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- Title
SEIHCRD Model for COVID-19 Spread Scenarios, Disease Predictions and Estimates the Basic Reproduction Number, Case Fatality Rate, Hospital, and ICU Beds Requirement.
- Authors
Singh, Avaneesh; Bajpai, Manish Kumar
- 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 studiedCOVID-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 modelwill propagate and forecast dynamic evolution. Themodel calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries' age-category scenario. Themodel 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 ARXmodel and the ARIMA model.RMSE,MAPE, andRsquaredmatrices 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.
- Subjects
FORECASTING; COVID-19; BASIC reproduction number; HOSPITAL beds; DISEASE incidence; BEDS; INTENSIVE care units; PEBBLE bed reactors
- Publication
CMES-Computer Modeling in Engineering & Sciences, 2020, Vol 125, Issue 3, p991
- ISSN
1526-1492
- Publication type
Article
- DOI
10.32604/cmes.2020.012503