Year: 2026
Type: article
Abstract: In recent years, the emergence and re-emergence of infectious diseases have posed significant threats toglobal public health. The COVID-19 pandemic, among other outbreaks, has underscored the urgent need forproactive surveillance systems capable of predicting outbreaks before they spiral out of control. Traditionaldisease surveillance methods are often limited by delayed reporting, manual data analysis, and a lack of realtimeinsights. This project aims to overcome these limitations by leveraging Artificial Intelligence (AI) todevelop a robust and scalable predictive analytics system for early detection and forecasting of diseaseoutbreaks.The proposed system integrates multiple data sources including historical health records, climatedata, population mobility patterns, and even social media signals to enhance prediction accuracy. Usingadvanced machine learning and deep learning algorithms such as Long Short-Term Memory (LSTM)networks and Random Forest classifiers, the system analyzes temporal and spatial patterns in the data. Byidentifying correlations between environmental factors and epidemiological trends, the AI model generatesearly warnings and hotspot forecasts, thereby aiding timely public health interventions. Data preprocessingplays a critical role in ensuring model accuracy, involving tasks like missing value handling, normalization,and feature engineering. A hybrid modelling approach is adopted, combining both time-series andclassification models to ensure adaptability across different diseases and geographical regions. The system isvalidated using real-world datasets from past outbreaks such as dengue, influenza, and COVID-19, withpromising results in both prediction accuracy and timeliness of alerts. This AI-driven framework also includesa real-time visualization dashboard to provide actionable insights to health authorities. By presentingpredictions in the form of maps, graphs, and trend lines, the dashboard facilitates swift decision-making andresource allocation. Integration with government and healthcare systems can further amplify the effectivenessof public health responses by enabling data-driven policies and crisis management strategies. In conclusion,this project demonstrates that AI can significantly enhance the speed, accuracy, and reliability of diseaseoutbreak predictions. It not only bridges the gap between data and decision-making but also provides ascalable solution adaptable to future health emergencies. With further development, this framework has thepotential to become a cornerstone of intelligent public health surveillance systems globally.
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7217690169