Thesis Abstract

Abstract
The rapid advancement of Artificial Intelligence (AI) technologies has provided innovative solutions to various sectors, including public health. This thesis explores the utilization of AI for predicting disease outbreaks in public health. The aim of this study is to develop a predictive model that leverages AI algorithms to forecast disease outbreaks, enabling early detection and proactive intervention strategies. The research methodology involves a comprehensive literature review to understand the current landscape of disease prediction, AI applications in public health, and existing predictive models. Subsequently, a dataset comprising historical disease outbreak data, environmental factors, and population demographics will be analyzed to train and validate the AI model. The findings of this study will be discussed in detail, highlighting the effectiveness and accuracy of the AI predictive model in forecasting disease outbreaks. The limitations of the study, such as data availability and model generalizability, will also be addressed. Moreover, the practical implications of implementing AI-based disease prediction systems in public health settings will be examined. The significance of this research lies in its potential to revolutionize disease surveillance and response mechanisms, ultimately reducing the impact of outbreaks on public health. By harnessing the power of AI for predictive analytics, public health authorities can proactively allocate resources, implement targeted interventions, and mitigate the spread of infectious diseases. In conclusion, this thesis underscores the importance of integrating AI technologies into public health practices for enhancing disease prediction capabilities. The findings of this study contribute to the growing body of knowledge on AI applications in public health and pave the way for future research and implementation of AI-driven disease surveillance systems.

Thesis Overview