Confidence in the dynamic spread of epidemics under biased sampling conditions

The interpretation of sampling data plays a crucial role in policy response to the spread of a disease during an epidemic, such as the COVID-19 epidemic of 2020. However, this is a non-trivial endeavor due to the complexity of real world conditions and limits to the availability of diagnostic tests,...

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Bibliographic Details
Published inPeerJ (San Francisco, CA) Vol. 8; p. e9758
Main Authors Brunner, James, Chia, Nicholas
Format Journal Article
LanguageEnglish
Published San Diego PeerJ. Ltd 14.08.2020
PeerJ, Inc
PeerJ Inc
Subjects
Asymptomatic
Bias
Coronaviruses
COVID-19
Data analysis
Disease spread
Disease transmission
Epidemic Sampling
Epidemics
Epidemiology
Infectious Diseases
Information management
Medical research
Ordinary differential equations
Population
Population Biology
Population modeling
Public Health
Sampling
Statistics
Stochastic models
Stochasticity
Trends
United States > US
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Summary:The interpretation of sampling data plays a crucial role in policy response to the spread of a disease during an epidemic, such as the COVID-19 epidemic of 2020. However, this is a non-trivial endeavor due to the complexity of real world conditions and limits to the availability of diagnostic tests, which necessitate a bias in testing favoring symptomatic individuals. A thorough understanding of sampling confidence and bias is necessary in order make accurate conclusions. In this manuscript, we provide a stochastic model of sampling for assessing confidence in disease metrics such as trend detection, peak detection and disease spread estimation. Our model simulates testing for a disease in an epidemic with known dynamics, allowing us to use Monte-Carlo sampling to assess metric confidence. This model can provide realistic simulated data which can be used in the design and calibration of data analysis and prediction methods. As an example, we use this method to show that trends in the disease may be identified using under 10,000 biased samples each day, and an estimate of disease spread can be made with additional 1,000–2,000 unbiased samples each day. We also demonstrate that the model can be used to assess more advanced metrics by finding the precision and recall of a strategy for finding peaks in the dynamics.
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ISSN:2167-8359
2167-8359
DOI:10.7717/peerj.9758