Volume 7, Issue 4 (Summer 2022)
Health in Emergencies and Disasters Quarterly 2022, 7(4): 193-204 |
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1- School of Safety Science and Emergency Management ,Wuhan University of Technology, China. , wangqian570719@163.com
2- School of Safety Science and Emergency Management ,Wuhan University of Technology, China.
2- School of Safety Science and Emergency Management ,Wuhan University of Technology, China.
Abstract: (1521 Views)
Background: Today, with the coronavirus disease 2019 (COVID-19) pandemic, the governments and international institutions are taking various approaches to control the infections. This study aims to propose an improved susceptible-exposed-infectious-removed (SEIR) model to predict the future trend of pandemic and assess the effectiveness of prevention and control strategies.
Materials and Methods: A new SEIR model was developed by adding two Q1 and Q2 isolation parameters (at home and hospital) named “SEIR-Q1Q2” to predict the future trend of pandemic, and assess the effectiveness of prevention and control strategies in Ezhou, Hubei province, China. The stimulation was conducted in Python by evaluating the effects of pandemic knowledge dissemination, medical supply, and both.
Results: due to the lack of knowledge of the disease risk, there was no strong tendency towards self-isolation, and the outbreak time coincided with the start of the Spring Festival, China’s major holiday, when many Chinse people are gathered and have close contact with each other. Therefore, it was not possible to disseminate the knowledge of pandemic, which let the virus kill many people.
Conclusion: The SEIR-Q1Q2 model can be used to predict the future trend of the COVID-19 pandemic by proposing the dissemination of the pandemic knowledge and increasing the supply of medical resources.
Materials and Methods: A new SEIR model was developed by adding two Q1 and Q2 isolation parameters (at home and hospital) named “SEIR-Q1Q2” to predict the future trend of pandemic, and assess the effectiveness of prevention and control strategies in Ezhou, Hubei province, China. The stimulation was conducted in Python by evaluating the effects of pandemic knowledge dissemination, medical supply, and both.
Results: due to the lack of knowledge of the disease risk, there was no strong tendency towards self-isolation, and the outbreak time coincided with the start of the Spring Festival, China’s major holiday, when many Chinse people are gathered and have close contact with each other. Therefore, it was not possible to disseminate the knowledge of pandemic, which let the virus kill many people.
Conclusion: The SEIR-Q1Q2 model can be used to predict the future trend of the COVID-19 pandemic by proposing the dissemination of the pandemic knowledge and increasing the supply of medical resources.
Type of Study: Research |
Subject:
General
Received: 2021/12/4 | Accepted: 2022/01/24 | Published: 2022/04/27
Received: 2021/12/4 | Accepted: 2022/01/24 | Published: 2022/04/27
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