Use of Twitter data to improve Zika virus surveillance in the United States during the 2016 epidemic

BMC Public Health

Jianfeng Jia¹  Chen Li¹  Guofa Zhou²  Shahir Masri²  Ming-Chieh Lee²  Jun Wu²  Guiyun Yan² 

[1] Department of Computer Science, University of California;Program in Public Health, College of Health Sciences, Uniersity of California;

关键词: Zika;    ZIKV;    Zika virus;    Disease surveillance;    Disease forecasting;    Predictive modeling;   

DOI  :  10.1186/s12889-019-7103-8

来源: DOAJ

【 摘 要 】

Abstract Background Zika virus (ZIKV) is an emerging mosquito-borne arbovirus that can produce serious public health consequences. In 2016, ZIKV caused an epidemic in many countries around the world, including the United States. ZIKV surveillance and vector control is essential to combating future epidemics. However, challenges relating to the timely publication of case reports significantly limit the effectiveness of current surveillance methods. In many countries with poor infrastructure, established systems for case reporting often do not exist. Previous studies investigating the H1N1 pandemic, general influenza and the recent Ebola outbreak have demonstrated that time- and geo-tagged Twitter data, which is immediately available, can be utilized to overcome these limitations. Methods 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 1 week in advance. Results While models tended to over-predict at low case counts and under-predict at extreme high counts, a comparison of predicted versus observed weekly ZIKV case counts following model calibration demonstrated overall reasonable predictive accuracy, with an R2 of 0.74 for the Florida model and 0.70 for the U.S. model. Time-series analysis of predicted and observed ZIKV cases following internal cross-validation exhibited very similar patterns, demonstrating reasonable model performance. Spatially, the distribution of cumulative ZIKV case counts (local- & travel-related) and zika tweets across all 50 U.S. states showed a high correlation (r = 0.73) after adjusting for population. Conclusions 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.

【 授权许可】

Unknown