COVID-19 data analysis and Spatio-temporal hotpot identification

A major global public health issue that was called COVID-19 emerged in China at the end of 2019. The disease has caused many life-threatening physicals, emotional and financial problems for all people in the world. With the increasing number of cases of COVID-19, their clustering and pattern discovery are essential. Previous research concentrated mainly on the COVID-19 spatial, statistical, or temporal analysis. This research uses a Spatio-temporal analysis method that integrates time-space cube analysis, spatial autocorrelation analysis, and emerging hot-spot analysis to investigate COVID-19 Case and Death data. In this paper, according to the Spatial and Spatio-temporal analysis, hot/cold spots were identified based on data until March 21. The results of hot/cold spots analysis for cases with different temporal neighborhood steps across countries showed that an average of 38.06%, 7.3%, 10.78% and 1.86% were identified for oscillating, sporadic, consecutive and new hot spots ,and 42% for cold spots, respectively. The results confirm a global crisis that requires serious prevention, hand hygiene, self-quarantine ,and social distancing until vaccines will be discovered.

COVID-19 data analysis and Spatio-temporal hotpot identification Keywords:

COVID-19 , Spatio-temporal Analysis , Epidemiology