Jennifer O Ahweyevu1, Ngozi P Chukwudebe1, Brittany M Buchanan1, Jingjing Yin1, Bishwa B Adhikari2, Xiaolu Zhou3,4, Zion Tsz Ho Tse5,6, Gerardo Chowell7, Martin I Meltzer2, Isaac Chun-Hai Fung1,2. 1. Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia. 2. Health Economics and Modeling Unit, Division of Preparedness and Emerging Infections, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia. 3. Department of Geology and Geography, College of Science and Mathematics, Georgia Southern University, Statesboro, Georgia. 4. Department of Geography, Texas Christian University, Fort Worth, Texas. 5. School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, Georgia. 6. Department of Electronic Engineering, University of York, Heslington, York, United Kingdom. 7. Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia.
Abstract
OBJECTIVES: To aid emergency response, Centers for Disease Control and Prevention (CDC) researchers monitor unplanned school closures (USCs) by conducting online systematic searches (OSS) to identify relevant publicly available reports. We examined the added utility of analyzing Twitter data to improve USC monitoring. METHODS: Georgia public school data were obtained from the National Center for Education Statistics. We identified school and district Twitter accounts with 1 or more tweets ever posted ("active"), and their USC-related tweets in the 2015-16 and 2016-17 school years. CDC researchers provided OSS-identified USC reports. Descriptive statistics, univariate, and multivariable logistic regression were computed. RESULTS: A majority (1,864/2,299) of Georgia public schools had, or were in a district with, active Twitter accounts in 2017. Among these schools, 638 were identified with USCs in 2015-16 (Twitter only, 222; OSS only, 2015; both, 201) and 981 in 2016-17 (Twitter only, 178; OSS only, 107; both, 696). The marginal benefit of adding Twitter as a data source was an increase in the number of schools identified with USCs by 53% (222/416) in 2015-16 and 22% (178/803) in 2016-17. CONCLUSIONS: Policy-makers may wish to consider the potential value of incorporating Twitter into existing USC monitoring systems.
OBJECTIVES: To aid emergency response, Centers for Disease Control and Prevention (CDC) researchers monitor unplanned school closures (USCs) by conducting online systematic searches (OSS) to identify relevant publicly available reports. We examined the added utility of analyzing Twitter data to improve USC monitoring. METHODS: Georgia public school data were obtained from the National Center for Education Statistics. We identified school and district Twitter accounts with 1 or more tweets ever posted ("active"), and their USC-related tweets in the 2015-16 and 2016-17 school years. CDC researchers provided OSS-identified USC reports. Descriptive statistics, univariate, and multivariable logistic regression were computed. RESULTS: A majority (1,864/2,299) of Georgia public schools had, or were in a district with, active Twitter accounts in 2017. Among these schools, 638 were identified with USCs in 2015-16 (Twitter only, 222; OSS only, 2015; both, 201) and 981 in 2016-17 (Twitter only, 178; OSS only, 107; both, 696). The marginal benefit of adding Twitter as a data source was an increase in the number of schools identified with USCs by 53% (222/416) in 2015-16 and 22% (178/803) in 2016-17. CONCLUSIONS: Policy-makers may wish to consider the potential value of incorporating Twitter into existing USC monitoring systems.
Entities:
Keywords:
digital health; public health surveillance; social distancing; social media
Authors: Ashley M Jackson; Lindsay A Mullican; Zion T H Tse; Jingjing Yin; Xiaolu Zhou; Dharamendra Kumar; Isaac C-H Fung Journal: J Sch Health Date: 2020-05-07 Impact factor: 2.118
Authors: Marcel Salathé; Clark C Freifeld; Sumiko R Mekaru; Anna F Tomasulo; John S Brownstein Journal: N Engl J Med Date: 2013-07-03 Impact factor: 91.245