The analysis of urban health with integrating crowdsourcing data

Abstract

Dengue is a vector born disease transmitted by Aedes aegypti mosquito. It has seen an increase in the number of cases in Southeast Asia and Malaysia. The burden of dengue fever outbreaks is causing the loss of lives and unnecessary waste of resources to the authorities. There have been numerous research work studying the variables involved in the life-cycle of the dengue spread. Variables mainly related to the vector elements such as being an “Urban Disease”, weather patterns (temperature and rain), moon phases and land use. In this context, the easy access to internet technology through smartphone`s and lowest cost of devices worldwide opens the way to new possibilities for data collection through volunteered geographic information. The aim of this study is to develop an urban health system to generate an index based on factors influencing the dengue outbreaks and study the interest by the citizens to be involved and transmitting data using smartphones using an App. The data could in a later stage be integrated into the developed system. The developed index was generated by combining spatial and temporal factors consisting of land use, temperature, rainfall, moon phases. The app would transmit the data comprising of the existence of mosquito larvae, rubbish, known dengue cases in the neighborhood, the vector bites pattern including the location and the date. This study was restricted geographically to the areas of Selangor state in Malaysia and the federal territories of Kuala Lumpur and Putrajaya for using data for the years of 2014. The findings show that, after generating the dengue index for the years of 2014 and 2015, it was observed from the time series that the generated dengue index reflected the fluctuation in the number of cases 52 days in average before the occurrence of the actual number of cases. Finally, the results also show that the number of cases of dengue increased during the new moon phase every lunar month and the cases and sites increased during raining seasons with little rain and high temperatures while they would decrease during raining seasons with lower temperatures. Crowdsourcing data from volunteers were received from users within Malaysia and other countries as well, however, the data was out of the temporal frame with the dengue data used in this research. In conclusion, this research two forewarning cycles that have been identified on upcoming dengue outbreaks; a short cycle coinciding with the moon phases while a longer cycle coinciding with the weather, land use variables. Finally, data obtained from crowdsourcing in this study shows that even if there was no advertising about the developed app, there was an interest drawn in Malaysia and outside the country to contribute voluntarily with information. This data could be integrated with the index in the future to assist relevant authorities to pinpoint spatially the locations to intervene ahead of probable dengue outbreaks.