Epidemic Outbreak Prediction Using Artificial Intelligence

Adhikari, Nimai Chand Das; Alka, Arpana; Kurva, Vamshi Kumar; Suhas, S; Nayak, Hitesh; Rishav, Kumar; Nayak, Ashish Kumar; Nayak, Sankalp Kumar; Shaj, Vaisakh; Karthikeyan, .

doi:10.5121/ijcsit.2018.10405

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…N. C. D. Adhikari et al [10] used twitter for data for a specific country to create a locational network. These data were then integrated with algorithms to detect the outbreak of any diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Techniques for Predicting the Outbreak of Covid19 Cases

Santra¹,

Dutta²

2022

IJISA

View full text Add to dashboard Cite

At present, the whole world is experiencing a huge disturbance in social, economic, and political levels which may mostly attributed to sudden outbreak of Covid-19. The World Health Organization (WHO) declared it as Public Health crisis and global pandemic. Researchers across the globe have already proposed different outbreak models to impose various control measures fight against the novel corona virus. In order to overcome various challenges for the prediction of Covid-19 outbreaks, different mathematical and statistical approaches have been recommended by the researchers. The approaches used machine learning and deep learning based techniques which are capable of prediction of hidden patterns from large and complex datasets. The purpose of the present paper is to study different machine learning and deep learning based techniques used to identify and predict the pattern and performs some comparative analysis on the techniques. This paper contains a detailed summary of 40 paper based on this issue along with the use of method they applied to obtain the purpose. After the review it has been found that no model is fully capable of predicting it with accuracy. So, a hybrid model with better training should be employed for better result. This paper also studies different performance measures that researchers have used to show the efficiency of their proposed model.

show abstract

Section: Literature Reviewmentioning

confidence: 99%