论文标题
COVID-19的模拟流行进化：隔室模型是否真的可以预测？
Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?
论文作者
论文摘要
模拟严重急性呼吸综合症冠状病毒2（SARS-COV-2）流行病的计算模型对于支持当局设计医疗保健政策和封锁措施以遏制其对公共卫生和经济的影响非常有用。在意大利，设计的预测主要是基于纯数据驱动的方法，它通过对意大利民政中心收集的流行病进化的开放数据进行拟合和推断。在这方面，从对人口隔室之间非线性相互作用的描述开始的流行病学模型将是一种理解和预测集体紧急响应的方法。目前的贡献解决了一个基本问题，是否适当地充满无症状和死亡的个体隔室的流行病学模型是否能够就流行性进化提供可靠的预测。为此，提出了一种基于粒子群优化（PSO）的机器学习方法，以根据意大利伦巴第（Lombardy）作为案例研究，根据渐进式增加规模的训练集自动识别模型参数。对预测中散点的分析表明，模型预测对用于训练的数据集的大小非常敏感，并且仍然需要进一步的数据来实现收敛性（因此可靠）预测。
Computational models for the simulation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic evolution would be extremely useful to support authorities in designing healthcare policies and lockdown measures to contain its impact on public health and economy. In Italy, the devised forecasts have been mostly based on a pure data-driven approach, by fitting and extrapolating open data on the epidemic evolution collected by the Italian Civil Protection Center. In this respect, SIR epidemiological models, which start from the description of the nonlinear interactions between population compartments, would be a much more desirable approach to understand and predict the collective emergent response. The present contribution addresses the fundamental question whether a SIR epidemiological model, suitably enriched with asymptomatic and dead individual compartments, could be able to provide reliable predictions on the epidemic evolution. To this aim, a machine learning approach based on particle swarm optimization (PSO) is proposed to automatically identify the model parameters based on a training set of data of progressive increasing size, considering Lombardy in Italy as a case study. The analysis of the scatter in the forecasts shows that model predictions are quite sensitive to the size of the dataset used for training, and that further data are still required to achieve convergent -- and therefore reliable -- predictions.
