Neda Kaffash Charandabi - Faculty of Geomatic, Marand Technical Faculty, University of Tabriz, Tabriz, Iran
Amir Gholami - Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran

A major global public health issue that was called COVID-۱۹ emerged in China at the end of ۲۰۱۹. 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-۱۹, their clustering and pattern discovery are essential. Previous research concentrated mainly on the COVID-۱۹ 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-۱۹ 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 ۲۱. The results of hot/cold spots analysis for cases with different temporal neighborhood steps across countries showed that an average of ۳۸.۰۶%, ۷.۳%, ۱۰.۷۸% and ۱.۸۶% were identified for oscillating, sporadic, consecutive and new hot spots ,and ۴۲% 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-۱۹, Spatio-temporal Analysis, Epidemiology