AI-Assisted Real-Time Monitoring of Infectious Diseases in Urban Areas
The rapid expansion of infectious diseases in urban environments presents a significant public health challenge, as traditional surveillance methods rely on delayed case reporting, limiting proactive response capabilities. With the increasing availability of real-time health data, artificial intelli...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/12/1911 |
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| author | Mohammed M. Alwakeel |
| author_facet | Mohammed M. Alwakeel |
| author_sort | Mohammed M. Alwakeel |
| collection | DOAJ |
| description | The rapid expansion of infectious diseases in urban environments presents a significant public health challenge, as traditional surveillance methods rely on delayed case reporting, limiting proactive response capabilities. With the increasing availability of real-time health data, artificial intelligence (AI) has emerged as a powerful tool for disease monitoring, anomaly detection, and outbreak prediction. This study proposes SmartHealth-Track, an AI-powered real-time infectious disease monitoring framework that integrates machine learning models with IoT-enabled surveillance, smart pharmacy analytics, wearable health tracking, and wastewater surveillance to enhance early outbreak detection and predictive forecasting. The system leverages time series forecasting with long short-term memory (LSTM) networks, logistic regression for outbreak probability estimation, anomaly detection with isolation forests, and natural language processing (NLP) for extracting epidemiological insights from public health reports and social media trends. Experimental validation using real-world datasets demonstrated that SmartHealth-Track achieves high accuracy, with an outbreak detection accuracy of 92.4%, wearable-based fever detection accuracy of 93.5%, AI-driven contact tracing precision of 91.2%, and AI-enhanced wastewater pathogen classification accuracy of 94.1%. The findings confirm that AI-driven real-time surveillance significantly improves outbreak detection and forecasting, enabling timely public health interventions. Future research should focus on federated learning for secure data collaboration and reinforcement learning for adaptive decision making. |
| format | Article |
| id | doaj-art-a63d6ab014c748f38f808c29dc1be963 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-a63d6ab014c748f38f808c29dc1be9632025-08-20T03:27:22ZengMDPI AGMathematics2227-73902025-06-011312191110.3390/math13121911AI-Assisted Real-Time Monitoring of Infectious Diseases in Urban AreasMohammed M. Alwakeel0Computer Engineering Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71491, Saudi ArabiaThe rapid expansion of infectious diseases in urban environments presents a significant public health challenge, as traditional surveillance methods rely on delayed case reporting, limiting proactive response capabilities. With the increasing availability of real-time health data, artificial intelligence (AI) has emerged as a powerful tool for disease monitoring, anomaly detection, and outbreak prediction. This study proposes SmartHealth-Track, an AI-powered real-time infectious disease monitoring framework that integrates machine learning models with IoT-enabled surveillance, smart pharmacy analytics, wearable health tracking, and wastewater surveillance to enhance early outbreak detection and predictive forecasting. The system leverages time series forecasting with long short-term memory (LSTM) networks, logistic regression for outbreak probability estimation, anomaly detection with isolation forests, and natural language processing (NLP) for extracting epidemiological insights from public health reports and social media trends. Experimental validation using real-world datasets demonstrated that SmartHealth-Track achieves high accuracy, with an outbreak detection accuracy of 92.4%, wearable-based fever detection accuracy of 93.5%, AI-driven contact tracing precision of 91.2%, and AI-enhanced wastewater pathogen classification accuracy of 94.1%. The findings confirm that AI-driven real-time surveillance significantly improves outbreak detection and forecasting, enabling timely public health interventions. Future research should focus on federated learning for secure data collaboration and reinforcement learning for adaptive decision making.https://www.mdpi.com/2227-7390/13/12/1911AI-driven surveillanceinfectious disease monitoringanomaly detectionpredictive analyticssmart pharmacy trackingIoT-enabled healthcare |
| spellingShingle | Mohammed M. Alwakeel AI-Assisted Real-Time Monitoring of Infectious Diseases in Urban Areas Mathematics AI-driven surveillance infectious disease monitoring anomaly detection predictive analytics smart pharmacy tracking IoT-enabled healthcare |
| title | AI-Assisted Real-Time Monitoring of Infectious Diseases in Urban Areas |
| title_full | AI-Assisted Real-Time Monitoring of Infectious Diseases in Urban Areas |
| title_fullStr | AI-Assisted Real-Time Monitoring of Infectious Diseases in Urban Areas |
| title_full_unstemmed | AI-Assisted Real-Time Monitoring of Infectious Diseases in Urban Areas |
| title_short | AI-Assisted Real-Time Monitoring of Infectious Diseases in Urban Areas |
| title_sort | ai assisted real time monitoring of infectious diseases in urban areas |
| topic | AI-driven surveillance infectious disease monitoring anomaly detection predictive analytics smart pharmacy tracking IoT-enabled healthcare |
| url | https://www.mdpi.com/2227-7390/13/12/1911 |
| work_keys_str_mv | AT mohammedmalwakeel aiassistedrealtimemonitoringofinfectiousdiseasesinurbanareas |