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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Generation interval contraction and epidemic data analysis.

Eben Kenah1, Marc Lipsitch, James M Robins

  • 1Department of Epidemiology, Harvard School of Public Health, 677 Huntington Ave., Boston, MA 02115, USA. ekenah@hsph.harvard.edu

Mathematical Biosciences
|April 9, 2008
PubMed
Summary
This summary is machine-generated.

The mean generation interval shortens when individuals face multiple infection risks, a phenomenon observed in epidemic models. This study explores "competition" effects influencing disease transmission dynamics.

Related Experiment Videos

Last Updated: Jul 6, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Infectious Disease Modeling

Background:

  • Generation interval is crucial for epidemic models and data analysis.
  • Understanding factors affecting generation interval is key to accurate disease spread prediction.
  • Previous models often assumed independent infection sources.

Purpose of the Study:

  • To investigate how multiple infection sources affect the mean generation interval in a stochastic SIR model.
  • To introduce and illustrate the concepts of global and local competition in infectious disease spread.
  • To explore the use of infectious contact hazards as an alternative to generation intervals for estimating the effective reproductive number.

Main Methods:

  • Specification of a general stochastic SIR (Susceptible-Infected-Recovered) epidemic model.
  • Mathematical proof demonstrating the decrease in mean generation interval with multiple infection sources.
  • Computational simulations to visualize global and local competition effects.
  • Development of a method using hazards of infectious contact.

Main Results:

  • The mean generation interval decreases when susceptible individuals have multiple potential infectors (global competition).
  • Mean generation interval also contracts due to high local infection prevalence within contact clusters (local competition).
  • Simulations confirmed the impact of both global and local competition on generation interval contraction.
  • Hazards of infectious contact provide a viable alternative for estimating the effective reproductive number over time.

Conclusions:

  • Multiple infection sources and clustered transmission significantly shorten the mean generation interval.
  • The concept of competition (global and local) offers new insights into epidemic dynamics.
  • Using infectious contact hazards aligns epidemic analysis with survival analysis methods, suggesting new research directions.