Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction

CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation

Continual learning is the ability of a model to learn over time without forgetting previous knowledge. Therefore, adapting new data in dynamic fields like disease outbreak prediction is paramount. Deep neural networks are prone to error due to catastrophic forgetting. This study introduces a novel CEL model for Continual Learning by leveraging domain adaptation via Elastic weight consolidation (EWC). This model aims to mitigate the catastrophic forgetting phenomenon in a domain incremental setting. The Fisher information matrix (FIM) is constructed with EWC to develop a regularization term that penalizes changes to essential parameters. We conducted experiments on three distinct diseases, influenza, mpox, and measles, with customized metrics. The high R-squared values during evaluation and reevaluation outperform the other state-of-the-art models in several contexts. The results indicate that CEL adapts well to incremental data. CEL's robustness emphasizes its minimal 65% forgetting rate and 18% higher memory stability compared to existing benchmark studies. This study highlights CEL's versatility in disease outbreak prediction by addressing evolving data with temporal patterns. It offers a valuable model for proactive disease control with accurate and timely predictions.

Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022

BACKGROUND

Influenza-like illness (ILI) imposes a significant burden on patients, employers and society. However, there is no analysis and prediction at the hospital level in Chongqing. We aimed to characterize the seasonality of ILI, examine age heterogeneity in visits, and predict ILI peaks and assess whether they affect hospital operations.

METHODS

The multiplicative decomposition model was employed to decompose the trend and seasonality of ILI, and the Seasonal Auto-Regressive Integrated Moving Average with exogenous factors (SARIMAX) model was used for the trend and short-term prediction of ILI. We used Grid Search and Akaike information criterion (AIC) to calibrate and verify the optimal hyperparameters, and verified the residuals of the multiplicative decomposition and SARIMAX model, which are both white noise.

RESULTS

During the 12-year study period, ILI showed a continuous upward trend, peaking in winter (Dec. - Jan.) and a small spike in May-June in the 2-4-year-old high-risk group for severe disease. The mean length of stay (LOS) in ILI peaked around summer (about Aug.), and the LOS in the 0-1 and ≥ 65 years old severely high-risk group was more irregular than the others. We found some anomalies in the predictive analysis of the test set, which were basically consistent with the dynamic zero-COVID policy at the time.

CONCLUSION

The ILI patient visits showed a clear cyclical and seasonal pattern. ILI prevention and control activities can be conducted seasonally on an annual basis, and age heterogeneity should be considered in the health resource planning. Targeted immunization policies are essential to mitigate potential pandemic threats. The SARIMAX model has good short-term forecasting ability and accuracy. It can help explore the epidemiological characteristics of ILI and provide an early warning and decision-making basis for the allocation of medical resources related to ILI visits.

Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided gradient-based optimization

This research delves into the significance of influenza outbreaks in public health, particularly the importance of accurate forecasts using weekly Influenza-like illness (ILI) rates. The present work develops a novel hybrid machine-learning model by combining singular value decomposition with kernel ridge regression (SKRR). In this context, a novel hybrid model known as H-SKRR is developed by combining two robust forecasting approaches, SKRR and ridge regression, which aims to improve multi-step-ahead predictions for weekly ILI rates in Southern and Northern China. The study begins with feature selection via XGBoost in the preprocessing phase, identifying optimal precursor information guided by importance factors. It decomposes the original signal using multivariate variational mode decomposition (MVMD) to address non-stationarity and complexity. H-SKRR is implemented by incorporating significant lagged-time components across sub-components. The aggregated forecasted values from these sub-components generate ILI values for two horizons (i.e., 4-and 7-weekly ahead). Employing the gradient-based optimization (GBO) algorithm fine-tunes model parameters. Furthermore, the deep random vector functional link (dRVFL), Ridge regression, and gated recurrent unit neural network (GRU) models were employed to validate the MVMD-H-SKRR-GBO paradigm's effectiveness. The outcomes, assessed using the MARCOS (Measurement of alternatives and ranking according to compromise solution) method as a multi-criteria decision-making method, highlight the superior accuracy of the MVMD-H-SKRR-GBO model in predicting ILI rates. The results clearly highlight the exceptional performance of the MVMD-H-SKRR-GBO model, with outstanding precision demonstrated by impressive R, RMSE, IA, and U95 % values of 0.946, 0.388, 0.970, and 1.075, respectively, at t + 7.

Predicting influenza with pandemic-awareness via Dynamic Virtual Graph Significance Networks

Every year, influenza spreads worldwide and burdens people's health substantially. We need a reliable model to help hospitals, pharmaceutical companies, and governments better prepare for influenza outbreaks in a timely manner. However, the domain knowledge for such public health events, such as the variable influenza seasonality and occasional pandemics, poses significant challenges in predicting influenza outbreaks. The existing methods use current and historical values in a user-defined time window as input to predict future values but lack considering the situations outside the window. To address these limitations, we proposed Dynamic Virtual Graph Significance Networks (DVGSN). The graph-based algorithm can supervisedly and dynamically learn the implied knowledge from similar "infection situations" in all the historical timepoints without the limitation of time window. Furthermore, representation learning on the dynamic virtual graph can tackle the varied seasonality with pandemic-awareness without requiring domain knowledge input. The extensive experiments on real-world influenza data demonstrate that DVGSN significantly outperforms the state-of-the-art methods. To the best of our knowledge, this is the first attempt to supervisedly learn a dynamic virtual graph for time-series prediction tasks. Moreover, the proposed method has rich interpretabilities, which makes the method more acceptable in the fields of public health, life sciences, and so on. Our source code and dataset are available at https://github.com/aI-area/DVGSN.