论文标题
评估COVID-19从简化的SIR模型中的住院预测
Assessment of COVID-19 hospitalization forecasts from a simplified SIR model
论文作者
论文摘要
我们提出了SH模型，这是著名的传染病sir隔间模型的简化版本。有了优化的参数和初始条件，这种时间不变的两参数二维模型能够以高精度在几个月内适合Covid-19的住院数据（例如，在2020-03-15至2020-07-15期间，比利时的根相对平方误差低于比利时的10％）。此外，我们观察到，当模型在比利时第一个住院峰附近进行适当的三周期间进行训练时，它可以预测随后的两个月，平均绝对百分比误差（MAPE）低于4％。我们重复了每个法国部门的实验，发现了14个MAPE低于20％的实验。但是，当模型在增加阶段进行训练时，它在预测随后的演变方面的成功率较小。
We propose the SH model, a simplified version of the well-known SIR compartmental model of infectious diseases. With optimized parameters and initial conditions, this time-invariant two-parameter two-dimensional model is able to fit COVID-19 hospitalization data over several months with high accuracy (e.g., the root relative squared error is below 10% for Belgium over the period from 2020-03-15 to 2020-07-15). Moreover, we observed that, when the model is trained on a suitable three-week period around the first hospitalization peak for Belgium, it forecasts the subsequent two months with mean absolute percentage error (MAPE) under 4%. We repeated the experiment for each French department and found 14 of them where the MAPE was below 20%. However, when the model is trained in the increase phase, it is less successful at forecasting the subsequent evolution.
