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A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak
Xian-Xian Liu1; Simon James Fong1,2; Nilanjan Dey3; Rubén González Crespo4; Enrique Herrera-Viedma5
2021-07
Source PublicationApplied Intelligence
ISSN0924-669X
Volume51Issue:7Pages:4162-4198
Abstract

Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the medical system. The latest pandemic of COVID-19 that hits the world death tolls and economy loss very hard, is more complex and contagious than its precedent diseases. The complexity comes mostly from the emergence of asymptomatic patients and relapse of the recovered patients which were not commonly seen during SARS outbreaks. These new characteristics pertaining to COVID-19 were only discovered lately, adding a level of uncertainty to the traditional SEIR models. The contribution of this paper is that for the COVID-19 epidemic, which is infectious in both the incubation period and the onset period, we use neural networks to learn from the actual data of the epidemic to obtain optimal parameters, thereby establishing a nonlinear, self-adaptive dynamic coefficient infectious disease prediction model. On the basis of prediction, we considered control measures and simulated the effects of different control measures and different strengths of the control measures. The epidemic control is predicted as a continuous change process, and the epidemic development and control are integrated to simulate and forecast. Decision-making departments make optimal choices. The improved model is applied to simulate the COVID-19 epidemic in the United States, and by comparing the prediction results with the traditional SEIR model, SEAIRD model and adaptive SEAIRD model, it is found that the adaptive SEAIRD model’s prediction results of the U.S. COVID-19 epidemic data are in good agreement with the actual epidemic curve. For example, from the prediction effect of these 3 different models on accumulative confirmed cases, in terms of goodness of fit, adaptive SEAIRD model (0.99997) ≈ SEAIRD model (0.98548) > Classical SEIR model (0.66837); in terms of error value: adaptive SEAIRD model (198.6563) < < SEAIRD model(4739.8577) < < Classical SEIR model (22,652.796); The objective of this contribution is mainly on extending the current spread prediction model. It incorporates extra compartments accounting for the new features of COVID-19, and fine-tunes the new model with neural network, in a bid of achieving a higher level of prediction accuracy. Based on the SEIR model of disease transmission, an adaptive model called SEAIRD with internal source and isolation intervention is proposed. It simulates the effects of the changing behaviour of the SARS-CoV-2 in U.S. Neural network is applied to achieve a better fit in SEAIRD. Unlike the SEIR model, the adaptive SEAIRD model embraces multi-group dynamics which lead to different evolutionary trends during the epidemic. Through the risk assessment indicators of the adaptive SEAIRD model, it is convenient to measure the severity of the epidemic situation for consideration of different preventive measures. Future scenarios are projected from the trends of various indicators by running the adaptive SEAIRD model.

KeywordNovel Coronavirus Covid-19 Asymptomatic Cases Disease Transmission Seir Seaird Severity Risk Assessment Enhanced Surveillance Interventions
DOI10.1007/s10489-020-01938-3
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000604086200001
PublisherSPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
Scopus ID2-s2.0-85098488154
Fulltext Access
Citation statistics
Cited Times [WOS]:14   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorRubén González Crespo
Affiliation1.Department of Computer and Information Science,University of Macau,Macau,SAR,Macao
2.DACC Laboratory,Zhuhai Institutes of Advanced Technology of the Chinese Academy of Sciences,Zhuhai,China
3.Department of Computer Science and Engineering,JIS University,Kolkata,India
4.Universidad Internacional de La Rioja,Logroño,Spain
5.University of Granada,Granada,Spain
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Xian-Xian Liu,Simon James Fong,Nilanjan Dey,et al. A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak[J]. Applied Intelligence,2021,51(7):4162-4198.
APA Xian-Xian Liu,Simon James Fong,Nilanjan Dey,Rubén González Crespo,&Enrique Herrera-Viedma.(2021).A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak.Applied Intelligence,51(7),4162-4198.
MLA Xian-Xian Liu,et al."A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak".Applied Intelligence 51.7(2021):4162-4198.
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