Mathematical modeling approach to predict COVID-19 infected people in Sri Lanka

I. H. K. Premarathna; H. M. Srivastava; Z. A. M. S. Juman; Ali AlArjani; Md Sharif Uddin; Shib Sankar Sana; I. H. K. Premarathna; H. M. Srivastava; Z. A. M. S. Juman; Ali AlArjani; Md Sharif Uddin; Shib Sankar Sana

doi:10.3934/math.2022260

AIMS Mathematics

2022, Volume 7, Issue 3: 4672-4699. doi: 10.3934/math.2022260

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Mathematical modeling approach to predict COVID-19 infected people in Sri Lanka

1.
Department of General Studies, Academic Wing, Sri Lanka Military Academy, Diyatalawa, Sri Lanka
2.
Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8W 3R4, Canada
3.
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
4.
Department of Mathematics and Informatics, Azerbaijan University, 71 Jeyhun Hajibeyli Street, AZ1007 Baku, Azerbaijan
5.
Section of Mathematics, International Telematic University Uninettuno, I-00186 Rome, Italy
6.
Department of Mathematics, Faculty of Science, University of Peradeniya, Peradeniya, 20400, Sri Lanka
7.
Department of Industrial Engineering, Prince Sattam bin Abdulaziz University, AlKharj, KSA, 16273
8.
Department of Mathematics, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
9.
Kishore Bharati Bhagini Nivedita College, Ramkrishna Sarani, Behala, Kolkata 700060, India

Received: 04 September 2021 Revised: 07 December 2021 Accepted: 08 December 2021 Published: 24 December 2021
MSC : 92B05

The novel corona virus (COVID-19) has badly affected many countries (more than 180 countries including China) in the world. More than 90% of the global COVID-19 cases are currently outside China. The large, unanticipated number of COVID-19 cases has interrupted the healthcare system in many countries and created shortages for bed space in hospitals. Consequently, better estimation of COVID-19 infected people in Sri Lanka is vital for government to take suitable action. This paper investigates predictions on both the number of the first and the second waves of COVID-19 cases in Sri Lanka. First, to estimate the number of first wave of future COVID-19 cases, we develop a stochastic forecasting model and present a solution technique for the model. Then, another solution method is proposed to the two existing models (SIR model and Logistic growth model) for the prediction on the second wave of COVID-19 cases. Finally, the proposed model and solution approaches are validated by secondary data obtained from the Epidemiology Unit, Ministry of Health, Sri Lanka. A comparative assessment on actual values of COVID-19 cases shows promising performance of our developed stochastic model and proposed solution techniques. So, our new finding would definitely be benefited to practitioners, academics and decision makers, especially the government of Sri Lanka that deals with such type of decision making.
- COVID-19,
- stochastic model,
- model validation method,
- SIR and Logistic growth models
Citation: I. H. K. Premarathna, H. M. Srivastava, Z. A. M. S. Juman, Ali AlArjani, Md Sharif Uddin, Shib Sankar Sana. Mathematical modeling approach to predict COVID-19 infected people in Sri Lanka[J]. AIMS Mathematics, 2022, 7(3): 4672-4699. doi: 10.3934/math.2022260

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Abstract

The novel corona virus (COVID-19) has badly affected many countries (more than 180 countries including China) in the world. More than 90% of the global COVID-19 cases are currently outside China. The large, unanticipated number of COVID-19 cases has interrupted the healthcare system in many countries and created shortages for bed space in hospitals. Consequently, better estimation of COVID-19 infected people in Sri Lanka is vital for government to take suitable action. This paper investigates predictions on both the number of the first and the second waves of COVID-19 cases in Sri Lanka. First, to estimate the number of first wave of future COVID-19 cases, we develop a stochastic forecasting model and present a solution technique for the model. Then, another solution method is proposed to the two existing models (SIR model and Logistic growth model) for the prediction on the second wave of COVID-19 cases. Finally, the proposed model and solution approaches are validated by secondary data obtained from the Epidemiology Unit, Ministry of Health, Sri Lanka. A comparative assessment on actual values of COVID-19 cases shows promising performance of our developed stochastic model and proposed solution techniques. So, our new finding would definitely be benefited to practitioners, academics and decision makers, especially the government of Sri Lanka that deals with such type of decision making.

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math-07-03-260-Appendix A.xlsx

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