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ADDRESSING SELECTION BIAS AND MEASUREMENT ERROR IN COVID-19 CASE COUNT DATA USING AUXILIARY INFORMATION
1DEPARTMENT OF BIOSTATISTICS, UNIVERSITY OF MICHIGAN, ANN ARBOR, MI 48109.
The Annals of Applied Statistics
|June 28, 2024
Summary
Limited COVID-19 testing data is hampered by measurement error and selection bias. This study introduces a doubly robust method to correct these biases, improving the accuracy of epidemiological forecasts and prevalence estimations.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Coronavirus case-count data significantly influences public health policies and epidemiological modeling.
- Limited testing capacity and inherent biases (measurement error, selection bias) hinder accurate understanding of the COVID-19 pandemic.
- Increased testing alone does not fully resolve issues of measurement error and selection bias.
Purpose of the Study:
- To quantify the impact of measurement error and selection bias on COVID-19 prevalence and reproduction number estimations.
- To develop a novel, doubly robust estimation method addressing both measurement error and selection bias.
- To apply the developed method for estimating active infection prevalence in Indiana.
Main Methods:
- Demonstrated that large-scale molecular test data can have similar mean square error as small random samples due to biases.
- Developed a procedure combining case-count data and random samples to estimate selection propensities using covariate information.
- Constructed a doubly robust estimation method integrating selection propensities with epidemiological forecast models.
Main Results:
- Estimates from millions of molecular tests showed comparable mean square error to small random samples, highlighting bias issues.
- The doubly robust method was applied to Indiana data, integrating case counts, hospitalizations, deaths, demographics, a random molecular sample, and survey data.
- Accurate estimation of active infection prevalence was achieved by accounting for measurement error and selection bias.
Conclusions:
- Measurement error and selection bias are critical limitations in COVID-19 data, impacting policy and forecasting.
- The proposed doubly robust method offers a more accurate approach to estimating disease prevalence and understanding pandemic dynamics.
- Recommendations are provided based on the methodology to improve future epidemiological surveillance and modeling.
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