Epidemiology-aware Deep Learning for Infectious Disease Dynamics Prediction

Mutong Liu, Yang Liu, Jiming Liu

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

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 languageEnglish
Title of host publicationCIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery (ACM)
Pages4084-4088
Number of pages5
ISBN (Print)9798400701245
DOIs
Publication statusPublished - 21 Oct 2023
Event32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 - Birmingham, United Kingdom
Duration: 21 Oct 202325 Oct 2023
https://dl.acm.org/doi/proceedings/10.1145/3583780

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference32nd ACM International Conference on Information and Knowledge Management, CIKM 2023
Country/TerritoryUnited Kingdom
CityBirmingham
Period21/10/2325/10/23
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

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