Author: Saeed, F.; Paul, A.; Ahmed, M. J.
Title: Forecasting COVID-19 cases using multiple statistical models Cord-id: 98tmew8p Document date: 2020_1_1
ID: 98tmew8p
Snippet: An epidemic of respirational sickness triggered by a different coronavirus. Throughout the duration of an outbreak when the individual-to-individual transmission is traditional and testified subjects of coronavirus sickness 'COVID-19' are intensifying globally. Predicting is of extreme significance for medic-care devising and regulation the disease with the inadequate property. For the sake of prediction, in this paper, we have used the arithmetic equations with different growth rates along with
Document: An epidemic of respirational sickness triggered by a different coronavirus. Throughout the duration of an outbreak when the individual-to-individual transmission is traditional and testified subjects of coronavirus sickness 'COVID-19' are intensifying globally. Predicting is of extreme significance for medic-care devising and regulation the disease with the inadequate property. For the sake of prediction, in this paper, we have used the arithmetic equations with different growth rates along with Linear regression and the Autoregressive Integrated Moving Average model (ARIMA). The used methods for calculating the growth rates are different in every four cases. For ARIMA, the constraints are predicted by ACF and PACF correlogram. Simulated results show a better performance for each model. © 2020 IEEE.
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