Predictive Mathematical Modelling of the Total Number of COVID-19 Cases for Brazil

Authors

  • Garba Uba Department of Science Laboratory Technology, College of Science and Technology, Jigawa State Polytechnic, Dutse, PMB 7040, Nigeria.
  • H.M. Yakasai Department of Biochemistry, College of Basic Medical science, Bayero University, Kano, PMB 3001- Nigeria.
  • Abdussamad Abubakar Department of Microbiology, Faculty of Science, Bauchi State University Gadau, P. M. B. 67 Itas Gadau, Bauchi State.
  • Mohd Yunus Abd Shukor Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, D.E, Malaysia.

DOI:

https://doi.org/10.54987/jemat.v8i1.517

Keywords:

SARS-CoV-2, COVID-19, Total infection case, Morgan-Mercer-Flodin (MMF), Brazil

Abstract

In this paper, we present different growth models such as Von Bertalanffy, Baranyi-Roberts, Morgan-Mercer-Flodin (MMF), modified Richards, modified Gompertz, modified Logistics and Huang in fitting and analyzing the epidemic trend of COVID-19 in the form of total number of infection cases of SARS-CoV-2 in Brazil as of 15th of July 2020. The MMF model was found to be the best model with the highest adjusted R2 value with the lowest RMSE value. The Accuracy and Bias Factors values were close to unity (1.0). The parameters obtained from the MMF model include maximum growth rate (log) of 0.026               (95% CI from 0.024 to 0.028), curve constant (d) that affects the inflection point of 1.094 (95% CI from 1.024 to 1.165) and maximal total number of cases (ymax) of 66,527,316 (95% CI from 35,156,044 to 143,548,943). The MMF predicted that the total number of cases for Brazil on the coming 15th of August and 15th of September 2020 will be 2,993,850 (95% CI of 3,407,196 to 2,630,649) and 4,676,829 (95% CI of 5,553,936 to 3,938,240), respectively. The predictive ability of the model utilized in this study is a powerful tool for epidemiologist to monitor and assess the severity of COVID-19 in Brazil in months to come. However, as with any other model, these values need to be taken with caution due to the unpredictability of the COVID-19 situation locally and globally.

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Published

31.07.2020

How to Cite

Uba, G., Yakasai, H., Abubakar, A., & Shukor, M. Y. A. (2020). Predictive Mathematical Modelling of the Total Number of COVID-19 Cases for Brazil. Journal of Environmental Microbiology and Toxicology, 8(1), 16–20. https://doi.org/10.54987/jemat.v8i1.517

Issue

Vol. 8 No. 1 (2020)

Section

Articles

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