Exploring Future Disease ‘X’ Outbreak Possibilities, Risk and Challenging Factors: A Comprehensive Analysis with Machine Learning Algorithms

Abstract

Background Pandemic refers to the widespread outbreak of a disease that transitions from an epidemic to a global scale, affecting multiple countries. Some of the most devastating diseases in human history, such as Cholera, Plague, smallpox, and influenza, have had a significant impact on populations worldwide. Smallpox alone has caused an estimated 300-500 million deaths over thousands of years.
Methods Using of artificial intelligence tools plays a crucial role in assessing risks and identifying areas that require immediate attention during pandemics. The objective of the research to explore potential future pandemic scenarios and their vulnerabilities. Healthcare professionals are urged to implement preventive measures to protect their communities and reduce human casualties. Artificial intelligence, machine learning, and deep learning algorithms can be valuable assets in forecasting future pandemic trends and assessing vulnerabilities. By adopting machine learning-based methodologies outlined in this research, healthcare operations can swiftly and effectively identify potential pandemic scenarios.
Results Linear regression is a statistical method used to predict the value of one variable based on another. It has an MSE of 567116.1297, indicating prediction error, and an R-squared value of 0.7779. The Random Forest algorithm, also with an MSE of 567116.1297 and an R-squared of 0.7779, shows similar prediction accuracy. Support Vector Machine (SVM) shares the same MSE and R-squared values as Random Forest, indicating comparable performance. Random Forest is noted to be better for predicting better result based on dataset trends.
Conclusions The emergence of Pandemic X, highlighted by advanced Machine Learning algorithms, raises significant concerns for humanity. Key factors affecting its timeline and impact include population density, climate, economic activity, healthcare resources, preventive measures, historical data, global travel, and the effectiveness of pandemic models. A comprehensive approach integrating these factors is vital for effective preparedness and response strategies in the future.