Deep recurrent models for forecasting infectious diseases

The experimental results demonstrate that BiLSTM returns the highest F1-score of 0.83 for the term "COVID", while LSTM and GRU reach 0.73 and 0.77, respectively. Moreover, BiLSTM outperforms LSTM and GRU at all early time lags for the search terms "fever" and "cough". The results reveal the F1-scores for the term "fever" to be 0.77, 0.62, and 0.5 for BiLSTM, GRU, and LSTM, respectively. Whereas, the F1-scores for the search term "cough" are 0.62, 0.62, and 0.5 for BiLSTM, GRU, and LSTM, respectively. Although BiLSTM incurs higher computational costs, LSTM and GRU offer efficient alternatives to deliver rapid execution. These results highlight the effectiveness of deep learning models in instances of early anomaly detection, supporting timely healthcare interventions and advancing the development of real-time monitoring systems.