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Enhanced Dengue Fever Prediction in India through Deep Learning with Spatially Attentive LSTMs

Year: 2025

Type: article

Abstract: This research intends to forecast dengue fever occurrences in India using machine learningmethods. A dataset comprising weekly dengue occurrences at the state level in India from 2017 to2024 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 assessedfor 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 adataset of monthly dengue occurrences in India from 2017 to 2024, aiming to predict the number ofdengue cases using various climate, topographic, demographic, and land-use factors.

Source: Cuestiones de Fisioterapia

DOI: 10.48047/v3dm7y10

Authors: Usha D, Jesmalar L, Noorbasha Nagoor Meeravali, Mihirkumar B.Suthar, Rajeswari J, Pothumarthi Sridevi, Vengatesh T

Institutions: Dr. M.G.R. Educational and Research Institute, Koneru Lakshmaiah Education Foundation

Language: English

Topic: Mosquito-borne diseases and control

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4407924588

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