A crowdsensing algorithm for imputing Zika outbreak location data

Abstract

The Internet of Things is becoming an integral part of today's solutions to critical issues. In this paper, we consider applications to the field of Zika outbreaks. Current solutions are limited to preventative measures such as spraying pesticides, destruction of mosquito breeding grounds, and avoiding the outdoors in the evening. However, these current methods have significant limitations because the geographic areas of Zika-carrying mosquito infestation are not known in fine-grained detail and testing for these locations is difficult. However, through crowdsensing techniques there are ways to better identify and narrow location determination. Devices such as smartphones are very common among the majority of citizens, and these devices can collect a plethora of information. This paper will focus on the use of crowdsensing techniques coupled with medical professional's diagnosis of Zika virus to impute possible vector data to provide more fine-grained and sophisticated location determination for Zika outbreaks.