Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 11, 2022
Date Accepted: Jan 10, 2023
Establishing a new deep learning model for influenza prediction from multi-source heterogeneous data in a megacity
ABSTRACT
Background:
In megacities, it is urgent to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. The prediction and early warning model for influenza and other acute respiratory infectious diseases still needs to be improved.
Objective:
In this study, we integrated heterogeneous data from different sources in a megacity through improved deep learning models to explore more sensitive and accurate influenza prediction models, such as influenza-like illness case data from hospitals, and meteorological, search engine, and social economy data.
Methods:
We collected multisource and multidimensional data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness cases, data of virological surveillance, data of climate and demography, and data of search engines. To avoid collinearity, we had the best predictor according to the weight and correlation of each factor. We had established a new Multi-Attention-LSTM (MAL) deep learning model and predicted ILI% and the product of ILI% and influenza positive rate respectively. Finally, we compared models from various sources of data and different prediction methods.
Results:
The highest correlation coefficients were the Baidu query data for ILI% and air quality for ILI positive rate. In addition to these variables, temperature, sunlight, air pressure, humidity and wind speed were high correlation coefficients for ILI%, whereas temperature, Baidu query data, air pressure, wind speed, and humidity for positive rate. Whether it is ILI% or influenza activity, it is best to put data from various sources into the model, and R2 can reach more than 0.7. The comparison between different models showed that the new Multi-Attention-LSTM deep learning model we have established had the best prediction effect.
Conclusions:
The comparison between different models showed that the new MLA deep learning model we have established had the best prediction effect. Natural factors and search engine query data are more helpful in forecasting ILI patterns in megacities. Since a more timely and effective prediction of influenza and other respiratory infectious disease epidemic intensity, early and better preparedness can be made to reduce the health damage to the population
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