Abstract:
Dengue menace, which has been present in Sri Lanka since the early 1960s, has become a major public health concern. Climatic factors such as average rainfall, minimum tem...Show MoreMetadata
Abstract:
Dengue menace, which has been present in Sri Lanka since the early 1960s, has become a major public health concern. Climatic factors such as average rainfall, minimum temperature, and relative humidity greatly influence the epidemiological pattern of Dengue. The objective of this study is to develop an effective model to forecast future Dengue propagation in Colombo district in Sri Lanka, with the intention of raising awareness amongst the authorities and the public to take necessary steps to minimize the spread. In this paper, Long Short-Term Memory based few-shot learning (LSTM-FSL) model which makes use of limited data in the target task is proposed. The proposed model is compared with the Naive LSTM model and Vector Autoregression (VAR) model in terms of Mean Absolute Deviation (MAD) and Root Mean Square Error (RMSE). The LSTM-FSL model performs with an RMSE of 108.62 and MAD of 97.64, outperforming the other compared models.
Published in: 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS)
Date of Conference: 11-13 August 2021
Date Added to IEEE Xplore: 10 November 2021
ISBN Information:
Electronic ISSN: 2151-1810