What is it about?

A lot of articles were produced during the pandemic of COVID-19 and continue to be produced. The article proposes a system for diagnosis of COVID-19 disease. Also nowadays, the presentation of knowledge and the research for the reasoning algorithms are progressively improving in the domain of Artificial Intelligence. Besides these, distributed reasoning as a part of data mining has become a solution for the increasing everyday data amount. As a result, the paper proposes a case-based non-monotonic reasoner for uncertain and vague COVID-19 information that is appropriate for work with Big Data. Also, a COVID-19 knowledge base model is proposed. The reasoner implements rules for the distribution of the information that gives the possibility to work with Big data. The proposed reasoning algorithm is applied for COVID-19. It shows the implementation of the reasoner into the data mining system and the returned results from the system are evaluated. The results show that the system returns relatively high results concerning the other system for recommendation.

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Why is it important?

Data mining systems have played a crucial role in addressing the challenges posed by the COVID-19 pandemic. Here are the key reasons why these systems are important: 1. Epidemiological Tracking and Prediction: Early Detection: Data mining systems help in the early detection of outbreaks by analyzing data from various sources such as social media, health reports, and news articles. This early warning can be critical in preventing the spread of the virus. Trend Analysis: By analyzing historical and real-time data, data mining systems can identify trends and patterns in the spread of COVID-19, which aids in predicting future outbreaks and potential hotspots. 2. Public Health Decision Making: Resource Allocation: Data mining systems assist in optimizing the allocation of healthcare resources like ventilators, hospital beds, and medical staff based on the predicted needs. Policy Formulation: Insights derived from data mining help policymakers make informed decisions regarding lockdowns, travel restrictions, and other public health measures. 3. Vaccine and Drug Development: Accelerated Research: Data mining helps researchers identify potential drug candidates and vaccine targets by analyzing vast amounts of biomedical data. Clinical Trials: These systems can optimize clinical trial processes by selecting suitable candidates and predicting potential side effects, thus speeding up the development of effective treatments. 4. Contact Tracing and Quarantine Management: Effective Contact Tracing: Data mining enables efficient contact tracing by analyzing mobility and interaction data, which helps in quickly identifying and isolating potential carriers of the virus. Quarantine Monitoring: Systems can monitor compliance with quarantine measures through the analysis of location and health data, ensuring that isolation protocols are followed. 5. Information Dissemination and Public Awareness: Accurate Information: Data mining systems help in disseminating accurate and timely information to the public, combating misinformation and ensuring that people are informed about safety measures and vaccination campaigns. Behavioral Insights: By analyzing public sentiment and behavior, these systems can tailor public health messages to be more effective and resonate with different demographics. 6. Economic and Social Impact Analysis: Impact Assessment: Data mining can assess the economic and social impacts of the pandemic, helping governments and organizations to plan and implement recovery strategies. Support Programs: Analysis of data can help in the design and distribution of support programs for affected individuals and businesses, ensuring that aid reaches those in need. 7. Global Collaboration and Data Sharing: Collaborative Research: Data mining facilitates global collaboration by sharing data and insights across countries and institutions, leading to a more coordinated and effective response to the pandemic. Standardization and Integration: These systems help in standardizing and integrating data from various sources, making it easier to compile and analyze comprehensive datasets. Conclusion: Data mining systems have been indispensable in the fight against COVID-19 due to their ability to analyze large volumes of data, identify patterns, and provide actionable insights. They have supported epidemiological tracking, informed public health decisions, accelerated medical research, enabled effective contact tracing, and facilitated the dissemination of accurate information. Moreover, they have helped assess the economic and social impacts of the pandemic, fostered global collaboration, and improved the overall response to this unprecedented health crisis.

Perspectives

Examining the importance of data mining systems for COVID-19 from various perspectives reveals the broad impact and multifaceted benefits these systems provide. Here are the key perspectives: 1. Healthcare Providers and Public Health Officials: Improved Response Times: Data mining systems enable rapid identification of emerging hotspots and potential outbreaks, allowing healthcare providers and public health officials to respond swiftly. Resource Optimization: These systems help in predicting healthcare needs, ensuring optimal allocation of medical supplies, personnel, and facilities. Patient Care: By analyzing patient data, healthcare providers can improve treatment plans and manage patient care more effectively. 2. Researchers and Scientists: Accelerated Research: Data mining accelerates the research process by identifying trends and correlations in vast datasets, leading to quicker discoveries of treatments and vaccines. Collaborative Efforts: Researchers can share data and findings more easily, fostering global collaboration and accelerating collective efforts to understand and combat the virus. 3. Government and Policymakers: Informed Decision-Making: Policymakers rely on data-driven insights to make informed decisions about lockdowns, travel restrictions, and public health guidelines. Economic Planning: By understanding the pandemic's impact on various sectors, governments can develop targeted economic recovery plans and support programs. 4. Businesses and the Economy: Operational Continuity: Businesses use data mining to manage risks and ensure operational continuity by predicting and mitigating the impact of COVID-19 on their workforce and supply chains. Market Insights: Data mining provides insights into consumer behavior changes during the pandemic, helping businesses adapt their strategies and offerings. 5. General Public: Awareness and Safety: The public benefits from accurate and timely information on the pandemic, which helps in following safety measures and reducing the spread of the virus. Behavioral Insights: Understanding public sentiment and behavior through data analysis helps in crafting effective communication strategies to encourage compliance with health guidelines. 6. Educational Institutions: Remote Learning: Data mining helps educational institutions understand the challenges and effectiveness of remote learning, enabling them to make data-driven improvements. Safety Protocols: Schools and universities use data to develop and implement safety protocols for reopening and managing in-person learning environments. 7. Technology and Data Science Community: Innovation and Development: The pandemic has spurred innovation in data mining techniques and applications, driving advancements in the field of data science. Ethical Considerations: The community also engages in discussions about the ethical use of data, particularly regarding privacy and consent in health data mining. 8. International Organizations and NGOs: Global Coordination: International organizations use data mining to coordinate global responses, track the virus's spread, and manage resource distribution effectively. Humanitarian Aid: NGOs rely on data to identify regions most in need of humanitarian aid and to monitor the impact of their interventions. Conclusion: From these diverse perspectives, the critical role of data mining systems in managing the COVID-19 pandemic becomes evident. For healthcare providers, it means improved patient care and resource management. Researchers benefit from accelerated discoveries and collaboration. Governments and policymakers gain the ability to make data-driven decisions that protect public health and economic stability. Businesses and educational institutions can adapt and continue operations more effectively. The general public receives timely and accurate information, enhancing compliance with safety measures. The technology and data science community experiences growth and innovation, while international organizations and NGOs improve global coordination and humanitarian efforts. Each perspective underscores the transformative impact of data mining in addressing the myriad challenges posed by the pandemic.

Mariya Evtimova-Gardair
Technical University Sofia

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This page is a summary of: Data Mining from Knowledge Cases of COVID-19, WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS, February 2024, World Scientific and Engineering Academy and Society (WSEAS),
DOI: 10.37394/23209.2024.21.10.
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