Deep Learning Based Forecasting Models of Dengue Outbreak in Bangladesh: Comparative Analysis of LSTM, RNN, and GRU Models Using Multivariate Variables with a Two-Decade Dataset

In 2023, Bangladesh experienced its most severe dengue outbreak since 2000, necessitating improved prediction methods. This study employs diverse machine-learning techniques to forecast dengue fever occurrences in Bangladesh, aiming for enhanced accuracy and proactive public health measures. Unlike previous research focusing narrowly on specific variables, this study incorporates a wider range of factors and algorithms. Three models: Gated Recurrent Units (GRU), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) were evaluated using data from January 2000 to December 2022. Official patient data from the Ministry of Health and Family Welfare and meteorological data from the Bangladesh Meteorological Department were utilized. LSTM exhibited the highest accuracy at $87.98 %$, surpassing GRU at $79.81 %$, while RNN trailed at $57.18 %$. This underscores LSTM's efficacy in predicting dengue cases in Bangladesh, offering valuable insights for proactive disease management and public health interventions.