Abstract
Infectious disease risk prediction plays a vital role in disease control and prevention. Recent studies in machine learning have attempted to incorporate epidemiological knowledge into the learning process to enhance the accuracy and informativeness of prediction results for decision-making. However, these methods commonly involve single-patch mechanistic models, overlooking the disease spread across multiple locations caused by human mobility. Additionally, these methods often require extra information beyond the infection data, which is typically unavailable in reality. To address these issues, this paper proposes a novel epidemiology-aware deep learning framework that integrates a fundamental epidemic component, the next-generation matrix (NGM), into the deep architecture and objective function. This integration enables the inclusion of both mechanistic models and human mobility in the learning process to characterize within- and cross-location disease transmission. From this framework, two novel methods, Epi-CNNRNN-Res and Epi-Cola-GNN, are further developed to predict epidemics, with experimental results validating their effectiveness.
Original language | English |
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Title of host publication | CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management |
Publisher | Association for Computing Machinery (ACM) |
Pages | 4084-4088 |
Number of pages | 5 |
ISBN (Print) | 9798400701245 |
DOIs | |
Publication status | Published - 21 Oct 2023 |
Event | 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 - Birmingham, United Kingdom Duration: 21 Oct 2023 → 25 Oct 2023 https://dl.acm.org/doi/proceedings/10.1145/3583780 |
Publication series
Name | International Conference on Information and Knowledge Management, Proceedings |
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Conference
Conference | 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 |
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Country/Territory | United Kingdom |
City | Birmingham |
Period | 21/10/23 → 25/10/23 |
Internet address |
Scopus Subject Areas
- Business, Management and Accounting(all)
- Decision Sciences(all)
User-Defined Keywords
- Deep learning
- Epidemiological constraints
- Infectious disease dynamics prediction
- AI for social good