Document Type
Article
Publication Date
1-1-2020
Department
Political Science, International Development, and International Affairs
School
Social Science and Global Studies
Abstract
© 2013 IEEE. The mosquito-borne dengue fever is a major public health problem in tropical countries, where it is strongly conditioned by climate factors such as temperature. In this paper, we formulate a holistic machine learning strategy to analyze the temporal dynamics of temperature and dengue data and use this knowledge to produce accurate predictions of dengue, based on temperature on an annual scale. The temporal dynamics are extracted from historical data by utilizing a novel multi-stage combination of auto-encoding, window-based data representation and trend-based temporal clustering. The prediction is performed with a trend association-based nearest neighbour predictor. The effectiveness of the proposed strategy is evaluated in a case study that comprises the number of dengue and dengue hemorrhagic fever cases collected over the period 1985-2010 in 32 federal states of Mexico. The empirical study proves the viability of the proposed strategy and confirms that it outperforms various state-of-the-art competitor methods formulated both in regression and in time series forecasting analysis.
Publication Title
IEEE Access
Volume
8
First Page
52713
Last Page
52725
Recommended Citation
Appice, A.,
Gel, Y.,
Iliev, I.,
Lyubchich, V.,
Malerba, D.
(2020). A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico. IEEE Access, 8, 52713-52725.
Available at: https://aquila.usm.edu/fac_pubs/17904
Comments
Published by IEEE Access at 10.1109/access.2020.2980634.