论文标题
缺乏实践可识别性可能会妨碍COVID-19的流行模型的可靠预测
Lack of practical identifiability may hamper reliable predictions in COVID-19 epidemic models
论文作者
论文摘要
广泛采用隔室模型来描述和预测传染病的传播。此类模型的未知参数需要从数据中估算。此外，当某些模型变量在经验上不可访问时，例如Covid-19的无症状载体，必须作为模型的结果获得。在这里，我们介绍了一个框架，以量化数据中的不确定性如何影响给定模型的参数的确定和未测量变量的演变。我们说明该方法如何能够表征可识别性的不同制度，即使在隔间很少的模型中。最后，我们讨论在COVID-19的现实模型中缺乏可识别性如何阻止对流行动力学的可靠预测。
Compartmental models are widely adopted to describe and predict the spreading of infectious diseases. The unknown parameters of such models need to be estimated from the data. Furthermore, when some of the model variables are not empirically accessible, as in the case of asymptomatic carriers of COVID-19, they have to be obtained as an outcome of the model. Here, we introduce a framework to quantify how the uncertainty in the data impacts the determination of the parameters and the evolution of the unmeasured variables of a given model. We illustrate how the method is able to characterize different regimes of identifiability, even in models with few compartments. Finally, we discuss how the lack of identifiability in a realistic model for COVID-19 may prevent reliable forecasting of the epidemic dynamics.
