Chris Overton: Investigating how the epidemic phase biases the severity risk of observed cases
Channel: NorthWestBiomathsDataScienceSeminars
Date: 2022-01-05

North West Seminar Series of Mathematical Biology and Data Science 

Monday, 22nd November 2021 (hosted by Carl Whitfield) 

https://www.cms.livjm.ac.uk/APMSeminar/

Abstract:
Monitoring the risk of severe outcomes during an infectious disease outbreak is important for informing the public health response. However, it has recently been shown that using case-severity fluctuates with the phase of the epidemic: case-severity increases during phases of epidemic growth and decreases during phases of epidemic decline. This leads to unbiased estimators of case-severity being biased estimators of the underlying infection-severity. Using a simple epidemic model, we investigate this epidemic phase bias. We prove the existence and direction of this bias, and analyse the performance of a method that attempts to correct for this bias. This bias has led to challenges during the COVID-19 pandemic. Here, researchers have attempted to compare the infection-severity risk of different SARS-CoV-2 variants. However, these attempts are skewed by the epidemic phase bias. Applying the model to a COVID-19 inspired scenario, we show that the epidemic phase bias could lead to at least a 50% higher case-severity risk in a growing variant relative to a declining variant, regardless of any underlying change in infection-severity, which would skew any relative risk estimates. By raising awareness of this bias and the correction methods, future research should be able to address this challenge when comparing different variants of a pathogen.