SEIRD Model for Forecasting Spread of Covid - 19

Raghunath Bhadade; Alwin Anuse; Rupali Kute; Mousami Munot

doi:10.17485/IJST/v15i41.651

Article

SEIRD Model for Forecasting Spread of Covid - 19

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i41.651

Year: 2022, Volume: 15, Issue: 41, Pages: 2162-2170

Original Article

SEIRD Model for Forecasting Spread of Covid - 19

Raghunath Bhadade¹, Alwin Anuse², Rupali Kute¹, Mousami Munot^3*

¹Assistant Professor, School of ECE, Dr. Vishwanath Karad MIT- WPU, Pune, Maharashtra, India
²Associate Professor, School of ECE, Dr. Vishwanath Karad MIT- WPU, Pune, Maharashtra, India
³Associate Professor, Department of E&TC Engg, SCTR’s Pune Institute of Computer Technology, Maharashtra, India

*Corresponding Author
Email: [email protected]

Received Date:21 March 2022, Accepted Date:22 August 2022, Published Date:07 November 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To develop a reliable mathematical model in order to predict the evolution of various epidemiological factors and parameters for COVID-19 across the globe. Methods: A novel dynamic Susceptible- Exposed-Infected- Recovered-Died (SEIRD) model is proposed in this research. The proposed, two-step approach assumes the infection rate which is dependent on time, to estimate the evolution of various variables of the model. In the first step, parameters like clinical and transmission are estimated, whereas in the second step, simulation of the model is done to predict the outbreak. Findings: Making use of this model, the total number of people who are likely to be afflicted by an infectious disease in a closed population over a period of time can be computed theoretically. Novelty: The proposed model results into low computational complexity since it is deterministic in nature. Secondly, SEIRD model equations are solved in frequency domain that converts the integraldifferential equations into simple algebraic equations. This further reduces the computational burden. Keywords: COVID19; Modelling; SEIRD; Clinical; Transmission; Parameters

References

Li H, Liu SM, Yu XH, Tang SL, Tanga CK. Coronavirus disease 2019 (COVID-19): current status and future perspectives. Int J Antimicrob Agents .. 2020;55(5):105951. Available from: https://doi.org/10.1016/j.ijantimicag.2020.105951
World Health Organization. Available from: https://www.who.int/health-topics/coronavirus#tab=tab_1
Chatterjee S, Sarkar A, Karmakar M, Chatterjee S, Paul R. SEIRD model to study the asymptomatic growth during COVID-19 pandemic in India. Indian Journal of Physics. 2021;95:2575–2587. Available from: https://doi.org/10.1007/s12648-020-01928-8
Mwalili S, Kimanthi M, Ojiambo V, Gathungu D, Mbogo RW. SEIR Model for COVID-19 Dynamics Incorporating the Environment and Social Distancing. BMC Res Notes. 2020;13(352). Available from: https://doi.org/10.1186/s13104-020-05192-1
Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons & Fractals. 2020;139:110057. Available from: https://doi.org/10.1016/j.chaos.2020.110057
Maherala’raj, Munirmajdalawieh N. Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections. Infectious Disease Modelling. 2021;6:98–111. Available from: https://doi.org/10.1016/j.idm.2020.11.007
Alanazi MMSA, Kamruzzaman M, Alruwaili N, Alshammari SA, Alqahtani A, Karime. Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care. Journal of Healthcare Engineering. 2020;8857346. Available from: https://doi.org/10.1155/2020/8857346
Kim Y, Cho N. A Simulation Study on Spread of Disease and Control Measures in Closed Population Using ABM. Computation. 10(1):2. Available from: https://doi.org/10.3390/computation10010002
Squazzoni F, Polhill JG, Edmonds B, Ahrweiler P, Antosz P, Scholz G, et al. Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action. Journal of Artificial Societies and Social Simulation . 2020;23(2):10. Available from: https://doi.org/10.18564/jasss.4298
Li XZZ, Gao S, Fu YKK, Martcheva M. Modeling and Research on an Immuno-Epidemiological Coupled System with Coinfection. Bulletin of Mathematical Biology. 2021;83(11):116. Available from: https://doi.org/10.1007/s11538-021-00946-9
Tuan NH, Tri VV, Baleanu D. Analysis of the fractional corona virus pandemic via deterministic modeling. Mathematical Methods in the Applied Sciences. 2021;44(1):1086–1102. Available from: https://doi.org/10.1002/mma.6814
Lin Q, Chiu AP, Zhao S, He D. Modeling the spread of Middle East respiratory syndrome coronavirus in Saudi Arabia. Statistical Methods in Medical Research. 2018;27(7):1968–1978. Available from: https://doi:10.1177/0962280217746442
Masum M, Masud MA, Adnan MI, Shahriar H, Kim S. Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management. Socio-Economic Planning Sciences. 2022;80:101249. Available from: https://doi.org/10.1016/j.seps.2022.101249
Chen Y, Li N, Lourenço J, Wang L, Cazelles B, Dong L, et al. Measuring the effects of COVID-19-related disruption on dengue transmission in southeast Asia and Latin America: a statistical modelling study. The Lancet Infectious Diseases. 2022;22(5):657–667. Available from: https://doi.org/10.1016/S1473-3099(22)00025-1
Pichugin YA, Malafeyev OA, Zaitseva IV, Shulga AA, Kolesov DN. Forecast of Coronavirus Distribution in a Number of Countries of Europe and Asia: Dynamic and Stochastic Approach. IOP Conference Series: Earth and Environmental Science. 2021;666(3):032021. Available from: https://doi.org/10.1088/1755-1315/666/3/032021
Bu SJ, Cho SB. Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption. Energies. 2020;13(18):4722. Available from: https://doi.org/10.3390/en13184722
Harvey A, Kattuman P. Time Series Models Based on Growth Curves with Applications to Forecasting Coronavirus. Harvard Data Science Review. 2020;(S1). Available from: https://doi.org/10.1162/99608f92.828f40de
Asmelash Y, G, Asmelash D. Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box- Jenkins Modeling Procedure”. International Journal of General Medicine. 2021;14:1485–1498.
Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases. 2020;20(5):533–534. Available from: https://doi.org/10.1016/S1473-3099(20)30120-1
JHU, Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. n.d. Johns Hopkins Coronavirus Resource Center (CRC) Available from: https://coronavirus.jhu.edu/map.html
Korolev I. Identification and estimation of the SEIRD epidemic model for COVID-19. Journal of Econometrics. 2021;220(1):63–85. Available from: https://doi.org/10.1016/j.jeconom.2020.07.038
Liu L, Moon HR, Schorfheide F. Panel forecasts of country-level Covid-19 infections. Journal of Econometrics. 2021;220(1):2–22. Available from: https://doi.org/10.1016/j.jeconom.2020.08.010

Copyright

© 2022 Bhadade et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)