Table of Content

Open Access iconOpen Access

ARTICLE

crossmark

SEIHCRD Model for COVID-19 Spread Scenarios, Disease Predictions and Estimates the Basic Reproduction Number, Case Fatality Rate, Hospital, and ICU Beds Requirement

Avaneesh Singh*, Manish Kumar Bajpai

PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India

* Corresponding Author: Avaneesh Singh. Email: email

(This article belongs to the Special Issue: Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19)

Computer Modeling in Engineering & Sciences 2020, 125(3), 991-1031. https://doi.org/10.32604/cmes.2020.012503

Abstract

We have proposed a new mathematical method, the SEIHCRD model, which has an excellent potential to predict the incidence of COVID-19 diseases. Our proposed SEIHCRD model is an extension of the SEIR model. Three-compartments have added death, hospitalized, and critical, which improves the basic understanding of disease spread and results. We have studied COVID-19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. After estimating model parameters based on available clinical data, the model will propagate and forecast dynamic evolution. The model calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries’ age-category scenario. The model calculates two types of Case fatality rate one is CFR daily, and the other is total CFR. The proposed model estimates the approximate time when the disease is at its peak and the approximate time when death cases rarely occur and calculate how much hospital beds and ICU beds will be needed in the peak days of infection. The SEIHCRD model outperforms the classic ARX model and the ARIMA model. RMSE, MAPE, and R squared matrices are used to evaluate results and are graphically represented using Taylor and Target diagrams. The result shows RMSE has improved by 56%–74%, and MAPE has a 53%–89% improvement in prediction accuracy.

Keywords


Cite This Article

APA Style
Singh, A., Bajpai, M.K. (2020). SEIHCRD model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement. Computer Modeling in Engineering & Sciences, 125(3), 991-1031. https://doi.org/10.32604/cmes.2020.012503
Vancouver Style
Singh A, Bajpai MK. SEIHCRD model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement. Comput Model Eng Sci. 2020;125(3):991-1031 https://doi.org/10.32604/cmes.2020.012503
IEEE Style
A. Singh and M.K. Bajpai, "SEIHCRD Model for COVID-19 Spread Scenarios, Disease Predictions and Estimates the Basic Reproduction Number, Case Fatality Rate, Hospital, and ICU Beds Requirement," Comput. Model. Eng. Sci., vol. 125, no. 3, pp. 991-1031. 2020. https://doi.org/10.32604/cmes.2020.012503

Citations




cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 2755

    View

  • 2246

    Download

  • 0

    Like

Share Link