论文标题
基于中国共同数据的各种流行模型的合理评估
Rational evaluation of various epidemic models based on the COVID-19 data of China
论文作者
论文摘要
在本文中，基于Akaike信息标准，均方根误差和鲁棒性系数，对各种流行病模型/方法进行了合理评估，包括七个经验函数，四种统计推断方法和五个动态模型，以预测能力进行。关于中国Covid-19流行病的爆发数据，我们发现在拐点之前，所有模型都无法做出可靠的预测。逻辑函数始终低估了最终的流行大小，而Gompertz的功能在所有情况下都可以高估。对于统计推断方法，在流行病的后期，顺序贝叶斯和时间依赖性繁殖数的方法更准确。指数增长方法从低估到高估相对于拐点的过渡行为可能对构建更可靠的预测有用。与基于ODE的SIR，SEIR和SEIR-AHQ模型相比，SEIR-QD和SEIR-PO模型通常在研究Covid-19流行病方面表现出更好的表现，我们认为，我们认为其成功可以归因于模型复杂性和拟合准确性之间的适当权衡。我们的发现不仅对19日期的流行病的预测至关重要，而且还适用于其他传染病。
In this paper, based on the Akaike information criterion, root mean square error and robustness coefficient, a rational evaluation of various epidemic models/methods, including seven empirical functions, four statistical inference methods and five dynamical models, on their forecasting abilities is carried out. With respect to the outbreak data of COVID-19 epidemics in China, we find that before the inflection point, all models fail to make a reliable prediction. The Logistic function consistently underestimates the final epidemic size, while the Gompertz's function makes an overestimation in all cases. Towards statistical inference methods, the methods of sequential Bayesian and time-dependent reproduction number are more accurate at the late stage of an epidemic. And the transition-like behavior of exponential growth method from underestimation to overestimation with respect to the inflection point might be useful for constructing a more reliable forecast. Compared to ODE-based SIR, SEIR and SEIR-AHQ models, the SEIR-QD and SEIR-PO models generally show a better performance on studying the COVID-19 epidemics, whose success we believe could be attributed to a proper trade-off between model complexity and fitting accuracy. Our findings not only are crucial for the forecast of COVID-19 epidemics, but also may apply to other infectious diseases.
