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Inferring generation-interval distributions from contact-tracing data.

Sang Woo Park1,2, David Champredon3,4, Jonathan Dushoff3,5

  • 1Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.

Journal of the Royal Society, Interface
|June 24, 2020
PubMed
Summary
This summary is machine-generated.

Understanding epidemic spread relies on generation intervals. This study reveals how to correct these intervals for observation time, accurately linking epidemic growth rates and reproductive numbers for better disease modeling.

Keywords:
basic reproductive numbercontact tracinggeneration intervalinfectious disease modellingpopulation structure

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Statistical Modeling

Background:

  • Generation intervals are crucial for understanding epidemic dynamics, linking initial reproductive number (R0) and exponential growth rate (r).
  • Realized generation intervals, measured via contact tracing, can differ from intrinsic intervals due to various factors.
  • Factors like temporal truncation and susceptible depletion influence observed generation intervals.

Purpose of the Study:

  • To investigate the differences between realized and intrinsic generation intervals.
  • To identify and quantify spatial and temporal effects on generation intervals.
  • To develop and validate methods for correcting generation intervals for accurate epidemic parameter estimation.

Main Methods:

  • Analysis of spatial and temporal effects on generation intervals.
  • Development of statistical methods for temporal correction of generation intervals.
  • Validation using individual-based simulations on an empirical network.

Main Results:

  • Realized generation intervals are influenced by temporal truncation and spatial population structure.
  • Correcting for temporal truncation, but not spatial effects, yields the initial forward generation-interval distribution.
  • This corrected distribution accurately links the initial reproductive number and exponential growth rate.

Conclusions:

  • Temporal correction of generation intervals is essential for accurate epidemic modeling.
  • The proposed method provides a spatially informed generation-interval distribution.
  • This approach improves the estimation of key epidemic parameters like R0 and r early in an outbreak.