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Accounting for incomplete testing in the estimation of epidemic parameters
1Department of Biostatistics, New York University School of Global Public Health.
Medrxiv : the Preprint Server for Health Sciences
|June 9, 2020
Summary
Understanding COVID-19 spread requires accurate epidemic parameter estimates. This study clarifies how testing impacts these estimates, providing adjusted figures for New York City to correct for testing biases.
Area of Science:
- Epidemiology
- Infectious Disease Modeling
- Public Health
Background:
- The COVID-19 pandemic necessitates real-time understanding of epidemic dynamics and intervention effectiveness.
- Infectious disease epidemiology utilizes advanced modeling for epidemic parameter estimation, such as doubling time.
- Current estimation methods are often biased due to variations in community testing strategies.
Approach:
- Developed a model to clarify the functional relationship between testing dynamics and epidemic parameters.
- Performed sensitivity analyses to explore the impact of varying testing strategies on estimates.
- Derived adjustments for epidemic parameter estimates specific to New York City's testing data.
Key Points:
- Crude estimates of epidemic parameters are sensitive to underlying community testing strategies.
- Assuming stable or complete testing can lead to biased estimations of epidemic spread.
- The study provides a method to adjust estimates based on actual testing dynamics.
Conclusions:
- Testing strategies significantly influence the accuracy of epidemic parameter estimates.
- Assumptions of stable or complete testing can lead to biased estimations of epidemic spread.
- Accurate real-time assessment of pandemics requires explicit consideration of testing dynamics in epidemiological models.
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