Enhanced Dengue Fever Prediction in India through Deep Learning with Spatially Attentive LSTMs
DOI:
https://doi.org/10.48047/v3dm7y10Keywords:
Dengue fever; LSTM; spatial attention; temporal attention; IndiaAbstract
This research intends to forecast dengue fever occurrences in India using machine learning
methods. A dataset comprising weekly dengue occurrences at the state level in India from 2017 to
2024 was sourced from the India Open Data website and contains factors such as climate, geography,
and demographics. Six distinct long short-term memory (LSTM) models were created and assessed
for dengue forecasting in India: LSTM, stacked LSTM (S-LSTM), LSTM with temporal attention
(TA-LSTM), S-LSTM with temporal attention (STA-LSTM), LSTM with spatial attention (SALSTM), and S-LSTM with spatial attention (SSA-LSTM). The models were trained and tested on a
dataset of monthly dengue occurrences in India from 2017 to 2024, aiming to predict the number of
dengue cases using various climate, topographic, demographic, and land-use factors.
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