Information-guided adaptive learning approach for active surveillance of infectious diseases
The infectious disease surveillance system is a key support tool for public health decision making. Current research concentrates on optimizing static sentinel deployment to address the problem of incomplete data due to the lack of sufficient surveillance resources. In this study, we introduce an in...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Infectious Disease Modelling |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042724001209 |
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| _version_ | 1846113712482549760 |
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| author | Qi Tan Chenyang Zhang Jiwen Xia Ruiqi Wang Lian Zhou Zhanwei Du Benyun Shi |
| author_facet | Qi Tan Chenyang Zhang Jiwen Xia Ruiqi Wang Lian Zhou Zhanwei Du Benyun Shi |
| author_sort | Qi Tan |
| collection | DOAJ |
| description | The infectious disease surveillance system is a key support tool for public health decision making. Current research concentrates on optimizing static sentinel deployment to address the problem of incomplete data due to the lack of sufficient surveillance resources. In this study, we introduce an information-guided adaptive learning strategy for the dynamic surveillance of infectious diseases. The goal is to improve monitoring effectiveness in situations where it is possible to adjust the focus of surveillance, such as serial surveys and allocation of testing tools. Specifically, we develop a probabilistic neural network model to learn spatio-temporal correlations among the numbers of infections. Based on a probabilistic model, we evaluate the information gain of monitoring a spatio-temporal target and design a greedy selection algorithm for monitoring targets selection. Moreover, we integrate two major surveillance objectives, i.e., informativeness and coverage, in the monitoring target selection. The experimental results on the synthetic dataset and two real-world datasets demonstrate the effectiveness of our approach, showcasing the promise of further exploration and application of dynamic adaptive active surveillance. |
| format | Article |
| id | doaj-art-5085a373da7a4958914c1dd5697155d2 |
| institution | Kabale University |
| issn | 2468-0427 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj-art-5085a373da7a4958914c1dd5697155d22024-12-21T04:28:49ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272025-03-01101257267Information-guided adaptive learning approach for active surveillance of infectious diseasesQi Tan0Chenyang Zhang1Jiwen Xia2Ruiqi Wang3Lian Zhou4Zhanwei Du5Benyun Shi6College of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu Province, China; College of Artificial Intelligence, Nanjing Tech University, Nanjing, Jiangsu Province, ChinaCollege of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu Province, China; College of Artificial Intelligence, Nanjing Tech University, Nanjing, Jiangsu Province, ChinaCollege of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu Province, China; College of Artificial Intelligence, Nanjing Tech University, Nanjing, Jiangsu Province, ChinaFaculty of Arts and Social Sciences, Hong Kong Baptist University, Hong Kong SAR, ChinaJiangsu Provincial Center for Disease Control and Prevention, Nanjing, ChinaWHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong SAR, China; Laboratory of Data Discovery for Health Limited, Hong Kong SAR, ChinaCollege of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu Province, China; College of Artificial Intelligence, Nanjing Tech University, Nanjing, Jiangsu Province, China; Corresponding author. College of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu Province, China.The infectious disease surveillance system is a key support tool for public health decision making. Current research concentrates on optimizing static sentinel deployment to address the problem of incomplete data due to the lack of sufficient surveillance resources. In this study, we introduce an information-guided adaptive learning strategy for the dynamic surveillance of infectious diseases. The goal is to improve monitoring effectiveness in situations where it is possible to adjust the focus of surveillance, such as serial surveys and allocation of testing tools. Specifically, we develop a probabilistic neural network model to learn spatio-temporal correlations among the numbers of infections. Based on a probabilistic model, we evaluate the information gain of monitoring a spatio-temporal target and design a greedy selection algorithm for monitoring targets selection. Moreover, we integrate two major surveillance objectives, i.e., informativeness and coverage, in the monitoring target selection. The experimental results on the synthetic dataset and two real-world datasets demonstrate the effectiveness of our approach, showcasing the promise of further exploration and application of dynamic adaptive active surveillance.http://www.sciencedirect.com/science/article/pii/S2468042724001209Active surveillanceAdaptive learningIncomplete dataInformation guide |
| spellingShingle | Qi Tan Chenyang Zhang Jiwen Xia Ruiqi Wang Lian Zhou Zhanwei Du Benyun Shi Information-guided adaptive learning approach for active surveillance of infectious diseases Infectious Disease Modelling Active surveillance Adaptive learning Incomplete data Information guide |
| title | Information-guided adaptive learning approach for active surveillance of infectious diseases |
| title_full | Information-guided adaptive learning approach for active surveillance of infectious diseases |
| title_fullStr | Information-guided adaptive learning approach for active surveillance of infectious diseases |
| title_full_unstemmed | Information-guided adaptive learning approach for active surveillance of infectious diseases |
| title_short | Information-guided adaptive learning approach for active surveillance of infectious diseases |
| title_sort | information guided adaptive learning approach for active surveillance of infectious diseases |
| topic | Active surveillance Adaptive learning Incomplete data Information guide |
| url | http://www.sciencedirect.com/science/article/pii/S2468042724001209 |
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