Selected article for: "public health and selection bias"
Author: Bradley, V. C.; Kuriwaki, S.; Isakov, M.; Sejdinovic, D.; Meng, X.-L.; Flaxman, S.
Title: Are We There Yet? Big Data Significantly Overestimates COVID-19 Vaccination in the US
  • Cord-id: j6o3dcr7
  • Document date: 2021_6_15
    • ID: j6o3dcr7
    Snippet: 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 o
    Document: 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.

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