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Library's collection Library's IT development CancelSince the dawn of the COVID-19 pandemic, predicting the trajectory of COVID-19 cases growth had been one of the most important topics among researchers. Harnessing the ability to predict the trajectory of COVID-19 will allow organizations, both government bodies and private companies, to adjust their policies and prevent additional casualties. The purpose of this study was to propose a novel way to predict the trajectory of COVID-19 cases by utilizing sigmoidal functions and ordinary least square regression. Moreover, linearization manipulation and Taylor approximations are also leveraged in this study.
Besides the need to predict the trajectory of COVID-19 cases, it is also a major interest for organizations to know whether a COVID-19 growth is in its increasing surge or in its decreasing surge. As a response to this need, the writer also proposed a new method to determine the inclination of the COVID-19 surge. A Comparison of the slope from the sigmoidal function is utilized in this study to determine the inclination. This study would be beneficial mainly for government bodies for health, particularly for enhancing its response in regard to future COVID-19 waves or even future pandemics.