A Retrospective Study of COVID-19 Based on A Dual-Stage Model

Tian Wu; Jiajun Song; Yiyang She; Yuxin Wen; Yiran Wang

doi:10.62051/ijphmr.v1n3.17

Authors

Tian Wu
Jiajun Song
Yiyang She
Yuxin Wen
Yiran Wang

DOI:

https://doi.org/10.62051/ijphmr.v1n3.17

Keywords:

SEIR model, Infectious disease dynamics, Quarantine measures, Public health policy

Abstract

This study proposed initial stage SEI₁I₂I₃RD and final stage SEQ I₁I₂I₃RD models to deeply analyze the transmission dynamics of COVID-19 at different stages and propose scientific prevention and control strategies. The initial stage SE I₁I₂I₃RD model divides infected individuals into mild, moderate, and severe cases during the early stage of the epidemic, effectively predicting the number of new infections and cumulative recoveries. The final stage SEQ I₁I₂I₃RD model builds on the initial stage SE I₁I₂I₃RD model by adding a quarantine compartment, which reflects the impact of nucleic acid testing and quarantine measures on epidemic control. By introducing dynamic parameters and nonlinear processing, the final stage SEQ I₁I₂I₃RD model significantly improves prediction accuracy. The study demonstrates that increasing the isolation rate, reducing the daily contact rate of infected persons, and improving the recovery rate of mildly infected persons are effective control strategies. Based on the model's predictions, policy recommendations such as enhancing public health education, strictly implementing isolation measures, restricting population movement, and optimizing medical resource allocation are proposed to effectively control the spread of the epidemic, reduce the burden on the healthcare system, and create favorable conditions for economic recovery.

References

Kermack, W. O. & McKendrick, A. G. Contributions to the mathematical theory of epidemics--I. 1927.

Kermack, W. O. & McKendrick, A. G. Contributions to the mathematical theory of epidemics--II. The problem of endemicity.1932.

Cooper, I., Mondal, A. & Antonopoulos, C. G. A SIR model assumption for the spread of COVID-19 in different communities.

Alenezi, M. N., Al-Anzi, F. S. & Alabdulrazzaq, H. Building a sensible SIR estimation model for COVID-19 outspread in Kuwait. Alexandria Engineering Journal 60, 3161-3175, doi:10.1016/j.aej.2021.01.025 (2021).

Aron Jl Fau - Schwartz, I. B. & Schwartz, I. B. Seasonality and period-doubling bifurcations in an epidemic model.

Xu, R., Wang, Z. & Zhang, F. Global stability and Hopf bifurcations of an SEIR epidemiological model with logistic growth and time delay. Applied Mathematics and Computation 269, 332-342, doi:10.1016/j.amc.2015.07.084 (2015).

Tang, B. et al. Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions. Journal of Clinical Medicine 9, doi:10.3390/jcm9020462 (2020).

Yang, Z. et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions.

Efimov, D. & Ushirobira, R. On an interval prediction of COVID-19 development based on a SEIR epidemic model. Annual Reviews in Control 51, 477-487, doi:10.1016/j.arcontrol.2021.01.006 (2021).

Chen, Z., Feng, L., Lay, H. A., Jr., Furati, K. & Khaliq, A. SEIR model with unreported infected population and dynamic parameters for the spread of COVID-19.

Wan, H., Cui, J.-A. & Yang, G.-J. Risk estimation and prediction of the transmission of coronavirus disease-2019 (COVID-19) in the mainland of China excluding Hubei province. Infectious Diseases of Poverty 9, doi:10.1186/s40249-020-00683-6 (2020).

Ala’raj, M., Majdalawieh, M. & Nizamuddin, N. Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections. Infectious Disease Modelling 6, 98-111, doi:10.1016/j.idm.2020.11.007 (2021).

Raslan, W. E. Fractional mathematical modeling for epidemic prediction of COVID-19 in Egypt. Ain Shams Engineering Journal 12, 3057-3062, doi:10.1016/j.asej.2020.10.027 (2021).

Singh, A. & Deolia, P. COVID-19 outbreak: a predictive mathematical study incorporating shedding effect. Journal of Applied Mathematics and Computing 69, 1239-1268, doi:10.1007/s12190-022-01792-1 (2022).

Msmali, A. H. et al. A Nonstandard Computational Investigation of SEIR Model with Fuzzy Transmission, Recovery and Death Rates. Computers, Materials & Continua 77, 2251-2269, doi:10.32604/cmc.2023.040266 (2023).

Liu, X. X., Fong, S. J., Dey, N., Crespo, R. A.-O. & Herrera-Viedma, E. A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak.

Wintachai, P. & Prathom, K. Stability analysis of SEIR model related to efficiency of vaccines for COVID-19 situation. Heliyon 7, doi:10.1016/j.heliyon.2021.e06812 (2021).

Foy, B. H. et al. Comparing COVID-19 vaccine allocation strategies in India: A mathematical modelling study. International Journal of Infectious Diseases 103, 431-438, doi:10.1016/j.ijid.2020.12.075 (2021).

Caetano, C. et al. Measuring the impact of COVID-19 vaccination and immunity waning: A modelling study for Portugal. Vaccine 40, 7115-7121, doi:10.1016/j.vaccine.2022.10.007 (2022).