Spatio-Temporal Analysis of the Spread COVID-19 in Saudi Arabia

• Main Authors: Almobarak, Arwa S., Almohammadi, Hanan R., Aboalnaser, Sara A., Syed, Liyakathunisa
• Format: Conference Proceeding
• Language: English
• Subjects: Analytical models; ARIMA model; coronavirus; COVID-19; Data models; k-mean; moran scatter plot; Prediction algorithms; Predictive models; spatial analysis; Strategic planning; SVM classifier; temporal analysis
• ISSN: 2161-1351
• DOI: 10.1109/DeSE51703.2020.9450770

A novel Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. In most cases, the COVID-19 virus spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes. Moreover, it specifically targets the patient's respiratory system. To date, there are no specific vaccines or treatments for COVID-19. However, many ongoing clinical trials are evaluating potential therapies. In this paper, we provide a spatial-temporal analysis of the COVID-19 spread in Saudi Arabia as a case study. Two data sets are used and processed, one supplied by the WHO organization and the second from the ministry of health of Saudi Arabia. This study presents a spatial and temporal analysis of the spread of Coronavirus disease (COVID-19) in Saudi Arabia. This kind of viral outbreaks requires early elucidation, understanding its details, clarifying the virus's classification, and its genetic origin for strategic planning, containment, and treatment. In our proposed approach, we use Scatter Plots, Moran Scatter Plots to locate the spread of (COVID-19) on the Saudi Arabia map for spatial analysis. Further, we forecast the spreading using ARIMA (Autoregressive Integrated Moving Average), it is a complex model to estimate regression models in python language. The proposed model shows incredible ability in representing the virus spread pattern with a small error margin of less than 11%.