Modeling COVID-19 Confirmed Cases Using a Hybrid Model

Abstract

Purpose:The COVID-19 virus has caused numerous problems worldwide. Given the negative effects of COVID-19, this study aims to estimate accurate forecasts of the number of confirmed cases to help policymakers determine and make the right decisions.Design/methodology/approach: This paper uses a hybrid approach for forecasting the daily COVID-19 cases based on combining the Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Neural Network (NNAR) with a single hidden layer. To fit the linear pattern from the data, ARIMA models are used. Then, the NNAR models are used to capture the nonlinear pattern. The final prediction is obtained by adding up the two predictions.

On COVID-19 Vaccination in Nigeria: An Empirical Study

“…The emergence of acute upper respiratory tract infections in humans caused by the coronavirus, which have claimed many lives, has called for the urgent need to curb the spread, hence the need for vaccination against the pandemic. Kahn et al [2][3][4] discussed the negative impact of the coronavirus from 2002 to 2003 until the recent occurrence, which was first discovered in late 2019 in Wuhan, China. Various forms of coronavirus have been newly discovered, such as delta and omicron, mutations of the SARS virus.…”