identified parameters to make predictions. To perform the estimation of the parameters we propose the average of an ensemble of neural networks. Each neural network is constructed based on a database built by solving the SIRD for 7 days, with random parameters. In this way, the networks learn the parameters from the solution of the SIRD model. Lastly we use the ensemble to get estimates of the parameters from the real data of Covid19 in Romania and then we illustrate the predictions for different periods of time, from 10 up to 45 days, for the number of deaths. The main goal was to apply this approach on the analysis of COVID-19 evolution in Romania, but this was also exemplified on other countries like Hungary, Czech Republic and Poland with similar results. The results are backed by a theorem which guarantees that we can recover the parameters of the model from the reported data. We believe this methodology can be used as a general tool for dealing with short term predictions of infectious diseases or in other compartmental models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
