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Shoaib, M., Salahudin, H., Hammad, M., Ahmad, S., Khan, A.A., Khan, M.M., Baig, M.A.I., Ahmad, F. and Ullah, M.K. (2021) Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases. SN Computer Science, 2, Article No. 372.
https://doi.org/10.1007/s42979-021-00764-9

has been cited by the following article:

• TITLE: The Method for Optimum Estimation of COVID-19 Variant Type Virus Infection Status Analysis by the Multivariate Analysis Considering the Environmental Variability Impact in Japan

  AUTHORS: Eiji Toma, Yukinori Kobayashi

  KEYWORDS: COVID-19, Sequential SIR Model, Effective Reproduction Number, Multivariate Analysis Method, T-Method, Regression Analysis

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.10 No.2, February 23, 2022

  ABSTRACT: Currently, the estimated value of the effective reproduction number (ERN), which is an index for grasping the COVID-19 infection status, is used for important planning and evaluation of infection prevention measures. Since ERN in the Sequential SIR model fluctuates in multiple dimensions due to changes in the surrounding environment, it is difficult to set the appropriate accuracy of the uncertainty region of the estimated data. The challenge in this study is to build a mathematical model of infectious disease according to the characteristics and data characteristics of the infectious disease and select an appropriate estimation method. Highly accurate quantitative research that analyzes the validity of “how infectious diseases prevail” from an academic point of view is the key to prediction and estimation in appropriate infection situation analysis. In this study, we adopted a statistical multivariate analysis method (T method) that enables evaluation and prediction of important factors related to ERN estimation and analysis of phenomena that change in real time (time series analysis). It was clarified that it is possible to estimate with higher accuracy by applying the T method to the estimated value of ERN by the current SIR mathematical model.