Comparative estimation of the spread of acute diarrhea and dengue in India using statistical mathematical and deep learning models.

ABSTRACT: This study aims to forecast the spread of acute diarrhoea and dengue diseases in India by conducting a comparative analysis of statistical, mathematical (compartmental), and deep learning time series models. Utilizing weekly reported cases and fatalities from January 1, 2011, to Week 33, 2024, we evaluated ten forecasting techniques, including Regression, Bayesian Linear Regression with MultiOutputRegressor + XGBoost, SIR model, Prophet, N-BEATS, GluonTS, LSTM, Seq2Seq, and the ARIMA statistical model. Performance was assessed using mean absolute percentage error (MAPE) and root mean square error (RMSE). Our findings indicate that the ARIMA model excels in predicting acute diarrhoeal disease cases, achieving an RMSE of 317.7 and a MAPE of 2.4. Conversely, the Seq2Seq model outperforms othe

SUBMITTER: Singh A 

PROVIDER: S-EPMC12501317 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature