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Recurrent neural network ensemble, a new instrument for the prediction of infectious diseases
1Guidonia M., Rome, Italy.
Abstract:
Infectious diseases afflict human beings since ancient times. We can classify the infectious disease in two principal types: the emerging diseases, that are caused by new pathogens, and the re-emerging diseases, due to a new spread of a known pathogen. Both types can then be subdivided in natural, accidental or intentional spreads. The risk associated to infectious diseases strongly increased in the last decades, especially because of the globalisation, which leads to a denser and more efficient link between nations, involving that a local infectious may easily spread worldwide, such as the SARS-CoV-2 in 2019-2020. The development of new methods to predict the spread of diseases is crucial. However, sometimes the variables are too many that classical algorithms fail in the prediction. Aim of this work is to investigate the use of an ensemble of recurrent neural networks for disease prediction, using real flu's data to train and develop an instrument with the capability to determine the future flues. Two different types of study have been conducted. The first study investigates the influence of the neural network architecture, and it has been performed using 12 seasons to train the model and 3 seasons to test it. The second test aims to investigate the number of seasons needed to have a good prediction for future ones. The results demonstrated that this approach could ensure very high performances also with simple architectures. The ensemble approach allows to have information about the uncertainty of the prediction, allowing also to take countermeasures as a function of that value. In the future, the use of this approach may be applied to many other types of disease.
Insights
This study introduces an ensemble of recurrent neural networks for predicting infectious disease outbreaks, like influenza. This advanced method accurately forecasts future disease spread, even with complex data, and provides uncertainty information for effective countermeasures.
Area of Science:
- Epidemiology
- Computational Biology
- Artificial Intelligence
Background:
- Infectious diseases pose a significant global health risk, amplified by globalization.
- Predicting disease spread is challenging due to numerous variables, often overwhelming classical algorithms.
- Emerging and re-emerging infectious diseases require novel prediction methodologies.
Purpose of the Study:
- To investigate the efficacy of an ensemble of recurrent neural networks for infectious disease prediction.
- To develop a predictive tool for future influenza outbreaks using real-world data.
- To assess the impact of neural network architecture and historical data on prediction accuracy.
Main Methods:
- Utilized an ensemble of recurrent neural networks for disease spread prediction.
- Trained and tested models using influenza data from multiple seasons (12 for training, 3 for testing).
- Conducted two studies: one on network architecture influence and another on the number of seasons required for accurate prediction.
Main Results:
- The ensemble recurrent neural network approach demonstrated very high prediction performance, even with simple architectures.
- The study identified the optimal number of historical seasons needed for reliable future disease forecasting.
- The ensemble method provides valuable insights into prediction uncertainty, enabling informed public health interventions.
Conclusions:
- Ensemble recurrent neural networks offer a powerful and accurate method for predicting infectious disease outbreaks.
- This approach can be adapted for various infectious diseases beyond influenza.
- The ability to quantify prediction uncertainty is crucial for proactive disease management and public health strategies.
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01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.