In this work, an "analytical data model of mosquito vector" was developed to perform analytical computation to the character of the dengue vectors. Our goal is to investigate a way to understand how the temporal trend of collected dataset correlates with the incidence dengue as identified by national health authorities. Based upon the mosquito-vector big data collections, we investigate how changes in some specific variables such as rainfall, temperature, and humidity can dramatically affect the population of mosquito vectors, in order to provide early warnings of dengue outbreaks. Thus, our system will collectively collect online sensing data of the variables and store them in a database, in order to combine the historical big data as training datasets for analytical computation. Also, the developed model is able to merge the experimental datasets with current hot-topic information which is relevant to mosquito vectors obtained from data of social sensors (i.e. social messages). The experimental data show that our system is of great potentials in providing early warnings of dengue outbreaks.
['Chung-Hong Lee', 'Hsin-Chang Yang', 'Shih-Jan Lin']
Incorporating Big Data and Social Sensors in a Novel Early Warning System of Dengue Outbreaks