Mathematical modeling approach to predict COVID-19 infected people in Sri Lanka
| dc.contributor.author | Premarathna, I. H. K. | |
| dc.contributor.author | Srivastava, H.M. | |
| dc.contributor.author | Juman, Z. A. M. S. | |
| dc.contributor.author | AlArjani, Ali | |
| dc.contributor.author | Uddin, Md Sharif | |
| dc.contributor.author | Sana, Shib Sankar | |
| dc.date.accessioned | 2022-01-17T18:53:39Z | |
| dc.date.available | 2022-01-17T18:53:39Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022 | |
| dc.description | The authors would like to express their sincerest thanks to the editors and reviewers for their constructive suggestions and correction to enhance the clarity of the article. | en_US |
| dc.description.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. | en_US |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Faculty | en_US |
| dc.identifier.citation | Premarathna, I. H. K., Srivastava, H. M., Juman, Z. A. M. S., AlArjani, A., Uddin, M. S., & Sana, S. S. (2022). “Mathematical modeling approach to predict COVID-19 infected people in Sri Lanka.” AIMS Mathematics, 7(3), 4672-4699. https://doi.org/10.3934/math.2022260 | en_US |
| dc.identifier.uri | https://doi.org/10.3934/math.2022260 | |
| dc.identifier.uri | http://hdl.handle.net/1828/13709 | |
| dc.language.iso | en | en_US |
| dc.publisher | AIMS Mathematics | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | stochastic model | en_US |
| dc.subject | model validation method | en_US |
| dc.subject | SIR and Logistic growth models | en_US |
| dc.title | Mathematical modeling approach to predict COVID-19 infected people in Sri Lanka | en_US |
| dc.type | Article | en_US |