What is it about?
This book chapter presents a new system called an Epidemic Management System (EMS) to address the challenges of pandemics like COVID-19. The authors argue that existing systems are not enough because they don't fully integrate decision support systems or connect with broader resource management tools. This new system aims to solve this problem. It uses data from various sources, including mobile tracking apps, public health databases, and even information about medical suppliers. The core of the system is a decision support engine powered by two key technologies: ● Epidemic Modeling: This uses mathematical formulas to simulate how a disease spreads, helping to predict future outbreaks and evaluate the effectiveness of different interventions. ● Machine Learning: This allows the system to learn from the massive amounts of data being collected, identifying patterns and insights that humans might miss. This helps with tasks like predicting which hospitals will be overwhelmed or identifying high-risk areas. To ensure privacy, the system proposes using blockchain technology. This allows for secure and transparent data sharing while giving individuals control over their personal information.
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Why is it important?
This chapter is important because it presents a new approach to managing pandemics. By combining different technologies and data sources, this system has the potential to: ● Make better decisions faster: This is crucial in rapidly evolving situations like a pandemic, where delays can cost lives. ● Optimize resource allocation: By predicting shortages and identifying high-need areas, the system can help direct medical supplies and personnel where they're needed most. ● Improve preparedness: By analyzing past outbreaks, the system can help identify vulnerabilities and develop more effective response plans for future pandemics.
Perspectives
- Practical Application: The system's design considers real-world applicability, providing a practical tool for public health professionals to make informed decisions and manage resources effectively during health crises. - Technological Integration: This chapter highlights the innovative use of a Multi-Platform Interoperable Scalable Architecture (MPISA) model, which allows for the seamless integration of various platforms, addressing scalability and interoperability challenges. - Privacy and Security: It emphasizes the use of decentralized identity and zero-knowledge proof mechanisms to protect users' data, ensuring that individuals control their own information. - Further development need: The authors acknowledge that the proposed system is still under development. For example, they note that the privacy-enhancing technologies, while promising, are not yet fully mature. Further research is needed to improve the accuracy of the models and ensure the system can adapt to different scenarios. - Other applicable areas: While the paper focuses on COVID-19, the system has broader applications for managing other infectious diseases and public health emergencies.
Dr. Enis Karaarslan
Mugla Sitki Kocman Universitesi
Read the Original
This page is a summary of: An artificial intelligence–based decision support and resource management system for COVID-19 pandemic, January 2021, Elsevier,
DOI: 10.1016/b978-0-12-824536-1.00029-0.
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