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Misspecification of the generation time distribution and its impact on Rt estimates in structured populations
Ioana Bouros1, Robin N Thompson2, David Gavaghan3
1Department of Computer Science, University of Oxford, Oxford, UK; Department of Pathobiology & Population Sciences, Royal Veterinary College, London, UK.
Journal of Theoretical Biology
|June 23, 2026
Summary
Estimating the time-dependent reproduction number (Rt) requires careful consideration of population structure. Our study shows that assuming a homogeneous population can lead to inaccurate Rt estimates, impacting public health policy.
Area of Science:
- Epidemiology
- Mathematical Biology
- Public Health
Background:
- The time-dependent reproduction number (Rt) is crucial for tracking infectious disease spread and intervention effectiveness.
- Renewal equations are common models for Rt inference, assuming a uniform generation time distribution across populations.
Purpose of the Study:
- To compare Rt estimates from homogeneous and structured population models.
- To investigate conditions where these models yield different conclusions.
- To develop a method for adapting single-group models for structured populations and assess real-world data.
Main Methods:
- Developed and compared two Rt inference frameworks: one for homogeneous groups and one for structured populations.
- Employed analytical methods and simulations to evaluate model performance.
- Utilized real epidemic data to demonstrate practical differences in Rt estimates.
Main Results:
- Rt estimates can differ significantly depending on whether a homogeneous or structured population model is used.
- A methodology was developed to adapt single-group models for structured populations, improving Rt inference accuracy.
- Real epidemic data confirmed discrepancies between one-group and multi-group model estimates.
Conclusions:
- Accurate Rt estimation necessitates accounting for population structure and variations in generation time.
- Rigorously collected epidemic data, considering subgroup differences, is vital for effective public health policy.
- The choice of modeling paradigm significantly influences Rt estimates and subsequent public health decisions.
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