Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review
Background This research focuses on improving epidemic monitoring systems by incorporating advanced technologies to enhance the surveillance of diseases more effectively than before. Considering the drawbacks associated with surveillance methods in terms of time consumption and efficiency, issues hi...
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PeerJ Inc.
2025-05-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2874.pdf |
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| author | Hazeeqah Amny Kamarul Aryffin Murtadha Arif Bin Sahbudin Sakinah Ali Pitchay Azni Haslizan Abhalim Ilfita Sahbudin |
| author_facet | Hazeeqah Amny Kamarul Aryffin Murtadha Arif Bin Sahbudin Sakinah Ali Pitchay Azni Haslizan Abhalim Ilfita Sahbudin |
| author_sort | Hazeeqah Amny Kamarul Aryffin |
| collection | DOAJ |
| description | Background This research focuses on improving epidemic monitoring systems by incorporating advanced technologies to enhance the surveillance of diseases more effectively than before. Considering the drawbacks associated with surveillance methods in terms of time consumption and efficiency, issues highlighted in this study includes the integration of Artificial Intelligence (AI) in early detection, decision support and predictive modeling, big data analytics in data sharing, contact tracing and countering misinformation, Internet of Things (IoT) devices in real time disease monitoring and Geographic Information Systems (GIS) for geospatial artificial intelligence (GeoAI) applications and disease mapping. The increasing intricacy and regular occurrence of disease outbreaks underscore the pressing necessity for improvements in public health monitoring systems. This research delves into the developments and their utilization in detecting and handling infectious diseases while exploring how these progressions contribute to decision making and policy development, in public healthcare. Methodology This review systematically analyzes how technological tools are being used in epidemic monitoring by conducting a structured search across online literature databases and applying eligibility criteria to identify relevant studies on current technological trends in public health surveillance. Results The research reviewed 69 articles from 2019 to 2023 focusing on emerging trends in epidemic intelligence. Most of the studies emphasized the integration of artificial intelligence with technologies like big data analytics, geographic information systems, and the Internet of Things for monitoring infectious diseases. Conclusions The expansion of publicly accessible information on the internet has opened a new pathway for epidemic intelligence. This study emphasizes the importance of integrating information technology tools such as AI, big data analytics, GIS, and the IoT in epidemic intelligence surveillance to effectively track infectious diseases. Combining these technologies helps public health agencies in detecting and responding to health threats. |
| format | Article |
| id | doaj-art-e5cbeb9a7f9444dfb540e262f6e4901f |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-e5cbeb9a7f9444dfb540e262f6e4901f2025-08-20T03:53:17ZengPeerJ Inc.PeerJ Computer Science2376-59922025-05-0111e287410.7717/peerj-cs.2874Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature reviewHazeeqah Amny Kamarul Aryffin0Murtadha Arif Bin Sahbudin1Sakinah Ali Pitchay2Azni Haslizan Abhalim3Ilfita Sahbudin4Faculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai, Negeri Sembilan, MalaysiaInstitute of Applied Data Analytics, Universiti Brunei Darussalam, Bandar Seri Begawan, BruneiFaculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai, Negeri Sembilan, MalaysiaFaculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai, Negeri Sembilan, MalaysiaRheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United KingdomBackground This research focuses on improving epidemic monitoring systems by incorporating advanced technologies to enhance the surveillance of diseases more effectively than before. Considering the drawbacks associated with surveillance methods in terms of time consumption and efficiency, issues highlighted in this study includes the integration of Artificial Intelligence (AI) in early detection, decision support and predictive modeling, big data analytics in data sharing, contact tracing and countering misinformation, Internet of Things (IoT) devices in real time disease monitoring and Geographic Information Systems (GIS) for geospatial artificial intelligence (GeoAI) applications and disease mapping. The increasing intricacy and regular occurrence of disease outbreaks underscore the pressing necessity for improvements in public health monitoring systems. This research delves into the developments and their utilization in detecting and handling infectious diseases while exploring how these progressions contribute to decision making and policy development, in public healthcare. Methodology This review systematically analyzes how technological tools are being used in epidemic monitoring by conducting a structured search across online literature databases and applying eligibility criteria to identify relevant studies on current technological trends in public health surveillance. Results The research reviewed 69 articles from 2019 to 2023 focusing on emerging trends in epidemic intelligence. Most of the studies emphasized the integration of artificial intelligence with technologies like big data analytics, geographic information systems, and the Internet of Things for monitoring infectious diseases. Conclusions The expansion of publicly accessible information on the internet has opened a new pathway for epidemic intelligence. This study emphasizes the importance of integrating information technology tools such as AI, big data analytics, GIS, and the IoT in epidemic intelligence surveillance to effectively track infectious diseases. Combining these technologies helps public health agencies in detecting and responding to health threats.https://peerj.com/articles/cs-2874.pdfEpidemic intelligenceArtificial intelligenceBig dataInternet of thingsGeographic information systemsCOVID-19 |
| spellingShingle | Hazeeqah Amny Kamarul Aryffin Murtadha Arif Bin Sahbudin Sakinah Ali Pitchay Azni Haslizan Abhalim Ilfita Sahbudin Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review PeerJ Computer Science Epidemic intelligence Artificial intelligence Big data Internet of things Geographic information systems COVID-19 |
| title | Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review |
| title_full | Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review |
| title_fullStr | Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review |
| title_full_unstemmed | Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review |
| title_short | Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review |
| title_sort | technological trends in epidemic intelligence for infectious disease surveillance a systematic literature review |
| topic | Epidemic intelligence Artificial intelligence Big data Internet of things Geographic information systems COVID-19 |
| url | https://peerj.com/articles/cs-2874.pdf |
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