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

Abstract

medRxiv preprintDuring 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.

Epidemiological Characteristics of COVID-19 Ongoing Epidemic in Iraq

“…During the study of epidemics, one of the most significant and challenging problem is to forecast the future trends, like how many individuals might be infected each day [3,4,5], when the epidemics stop spreading, what kinds of policies and actions have to be taken and how they influence the epidemics, and so forth [6,7,8]. After the outbreak of an epidemic, all actions of individuals and government heavily depend on our understandings on its future trend.…”

“…Since the accurate epidemic forecast is so critical, there are diverse methods reported in the literature to try to achieve this goal [16]. Among them, empirical functions, methods based on statistical inference and dynamical models (difference equations, ODEs and PDEs) are three major routines [5]. Empirical functions, especially those with explicit forms, are most popular ones.…”

“…In dynamical models, the basic reproduction number is transformed into reaction coefficients. Based on compartment assumptions on populations involved in dynamical modeling epidemics, it shows a great ability to correctly reproduce the basic features of the spreading process of infectious disease, to reveal the hidden dynamics, like the numbers of exposed cases and asymptomatic carriers which are hard to be learnt from usual epidemiology investigation, to forecast the future trends of epidemics, as well to evaluate the influence of diverse control policies and actions against the spreading of infectious diseases in quantity [5,17].…”

Panorama da fase inicial do crescimento dos números de casos e óbitos causados pela COVID-19 no Brasil

“…Segundo Yang et al (2020), existem várias funções não lineares apropriadas para a previsão de epidemias, sendo as mais adequadas: Hill, Logística, Gompertz e Richards, no entanto, existem outras funções: lineares, quadráticas, cúbicas e exponenciais, que apesar de úteis, podem não capturar adequadamente a curva típica em forma de S. Contudo, os autores do estudo supracitado deixam claro que ainda existem poucos estudos comparando modelos não lineares para ajustes de curvas para os casos de óbitos.…”

How high and long will the COVID-19 wave be? A data-driven approach to model and predict the COVID-19 epidemic and the required capacity for the German health system

“…CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) insights regarding the stage of the epidemic, allow the prediction of future trends of the outbreak and estimate its final size [7][8][9].…”