Deep neural networks with application in predicting the spread of avian influenza through disease-informed neural networks

Nickson Golooba; Woldegebriel Assefa Woldegerima; Huaiping Zhu; Nickson Golooba; Woldegebriel Assefa Woldegerima; Huaiping Zhu

doi:10.3934/bdia.2025001

Big Data and Information Analytics

2025, Volume 9, 1-28. doi: 10.3934/bdia.2025001

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Research article

Deep neural networks with application in predicting the spread of avian influenza through disease-informed neural networks

Nickson Golooba ¹,
Woldegebriel Assefa Woldegerima ^{2,3
,
,},
Huaiping Zhu ^1,2

1.
Laboratory of Mathematical Parallel Systems (LAMPS), CDM, and OMNI-RÉUNIS, Department of Mathematics and Statistics, York University, Toronto, Canada
2.
Centre for Disease Modeling (CDM), Department of Mathematics and Statistics, York University, Toronto, Canada
3.
Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, Canada

Received: 09 December 2024 Revised: 01 February 2025 Accepted: 07 February 2025 Published: 21 March 2025

Deep learning has emerged in many fields in recent times where neural networks are used to learn and understand data. This study combined deep learning frameworks with epidemiological models and was aimed specifically at the creation and testing of disease-informed neural networks (DINNs) with a view of modeling the infection dynamics of epidemics. Our research thus trained the DINN on synthetic data derived from a susceptible infected-susceptible infected removed (SI-SIR) model designed for avian influenza and showed the model's accuracy in predicting extinction and persistence conditions. In the method, a twelve hidden layer model was constructed with sixty-four neurons per layer and the rectified linear unit activation function was used. The network was trained to predict the time evolution of five state variables for birds and humans over 50,000 epochs. The overall loss minimized to 0.000006, characterized by a combination of data and physics losses, enabling the DINN to follow the differential equations describing the disease progression.
- disease-informed neural network,
- physics-informed neural networks,
- deep neural networks,
- avian influenza,
- SI-SIR model,
- data and physics loss functions,
- neural network architecture
Citation: Nickson Golooba, Woldegebriel Assefa Woldegerima, Huaiping Zhu. Deep neural networks with application in predicting the spread of avian influenza through disease-informed neural networks[J]. Big Data and Information Analytics, 2025, 9: 1-28. doi: 10.3934/bdia.2025001

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Abstract

Deep learning has emerged in many fields in recent times where neural networks are used to learn and understand data. This study combined deep learning frameworks with epidemiological models and was aimed specifically at the creation and testing of disease-informed neural networks (DINNs) with a view of modeling the infection dynamics of epidemics. Our research thus trained the DINN on synthetic data derived from a susceptible infected-susceptible infected removed (SI-SIR) model designed for avian influenza and showed the model's accuracy in predicting extinction and persistence conditions. In the method, a twelve hidden layer model was constructed with sixty-four neurons per layer and the rectified linear unit activation function was used. The network was trained to predict the time evolution of five state variables for birds and humans over 50,000 epochs. The overall loss minimized to 0.000006, characterized by a combination of data and physics losses, enabling the DINN to follow the differential equations describing the disease progression.

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