Neural network models for influenza forecasting with associated uncertainty using Web search activity trends
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Neural network models for influenza forecasting with associated uncertainty using Web search activity trends. / Morris, Michael; Hayes, Peter; Cox, Ingemar J.; Lampos, Vasileios.
In: PLOS Computational Biology, Vol. 19, No. 8, e1011392, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Neural network models for influenza forecasting with associated uncertainty using Web search activity trends
AU - Morris, Michael
AU - Hayes, Peter
AU - Cox, Ingemar J.
AU - Lampos, Vasileios
N1 - Publisher Copyright: Copyright: © 2023 Morris et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2023
Y1 - 2023
N2 - Influenza affects millions of people every year. It causes a considerable amount of medical visits and hospitalisations as well as hundreds of thousands of deaths. Forecasting influenza prevalence with good accuracy can significantly help public health agencies to timely react to seasonal or novel strain epidemics. Although significant progress has been made, influenza forecasting remains a challenging modelling task. In this paper, we propose a methodological framework that improves over the state-of-the-art forecasting accuracy of influenza-like illness (ILI) rates in the United States. We achieve this by using Web search activity time series in conjunction with historical ILI rates as observations for training neural network (NN) architectures. The proposed models incorporate Bayesian layers to produce associated uncertainty intervals to their forecast estimates, positioning themselves as legitimate complementary solutions to more conventional approaches. The best performing NN, referred to as the iterative recurrent neural network (IRNN) architecture, reduces mean absolute error by 10.3% and improves skill by 17.1% on average in nowcasting and forecasting tasks across 4 consecutive flu seasons.
AB - Influenza affects millions of people every year. It causes a considerable amount of medical visits and hospitalisations as well as hundreds of thousands of deaths. Forecasting influenza prevalence with good accuracy can significantly help public health agencies to timely react to seasonal or novel strain epidemics. Although significant progress has been made, influenza forecasting remains a challenging modelling task. In this paper, we propose a methodological framework that improves over the state-of-the-art forecasting accuracy of influenza-like illness (ILI) rates in the United States. We achieve this by using Web search activity time series in conjunction with historical ILI rates as observations for training neural network (NN) architectures. The proposed models incorporate Bayesian layers to produce associated uncertainty intervals to their forecast estimates, positioning themselves as legitimate complementary solutions to more conventional approaches. The best performing NN, referred to as the iterative recurrent neural network (IRNN) architecture, reduces mean absolute error by 10.3% and improves skill by 17.1% on average in nowcasting and forecasting tasks across 4 consecutive flu seasons.
UR - http://www.scopus.com/inward/record.url?scp=85170279781&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1011392
DO - 10.1371/journal.pcbi.1011392
M3 - Journal article
C2 - 37639427
AN - SCOPUS:85170279781
VL - 19
JO - P L o S Computational Biology (Online)
JF - P L o S Computational Biology (Online)
SN - 1553-734X
IS - 8
M1 - e1011392
ER -
ID: 368341024