Rational evaluation of various epidemic models based on the COVID-19 data of China.
Rational evaluation of various epidemic models based on the COVID-19 data of China.
复制标题
基于中国COVID-19数据合理评价各种流行病模型
DOI：
10.1016/j.epidem.2021.100501
复制
发表时间：
2021-12
期刊：
影响因子：
3.8
通讯作者：
Hong L
中科院分区：
文献类型：
--
作者：
Yang W;Zhang D;Peng L;Zhuge C;Hong L
基于赤池信息量准则、均方根误差和鲁棒系数，对7种经验函数、4种统计推断方法和5种动力学模型的预测能力进行了合理的评价。对于中国COVID-19疫情的爆发数据，我们发现在拐点之前，所有模型都无法做出可靠的预测。Logistic函数始终低估了最终的流行规模，而Gompertz函数在所有情况下都高估了。在统计推断方法中，序贯贝叶斯方法和时间依赖的再生数方法在流行后期更准确。而指数增长法关于拐点的从低估到高估的过渡性行为可能有助于构造更可靠的预测。与基于ODE的SIR、SEIR和SEIR-AHQ模型相比，SEIR-QD和SEIR-PO模型在研究COVID-19疫情方面总体表现出更好的性能，我们认为其成功可归因于模型复杂性和拟合精度之间的适当权衡。我们的发现不仅对COVID-19疫情的预测至关重要，而且可能适用于其他传染病。
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.
登录
查看更多内容
影响因子：
11.7
作者：
Chowell G;Sattenspiel L;Bansal S;Viboud C
通讯作者：
Viboud C
影响因子：
3.7
作者：
Tabataba FS;Chakraborty P;Ramakrishnan N;Venkatramanan S;Chen J;Lewis B;Marathe M
通讯作者：
Marathe M
DOI：
10.1080/03610927808827599
发表时间：
1978-01-01
期刊：
COMMUNICATIONS IN STATISTICS PART A-THEORY AND METHODS
影响因子：
--
作者：
SUGIURA, N
通讯作者：
SUGIURA, N
影响因子：
--
作者：
Roosa, Kimberlyn;Chowell, Gerardo
通讯作者：
Chowell, Gerardo
DOI：
10.1093/biostatistics/kxy057
发表时间：
2020-07-01
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Stocks T;Britton T;Höhle M
通讯作者：
Höhle M