Farzane Tajidini - Tabarestan University of Chalus, Chalus, Iran
Raziye Mehri - ۲ Deputy of Research and Technology, Ardabil University of Medical Sciences, Ardabil, Iran ۳ Department of Community Medicine, Faculty of Medicine, Ardabil University of Medical Science, Ardabil, Iran

This study examines the applications and Deep Learning (ML) algorithms utilized in the COVID-۱۹ investigation and other contexts. Researchers and authorities have focused more on basic statistical and epidemiological methodology than the conventional methods for COVID-۱۹ worldwide epidemic prediction. One of the main obstacles to stopping the development of COVID-۱۹ is the inadequate lack of medical tests for detecting and finding a cure. To address this issue, a few statistically based enhancements are being reinforced, leading to a partial resolution to a specific degree. ML has pushed for a wide range of intelligence-based strategies, methods, and tools to address the medical business's problems. This work has examined how innovative structures like machine learning may be used to manage COVID-۱۹-related epidemic challenges. This study's primary objective is to Analyze the effects of the COVID-۱۹ data type.

_ Deep learning, Covid-۱۹, Epidemiological, Diagnosis