Remittances Review

ISSN:2059-6588 | e-ISSN: 2059-6596

ISSN:2059-6588 | e-ISSN: 2059-6596

Efficient Mechanism for the Prediction and Analysis of Disease Outbreak – Study based on Urban Area of Pakistan

Muhammad Zulqarnain Siddiqui, M Zamin Ali Khan ,Khalid Bin Muhammad, Muhammad Asghar Khan, Asim Iftikhar, Javeria Barkat
Disease Mapping, Predictive Modeling, Infectious Diseases, Data Analysis. Healthcare Management ,


This research project aims to revolutionize the availability of disease information by developing a state-of-the-art mobile application. The main goal is to provide users with predictive information about disease occurrence in certain areas. Using advanced data analysis techniques and machine learning algorithms, the application aims to enable people to proactively predict future diseases, facilitating informed decision making and proactive measures for personal health. The initiative goes beyond mapping and forecasting to emphasize proactive actions and community participation. An important part is the development of educational resources and community-based campaigns to increase awareness of disease prevention, symptom recognition and access to health services. The goal of this collaborative effort is to empower communities to take proactive steps to prevent disease and maintain health. At the heart of the mobile application is a comprehensive mapping function that visually presents diseases and trends in different parts of the city. Using various data sources such as historical records, user-generated reports, the application undergoes careful data processing and analysis. These processes reveal valuable patterns, paving the way for the development of accurate machine learning models to predict disease in specific regions. This predictive capability not only benefits individuals by helping them take precautions, but also helps public health agencies and healthcare providers optimize resource allocation and target interventions.  User experience continues to be critical to application and development. The user interface prioritizes intuitiveness, user-friendliness and aesthetic appeal. Users can interact with the map, explore different areas of the city and use filter options to focus on specific diseases. High-risk areas and related health advisories, informing users and enabling rapid action.