About: Rational evaluation of various epidemic models based on the COVID-19 data of China

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Rational evaluation of various epidemic models based on the COVID-19 data of China
Creator	Liu, Hong Peng, Liangrong Yang, Wuyue Zhang, Dongyan Zhuge, Changjing Pei-Yuan, Zhou
Source	MedRxiv
abstract	During the study of epidemics, one of the most significant and also challenging problems is to forecast the future trends, on which all follow-up actions of individuals and governments heavily rely. However, to pick out a reliable predictable model/method is far from simple, a rational evaluation of various possible choices is eagerly needed, especially under the severe threat of COVID-19 epidemics which is spreading worldwide right now. In this paper, based on the public COVID-19 data of seven provinces/cities in China reported during the spring of 2020, we make a systematical investigation on the forecast ability of eight widely used empirical functions, four statistical inference methods and five dynamical models widely used in the literature. We highlight the significance of a well balance between model complexity and accuracy, over-fitting and under-fitting, as well as model robustness and sensitivity. We further introduce the Akaike information criterion, root mean square errors and robustness index to quantify these three golden means and to evaluate various epidemic models/methods. Through extensive simulations, we find that the inflection point plays a crucial role in the choice of the size of dataset in forecasting. Before the inflection point, no model considered here could make a reliable prediction. We further notice the Logistic function steadily underestimate the final epidemic size, while the Gomertz's function makes an overestimation in all cases. Since the methods of sequential Bayesian and time-dependent reproduction number take the non-constant nature of the effective reproduction number with the progression of epidemics into consideration, we suggest to employ them especially in the late stage of an epidemic. 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. Towards the dynamical models based on ODEs, it is observed that the SEIR-QD and SEIR-PO models generally show a better performance than SIR, SEIR and SEIR-AHQ models on the COVID-19 epidemics, whose success could be attributed to the inclusion of self-protection and quarantine, and a proper trade-off between model complexity and fitting accuracy.
has issue date	2020-03-16(xsd:dateTime)
bibo:doi	10.1101/2020.03.12.20034595
has license	medrxiv
sha1sum (hex)	5f91066bc15728c9bdb5441363b2ff4671b674ba
schema:url	https://doi.org/10.1101/2020.03.12.20034595
resource representing a document's title	Rational evaluation of various epidemic models based on the COVID-19 data of China
resource representing a document's body	covid:5f91066bc15728c9bdb5441363b2ff4671b674ba#body_text
is schema:about of	named entity 'generally' named entity 'trade-off' named entity 'quarantine' named entity 'COVID-19' named entity 'TRADE' named entity 'QUARANTINE' named entity 'INCLUSION' named entity 'PROPER' named entity 'PROTECTION' named entity 'S-shaped curve' named entity 'linear function' named entity 'Confucianism' named entity 'training data set' named entity 'statistical methods' named entity 'initial conditions' named entity 'COVID-19' named entity 'medRxiv' named entity 'SIR' named entity 'data' named entity 'epidemic' named entity 'preprint' named entity 'SEIR' named entity 'SEIR' named entity 'quarantine' named entity 'SEIR' named entity 'China' named entity 'epidemic' named entity 'Shanghai' named entity 'inflection point' named entity 'medRxiv' named entity 'follow-up' named entity 'March 16, 2020' named entity 'medRxiv' named entity 'SEIR' named entity 'inflection point' named entity 'predictive model' named entity 'asymptotic stability' named entity 'COVID-19' named entity 'inflection point' named entity 'SEIR' named entity 'China' named entity 'Iran' named entity 'exponential growth' named entity 'under-fitting' named entity 'deterministic' named entity 'COVID-19' named entity 'China' named entity 'maximum likelihood method' named entity 'AICc' named entity 'Italy' named entity 'Boltzmann factor' named entity 'AICc' named entity 'under-fitting' named entity 'difference equations' named entity 'exponential functions' named entity 'peer review' named entity 'data set' named entity 'statistical inference' named entity 'medRxiv' named entity 'SEIR' named entity 'reproduction number' named entity 'epidemiology' named entity 'incomplete knowledge' named entity 'root mean square error' named entity 'AICc' named entity 'AIC' named entity 'decays exponentially' named entity 'COVID-19' named entity 'SEIR' named entity 'asymptomatic carriers' named entity 'ODE' named entity 'SEIR' named entity 'lower and upper bounds' named entity 'infectious disease' named entity 'variance' named entity 'goodness of fit' named entity 'March 16, 2020' named entity 'non-linear least squares' named entity 'medRxiv' named entity 'RMSE'

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