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Optimizing Global Influenza Surveillance for Locations with Deficient Data (Student Abstract)

conference contribution
posted on 2023-07-18, 04:41 authored by S Shan, Q Tan, YC Lau, Z Du, EHY Lau, P Wu, BJ Cowling
For better monitoring and controlling influenza, WHO has launched FluNet (recently integrated to FluMART) to provide a unified platform for participating countries to routinely collect influenza-related syndromic, epidemiological and virological data. However, the reported data were incomplete. We propose a novel surveillance system based on data from multiple sources to accurately assess the epidemic status of different countries, especially for those with missing surveillance data in some periods. The proposed method can automatically select a small set of reliable and informative indicators for assessing the underlying epidemic status and proper supporting data to train the predictive model. Our proactive selection method outperforms three other out-of-box methods (linear regression, multilayer perceptron, and long-short term memory) to make accurate predictions.

History

Volume

36

Pagination

13045-13046

Location

Virtual

Start date

2022-02-22

End date

2022-03-01

ISSN

2159-5399

eISSN

2374-3468

ISBN-13

9781577358763

ISBN-10

1577358767

Language

English

Title of proceedings

Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022

Event

AAAI Conference on Artificial Intelligence. (36th : 2022 : Virtual)

Issue

11

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Place of publication

Palo Alto, Calif.

Series

Proceedings of the AAAI Conference on Artificial Intelligence

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