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NCKU Launches “Mosquito Man” Cloud Platform to Raise Awareness of Dengue Fever

Published: Oct 04,2016

TAINAN, Taiwan - A cloud analytic framework for dengue fever designed by a research team from Department of Electrical and Computer Sciences, National Cheng Kung University (NCKU), Tainan, Taiwan, helped the Tainan City Government to control the outbreak of dengue fever effectively. The university recently launched another new cloud platform named “Mosquito Man” to help raise deeper understanding and awareness of dengue fever.

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A press conference was held on September 2 to announce the kickoff of the “Mosquito Man” cloud platform which is the result of interdisciplinary efforts from computer sciences, engineering and medicine in the university.

NCKU President Huey-Jen Jenny Su hosted the conference and extended her warm welcome to the guests. Tainan City Mayor Lai Ching-te and Tainan DOH Director Lin Sheng-che attended the conference to show their support.

The “Mosquito Man” cloud platform provides real-time forecasts of epidemic spread, information from clinics offering rapid screening, reports and feature stories about dengue fever, and QA service.

Another fun feature of “Mosquito Man” is a game which allows the users to play and learn more information about the disease and the prevention measures.

Dr. Chuang Kun-ta, a professor of electrical and computer Sciences, employed big data and web visualization to illustrate spatial temporal dispersion of dengue fever and hot spot detection in Tainan City.

He set up an organization called Taiwanstat where a map of dengue transmission in Tainan City is shown to illustrate and visualize the prevalence of dengue fever and abundance of their vectors.

The variation of determinants for dengue infection in space and time could be taken into account when designing local dengue control programs, Chuang said.

On the website of Taiwanstat, the spatial and temporal clustering of dengue virus transmission in the districts of Tainan City is observed, demonstrating variation in local infection patterns.

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