Masri S, Jia J, Li C, Zhang X, Carey MJ, Su S, Wu J. 2019. Interactive analytics and visualization of Zika epidemics using Tweets. BMC Public Health: Accepted.

In this study, we employed a recently developed system called Cloudberry to filter a random sample of Twitter data to investigate the feasibility of using such data for ZIKV epidemic tracking on a national and state (Florida) level. Two auto-regressive models were calibrated using weekly ZIKV case counts and zika tweets in order to estimate weekly ZIKV cases one week in advance. This study demonstrates the value of utilizing Twitter data for the purposes of disease surveillance. This is of high value to epidemiologist and public health officials charged with protecting the public during future outbreaks.

Interactive analytics and visualization of Zika epidemics using Tweets.