Manuscript Title:

PREDICTING FUTURE POSSIBLE COVID 19 OUTBREAKS USING MACHINE LEARNING

Author:

SHAWWAL RASHED, SHEERAZ AKRAM, SHAHARYAR RAFIQ, FAWAD NASIM, ARFAN JAFFAR, MUHAMMAD IMRAN TARIQ

DOI Number:

DOI:10.17605/OSF.IO/YP9FN

Published : 2023-02-10

About the author(s)

1. SHAWWAL RASHED - Lecturer Computer Science, Department of Software Engineering, Superior University, Lahore, Pakistan.
2. SHEERAZ AKRAM - Associate Professor Computer Science, Department of Software Engineering, Superior University, Lahore, Pakistan.
3. SHAHARYAR RAFIQ - Department of Software Engineering, Superior University, Lahore, Pakistan.
4. FAWAD NASIM - Assistant Professor Computer Science, Department of Computer Science, Superior University, Lahore, Pakistan.
5. ARFAN JAFFAR - Dean, Department of Computer Science and Information Technology, Superior University, Lahore, Pakistan.
6. MUHAMMAD IMRAN TARIQ - Assistant Professor Computer Science, Department of Computer Science, Superior University, Lahore, Pakistan.

Full Text : PDF

Abstract

By June 2020, almost 9 million confirmed verified Covid-19 cases had been confirmed, with above 468 thousand deaths. Contact with tainted objects can transmit the viruses and touch your mouth, eyes, or nose to an infected person. Since how much corona diseases are spreading quickly, it is hard to test because of time and cost factors. For a long time, ML has become reliable in medical fields. Usage of ML to estimate COVID-19 in patients will reduce the delays in the outcome of the clinical trial and develop a delay in providing proper medical treatment to the patients. The approach is based on Covid-19 dataset cases from WHO. The dataset includes data on each country's case, death, and recovery totals. The approach uses a Support Vector Machine (SVM) and polynomial regression (PR) to learn a model from the data. The model's outcomes demonstrate that the strategy can precisely forecast each country's case, mortality, and recovery rates. The study delivers an in-depth exploration of the global pandemic, its impact on various countries, and the future trends of the pandemic. This study highlights the need for an active response from citizens and governments of countries to global pandemic diseases. The study also provides recommendations about the steps that citizens and governments of countries need to take to reduce the effects of pandemic diseases.


Keywords

Machine Learning; Classification Techniques; COVID-19; Supervised Learning.