Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence

Abstract

Forecasting disease outbreaks in real-time using time-series data can help for the planning of public health interventions. We used a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), World Health Organization (WHO), and the Centers for Disease Control and Prevention (CDC) to predict upcoming records before the WHO made an official declaration. Our study, conducted on the time series data available from 22 January till 10 March 2020, revealed that COVID-19 was spreading at an alarming rate and progressing towards a pandemic. The initial insight that confirmed COVID-19 cases were increasing was because these received the highest number of effects for our selected dataset from 22 January to 10 March 2020, i.e., 126,344 (64%). The recovered cases were 68289 (34%), and the death rate was around 2%. Moreover, we classified the tweets from 22 January to 15 April 2020 into positive and negative sentiments to identify the emotions (stress or relaxed) posted by Twitter users related to the COVID-19 pandemic. Our analysis identified that tweets mostly conveyed a negative sentiment with a high frequency of words for #coronavirus and #lockdown amid COVID-19. However, these anxiety tweets are an alarm for healthcare authorities to devise plans accordingly.

Systemic analysis of a manufacturing process based on a small scale bakery

“…Nonetheless, this concept was previously applied especially in technical sciences to solve problems. For instance, vector methods were applied in: nanostructure analysis (Zhao et al 2021 ), forecasting in the energy sector (Yousaf et al 2021 ; Aslam et al 2021 ], e-business modelling (Sun et al 2021 ), machine learning (Shahpouri et al 2021 ), or issues related to the COVID-19 pandemic (Alghazzawi et al 2021 ; Scuttari et al 2021 ; Ting et al 2020 ).…”