Evaluating the Impact of Social Distancing on COVID-19 Spread in Vietnam by using Logistic Growth Curve Model

Abstract

The regular increase in COVID-19 cases and deaths has resulted in a worldwide lockdown, quarantine and some restrictions. Due to the lack of a COVID-19 vaccine, it is critical for developing and least developed countries like Vietnam to investigate the efficacy of non-pharmaceutical treatments like social distance or national lockdown in preventing COVID-19 transmission. To address this need, the goal of this study was to develop a clear and reliable model for assessing the impact of social distancing on the spread of coronavirus in Vietnam. For the case study, the Logistic Growth Curve (LGC) model, also known as the Sigmoid model, was chosen to fit COVID-19 infection data from January 23, 2020 to April 30, 2020 in Vietnam. To determine the optimal set of LGC model parameters, we used the gradient descent technique. We were pleasantly surprised to discover that the LGC model accurately predicted COVID-19 community transmission cases over this time period, with very high correlation coefficient value r = 0.993. The results of this study imply that using social distancing technique to flatten the curve of coronavirus disease infections will help minimize the surge in active COVID-19 cases and the spread of COVID-19 infections. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.