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Full-Text Articles in Biostatistics
Multi-Level Small Area Estimation Based On Calibrated Hierarchical Likelihood Approach Through Bias Correction With Applications To Covid-19 Data, Nirosha Rathnayake
Multi-Level Small Area Estimation Based On Calibrated Hierarchical Likelihood Approach Through Bias Correction With Applications To Covid-19 Data, Nirosha Rathnayake
Theses & Dissertations
Small area estimation (SAE) has been widely used in a variety of applications to draw estimates in geographic domains represented as a metropolitan area, district, county, or state. The direct estimation methods provide accurate estimates when the sample size of study participants within each area unit is sufficiently large, but it might not always be realistic to have large sample sizes of study participants when considering small geographical regions. Meanwhile, high dimensional socio-ecological data exist at the community level, providing an opportunity for model-based estimation by incorporating rich auxiliary information at the individual and area levels. Thus, it is critical …
Limited Early Warnings And Public Attention To Coronavirus Disease 2019 In China, January–February, 2020: A Longitudinal Cohort Of Randomly Sampled Weibo Users, Yuner Zhu, King-Wa Fu, Karen A. Grépin, Hai Liang, Isaac Fung
Limited Early Warnings And Public Attention To Coronavirus Disease 2019 In China, January–February, 2020: A Longitudinal Cohort Of Randomly Sampled Weibo Users, Yuner Zhu, King-Wa Fu, Karen A. Grépin, Hai Liang, Isaac Fung
Department of Biostatistics, Epidemiology, and Environmental Health Sciences Faculty Publications
Objective:
Awareness and attentiveness have implications for the acceptance and adoption of disease prevention and control measures. Social media posts provide a record of the public’s attention to an outbreak. To measure the attention of Chinese netizens to coronavirus disease 2019 (COVID-19), a pre-established nationally representative cohort of Weibo users was searched for COVID-19-related key words in their posts.
Methods:
COVID-19-related posts (N = 1101) were retrieved from a longitudinal cohort of 52 268 randomly sampled Weibo accounts (December 31, 2019–February 12, 2020).
Results:
Attention to COVID-19 was limited prior to China openly acknowledging human-to-human transmission on …