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Operational analysis for COVID-19 testing: Determining the risk from asymptomatic infections
Marc Mangel1,2,3
1Department of Biology, University of Bergen, Bergen, Norway.
Plos One
|February 13, 2023
Summary
Testing accuracy during pandemics is crucial. This study presents a method to translate observed positive test rates into true infection incidence, accounting for asymptomatic cases and informing risk assessment in groups.
Area of Science:
- Epidemiology
- Public Health Policy
- Mathematical Modeling
Background:
- Pandemic health management relies heavily on testing.
- Test inaccuracies (false positives/negatives) create a gap between observed and true infection rates.
Purpose of the Study:
- To illustrate and extend a method for estimating true disease incidence from observed test positivity.
- To incorporate asymptomatic infections into testing models.
- To refine risk assessment for group testing strategies.
Main Methods:
- Utilized analytical methods and Monte Carlo simulations to model the testing process.
- Developed a framework to translate surface positivity to incidence rate estimates.
- Extended the model to include asymptomatic infections and symptom status.
Main Results:
- Demonstrated that observed positivity rate differs from true incidence rate.
- Showed that symptom status and time since exposure provide valuable data for accurate estimates.
- Established that risk is a continuous function of group size, not binary.
- Identified the potential for over-testing in fixed regions due to diminishing returns of increased testing.
Conclusions:
- Positivity rate is not a direct measure of incidence rate.
- Symptom status and exposure history are critical for refining epidemiological estimates.
- Risk assessment and testing strategies must consider group size and the potential for diminishing returns.
