Sun, P orcid.org/0000-0001-8061-224X, Li, K orcid.org/0000-0001-6657-0522, Yang, Z et al. (1 more author) (2021) An SEIR Model for Assessment of COVID-19 Pandemic Situation. In: Recent Featured Applications of Artificial Intelligence Methods. LSMS 2020 and ICSEE 2020 Workshops. 2020 International Conference on Life System Modeling and Simulation & 2020 International Conference on Intelligent Computing for Sustainable Energy and Environment, 25 Oct 2020, Hangzhou, China. Springer Verlag , pp. 498-510. ISBN 978-981-33-6377-9
Abstract
The ongoing COVID-19 pandemic spread to the UK in early 2020 with the first few cases being identified in late January. A rapid increase in confirmed cases started in March, and the number of infected people is however unknown, largely due to the rather limited testing scale. A number of reports published so far reveal that the COVID-19 has long incubation period, high fatality ratio and non-specific symptoms, making this novel coronavirus far different from common seasonal influenza. In this note, we present a modified SEIR model which takes into account the latency effect and probability distribution of model states. Based on the proposed model, it was estimated in April 2020 that the actual total number of infected people by 1 April in the UK might have already exceeded 610,000. Average fatality rates under different assumptions at the beginning of April 2020 were also estimated. Our model also revealed that the R0R0 value was between 7.5–9 which is much larger than most of the previously reported values. The proposed model has a potential to be used for assessing future epidemic situations under different intervention strategies.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Singapore Pte Ltd. 2020. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | COVID-19; SEIR model; Coronavirus pandemic assessment |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Jan 2021 13:30 |
Last Modified: | 22 Mar 2022 01:52 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-981-33-6378-6_37 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:169521 |