https://repository.auw.edu.bd/handle/123456789/3363 2026-09-09T13:14:07Z 2026-09-09T13:14:07Z Nowsher, Tahmid Arafat, Shaiful Islam Kamrujjaman, Md. https://repository.auw.edu.bd/handle/123456789/3379 2026-07-19T04:30:22Z 2026-07-17T00:00:00Z

Deep learning with LSTM networks for dengue disease forecasting and control Nowsher, Tahmid; Arafat, Shaiful Islam; Kamrujjaman, Md. Accurate forecasting of infectious disease dynamics is essential for effective public health planning and early outbreak response. Traditional statistical models and modern deep learning approaches have both been widely used for epidemiological time–series forecasting. Dengue fever remains a major public health concern in Bangladesh, where seasonal outbreaks often produce sharp and unpredictable infection spikes. Reliable forecasting models are therefore important for anticipating infection trends and supporting early intervention strategies. This study investigates the performance of Long Short-Term Memory (LSTM) networks for predicting daily dengue infection counts in Bangladesh using surveillance data from 2010 to 2022. Multiple LSTM architectures were developed by varying the number of LSTM units, lookback window size, and data preprocessing strategies. A fixed time based data split was used, with training data up to 30 September 2021 and testing on subsequent observations to avoid information leakage. The proposed LSTM models were compared with several baseline forecasting approaches, including Naive, Seasonal-Naive, and ARIMA models. Model performance was evaluated using standard forecasting metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Symmetric Mean Absolute Percentage Error (SMAPE). Experimental results indicate that classical statistical models remain strong benchmarks for dengue forecasting. Among all evaluated models, the ARIMA(2,1,2) model achieved the best predictive performance on the test dataset with MAE of 8.40 and RMSE of 14.72. The best-performing LSTM configuration produced competitive results with MAE of 8.92 and RMSE of 15.87, outperforming simple baseline approaches such as the Naive and Seasonal-Naive models. These findings suggest that while deep learning models are capable of capturing complex temporal patterns, their advantage over well-tuned statistical models is not guaranteed for this dataset. The results highlight the importance of benchmarking deep learning models against classical time series approaches when forecasting epidemiological data. While LSTM networks demonstrate promising performance in modeling nonlinear temporal dynamics, the ARIMA model provided the most accurate predictions for the studied dengue dataset.

2026-07-17T00:00:00Z Mohammad, Kazi Mehedi Khan, Taufiquar Kamrujjaman, Md. https://repository.auw.edu.bd/handle/123456789/3364 2026-03-03T04:30:51Z 2026-01-13T00:00:00Z

Stochastic Control of Influenza Spread: A Lévy-Driven SDE and Branching Process Approach Mohammad, Kazi Mehedi; Khan, Taufiquar; Kamrujjaman, Md. Background Forecasting influenza outbreaks remains a significant challenge due to the complexity of dis ease transmission and the influence of environmental and behavioral factors. Traditional models based solely on the basic reproduction number (R0) often fall short in capturing the full scope of outbreak dynamics. Methods In this study, we employ a seasonally adjusted SEIRT model incorporating stochastic differ ential equations (SDEs), including Brownian motion and L´evy jump processes, to simulate random and abrupt fluctuations in transmission. A branching process approximation is used to evaluate the probability of an epidemic under the influence of seasonal variability and stochastic perturbations. The model is calibrated using weekly influenza case data from Mexico, with noise components estimated from publicly available CDC [1] and WHO [2] surveillance data. Results Simulation results show that the inclusion of stochastic effects and periodic transmission rates significantly enhances the model’s accuracy in reflecting real-world epidemic dynamics. Numerical comparisons between deterministic, Brownian-based, and L´evy-based scenarios reveal that both the initial state of the exposed or infectious subpopulation and the seasonal transmission patterns are critical to determining outbreak probabilities. Results indicate that seasonal transmission rates and stochastic effects significantly alter epidemic probabilities, with L´evy processes capturing abrupt outbreak dynamics more accurately than deterministic models. Conclusions The findings underscore that deterministic models may underestimate epidemic risk when they overlook random and sudden changes in contact rates or disease introduction. The proposed stochastic modeling framework yields a deeper understanding of influenza transmis sion dynamics by incorporating uncertainty and seasonal variability, thereby supporting more informed and effective public health decision-making.

2026-01-13T00:00:00Z