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Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models
Dominic G Whittaker1, Alejandra D Herrera-Reyes1, Maurice Hendrix2
1School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
Journal of Theoretical Biology
|November 9, 2022
Summary
Epidemic models used for policy-making can produce substantial errors if they mis-specify host infectiousness and infection-to-death time distributions. Incorporating 'infected age' improves accuracy for crucial metrics like the reproduction number.
Area of Science:
- Epidemiology
- Mathematical Biology
- Public Health Modelling
Background:
- Epidemic models are crucial for policy decisions during pandemics like SARS-CoV-2.
- Increased scrutiny highlights the need for accurate model assumptions and uncertainty quantification.
Purpose of the Study:
- To investigate the impact of mis-specifying host infectiousness and infection-to-death time distributions in epidemic models.
- To introduce and evaluate an 'infected age'-structured SIR model for improved accuracy.
Main Methods:
- Developed an SIR-type model incorporating 'infected age' (days since infection).
- Utilized a Bayesian approach for uncertainty quantification.
- Applied the model to synthetic and real UK data (Feb-Jul 2020).
Main Results:
- Models without 'infected age' lead to significant errors in key policy-relevant parameters (e.g., reproduction number).
- Accurate distributional assumptions are vital for reliable epidemic inference.
- Bayesian uncertainty quantification highlights misleading results from simplified models.
Conclusions:
- Accurate representation of host infectiousness and infection-to-death times is critical for robust epidemic modeling.
- The 'infected age' model structure provides a more clinically consistent and accurate approach.
- Neglecting uncertainty and distributional assumptions can lead to flawed policy recommendations.
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