ABSTRACT
This study utilizes the Logistic Growth Curve (LGC) based forecast model to assess the effectiveness of Stay At Home (SAH) Order on COVID-19 pandemic spread in California while making comparisons and visualizations for multiple countries. In comparing results, previous work relied on confirmed or death cases which not scientifically valid due to the differences of population sizes of each country. We presented several methods being used in the past and how we utilize percentages, normalization and derivatives to help our evaluation and comparisons of several countries using our model. Our approach compared the spread of the virus considering the growth rate and developed a quantitative measure that can help compare quantitatively between multiple states or countries. In our analysis, we showed evidence to suggest that the forecast results correspond to the progress and effectiveness of the SAH Order in flattening the curve, which is useful in controlling the spike in the number of active COVID-19 patients. © 2022, Innovative Information Science and Technology Research Group. All rights reserved.
ABSTRACT
This study presents a prediction model based on Logistic Growth Curve (LGC) to evaluate the effectiveness of Movement Control Order (MCO) on COVID-19 pandemic spread. The evaluation assesses and predicts the growth models. The estimated model is a forecast-based model that depends on partial data from the COVID-19 cases in Malaysia. The model is studied on the effectiveness of the three phases of MCO implemented in Malaysia, where the model perfectly fits with the R2 value 0.989. Evidence from this study suggests that results of the prediction model match with the progress and effectiveness of the MCO to flatten the curve, and thus is helpful to control the spike in number of active COVID-19 cases and spread of COVID-19 infection growth. © 2020 by the author(s). Published by Annals of Emerging Technologies in Computing (AETiC).