Are We There Yet? Big Data Significantly Overestimates COVID-19 Vaccination in the US
Are We There Yet? Big Data Significantly Overestimates COVID-19 Vaccination in the US
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DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
S. Flaxman
中科院分区：
文献类型：
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作者：
V. Bradley;Shiro Kuriwaki;Michael Isakov;D. Sejdinovic;X. Meng;S. Flaxman
控制COVID-19大流行的公共卫生工作依赖于准确的调查。然而，从Delphi-Facebook、Census Household Pulse和Axios-Ipsos调查中对美国疫苗摄取的估计显示了大数据悖论：调查规模越大，其估计与疾病控制和预防中心（CDC）提供的基准越远。2021年4月，规模最大的Delphi-Facebook调查将疫苗接种率高估了20个百分点。没有基准的疫苗意愿和犹豫估计之间的差异也会随着时间的推移而扩大，不能仅仅通过传统人口变量的选择偏差来解释。然而，最近关于调查大数据质量的框架（b孟，应用统计年鉴，2018）使我们能够量化影响因素，并为疫苗意愿和犹豫提供数据质量驱动的情景分析。
Public health efforts to control the COVID-19 pandemic rely on accurate surveys. However, estimates of vaccine uptake in the US from Delphi-Facebook, Census Household Pulse, and Axios-Ipsos surveys exhibit the Big Data Paradox: the larger the survey, the further its estimate from the benchmark provided by the Centers for Disease Control and Prevention (CDC). In April 2021, Delphi-Facebook, the largest survey, overestimated vaccine uptake by 20 percentage points. Discrepancies between estimates of vaccine willingness and hesitancy, which have no benchmarks, also grow over time and cannot be explained through selection bias on traditional demographic variables alone. However, a recent framework on investigating Big Data quality (Meng, Annals of Applied Statistics, 2018) allows us to quantify contributing factors, and to provide a data quality-driven scenario analysis for vaccine willingness and hesitancy.
影响因子：
1.8
作者：
Meng, Xiao-Li
通讯作者：
Meng, Xiao-Li
影响因子：
64.8
作者：
Galesic, Mirta;de Bruin, Wandi Bruine;van Der Does, Tamara
通讯作者：
van Der Does, Tamara