Towards a machine learning-based approach to forecasting Dengue virus outbreaks in Colombian cities: a case-study: Medellin, Antioquia

Alberto M. Ceballos-Arroyo; Daniel Maldonado-Perez; Hugo Mesa-Yepes; Laura Perez; Karl Ciuoderis; Guillermo Comach; Jorge E. Osorio-Benitez; Juan P. Hernandez-Ortiz; John W. Branch-Bedoya

doi:10.1117/12.2547001

3 January 2020 Towards a machine learning-based approach to forecasting Dengue virus outbreaks in Colombian cities: a case-study: Medellin, Antioquia

Alberto M. Ceballos-Arroyo, Daniel Maldonado-Perez, Hugo Mesa-Yepes, Laura Perez, Karl Ciuoderis, Guillermo Comach, Jorge E. Osorio-Benitez, Juan P. Hernandez-Ortiz, John W. Branch-Bedoya

Author Affiliations +

Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 1133016 (2020) https://doi.org/10.1117/12.2547001
Event: 15th International Symposium on Medical Information Processing and Analysis, 2019, Medelin, Colombia

Abstract

The geographical conditions of Colombia favor the re-emergence and propagation of infectious tropical diseases. Among them, the Dengue virus is highly endemic throughout the country, thereby locating this arbovirus as one of the major pathologies of the public health system. Therefore, there is a global challenge to generate novel strategies to predict and control dengue virus transmission. In particular, during the Colombian 2016 Dengue outbreak, more than 17 thousand Dengue cases were reported by the health authorities of Medellin, Antioquia. In this paper, we present a machine learning approach for the early detection of dengue outbreaks in the city of Medellin. We use an artificial neural network as the core of the machine learning algorithm, with environmental, meteorological and epidemiological data from the National Institute of Health –SIVIGILA– and the Aburra Valley Early Warning System –SIATA–. Our objective is to identify possible Dengue outbreaks, i.e. to create an early warning system, to provide a preventive timeline to the health authorities to design contingency plans and to mitigate the impact of dengue on the population of Medellin. Our results indicate that a artificial neural network forecasting for time series shows a trend for the correct prediction of dengue cases up to the first four weeks with a deterioration in precision as the forecast is pushed for additional ten to twenty weeks.

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Alberto M. Ceballos-Arroyo, Daniel Maldonado-Perez, Hugo Mesa-Yepes, Laura Perez, Karl Ciuoderis, Guillermo Comach, Jorge E. Osorio-Benitez, Juan P. Hernandez-Ortiz, and John W. Branch-Bedoya "Towards a machine learning-based approach to forecasting Dengue virus outbreaks in Colombian cities: a case-study: Medellin, Antioquia", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 1133016 (3 January 2020); https://doi.org/10.1117/12.2547001

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KEYWORDS

Data modeling

Neural networks

Climatology

Humidity

Atmospheric modeling

Neurons

Temperature metrology

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