Scholarlink

Background <p>Accurate prediction of trends in infectious diseases is crucial for early public health actions, which are difficult to achieve in low-resource countries such as Bangladesh, where the healthcare system is significantly challenged. Although many researchers worldwide have utilized machine learning (ML) and deep learning (DL) models for making COVID-19 predictions, limited research has focused on context-specific ensemble forecasting approaches for Bangladesh, creating a gap in local epidemic modeling. This study aims to design a Weighted Ensemble forecasting model that combines the best time series models (SARIMA, ETS), ML (XGBoost), and DL (LSTM, RNN, GRU) models to improve the accuracy of daily new COVID-19 cases in Bangladesh.</p> Methods <p>We developed a context-specific Weighted Ensemble model integrating Seasonal Autoregressive Integrated Moving Average, XGBoost, and Recurrent Neural Network, with weights (0.1531, 0.4319, 0.4150). Log transformation handled zeros, with scaling for neural networks. Forecasts extended to May 2027.</p> Result <p>Out-of-sample results (SARIMA: RMSE = 14.56, MAE = 9.02, MAPE = 216.87%; XGBoost: RMSE = 5.16, MAE = 3.45, MAPE = 134.52%; Recurrent Neural Network: RMSE = 5.37, MAE = 2.71, MAPE = 41.11%; Weighted Ensemble: RMSE = 5.87, MAE = 3.49, MAPE = 111.02%) show the Weighted Ensemble outperforms individual models, including the best time series, machine learning, and deep learning models, despite misspecification in traditional models.</p> Conclusions <p>The Combination model based on Seasonal Autoregressive Integrated Moving Average, XGBoost, and Recurrent Neural Network can improve prediction performance for Bangladesh’s complex COVID-19 data better than the XGBoost model does. The power of ensemble modeling in capturing linear seasonality and the nonlinear dynamics it is evident from these results. This novel integration can inform public health policy, allowing health authorities to fine-tune interventions and make more efficient use of resources while avoiding unnecessary lockdowns as COVID-19 becomes an endemic disease.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Combination forecasting of COVID-19 in Bangladesh: guiding public health policy through integrated time series, ML, and DL models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月12日

查看全文
  • Sharmin Akther,
  • Amartay Kumar Dhar,
  • Jakia Sultana Pingky

摘要

Background

Accurate prediction of trends in infectious diseases is crucial for early public health actions, which are difficult to achieve in low-resource countries such as Bangladesh, where the healthcare system is significantly challenged. Although many researchers worldwide have utilized machine learning (ML) and deep learning (DL) models for making COVID-19 predictions, limited research has focused on context-specific ensemble forecasting approaches for Bangladesh, creating a gap in local epidemic modeling. This study aims to design a Weighted Ensemble forecasting model that combines the best time series models (SARIMA, ETS), ML (XGBoost), and DL (LSTM, RNN, GRU) models to improve the accuracy of daily new COVID-19 cases in Bangladesh.

Methods

We developed a context-specific Weighted Ensemble model integrating Seasonal Autoregressive Integrated Moving Average, XGBoost, and Recurrent Neural Network, with weights (0.1531, 0.4319, 0.4150). Log transformation handled zeros, with scaling for neural networks. Forecasts extended to May 2027.

Result

Out-of-sample results (SARIMA: RMSE = 14.56, MAE = 9.02, MAPE = 216.87%; XGBoost: RMSE = 5.16, MAE = 3.45, MAPE = 134.52%; Recurrent Neural Network: RMSE = 5.37, MAE = 2.71, MAPE = 41.11%; Weighted Ensemble: RMSE = 5.87, MAE = 3.49, MAPE = 111.02%) show the Weighted Ensemble outperforms individual models, including the best time series, machine learning, and deep learning models, despite misspecification in traditional models.

Conclusions

The Combination model based on Seasonal Autoregressive Integrated Moving Average, XGBoost, and Recurrent Neural Network can improve prediction performance for Bangladesh’s complex COVID-19 data better than the XGBoost model does. The power of ensemble modeling in capturing linear seasonality and the nonlinear dynamics it is evident from these results. This novel integration can inform public health policy, allowing health authorities to fine-tune interventions and make more efficient use of resources while avoiding unnecessary lockdowns as COVID-19 becomes an endemic disease.