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Robust parameter estimation techniques for stochastic within-host macroparasite models.

Steven Riley1, Christl A Donnelly, Neil M Ferguson

  • 1Department of Infectious Disease Epidemiology, Imperial College London, St. Mary's Campus, Norfolk Place, W2 1PG London, UK. s.riley@imperial.ac.uk

Journal of Theoretical Biology
|November 15, 2003
PubMed
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This study models lymphatic filariasis (LF) parasite load dynamics within hosts. Random immune response variations, not host differences, explain parasite aggregation, crucial for understanding LF transmission.

Area of Science:

  • Mathematical modeling
  • Immunology
  • Parasitology

Background:

  • Lymphatic filariasis (LF) exhibits significant parasite load variation among infected individuals.
  • Understanding within-host dynamics is crucial for LF control strategies.

Purpose of the Study:

  • To develop and analyze a stochastic model for within-host LF population dynamics.
  • To estimate immunological parameters using a goodness-of-fit (GOF) method.
  • To investigate the sources of parasite load aggregation.

Main Methods:

  • Stochastic modeling of parasite population dynamics.
  • Simulated goodness-of-fit (GOF) for parameter estimation.
  • Comparison of stochastic model with deterministic moment closure approximations.

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Main Results:

  • Deterministic closure approximations did not accurately reproduce stochastic model behavior.
  • The stochastic model successfully reproduced observed data variation without inter-host parameter variability.
  • Parasite load aggregation can be explained by random variations in immune response development.

Conclusions:

  • Stochasticity in immune response development is a key driver of parasite load aggregation in LF.
  • The findings highlight the importance of considering random processes in within-host parasite dynamics.
  • This model provides a framework for estimating immunological parameters in LF.