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Interpretations and pitfalls in modelling vector-transmitted infections.

M Amaku1, F Azevedo2, M N Burattini2

  • 1School of Veterinary Medicine, University of São Paulo,São Paulo,SP,Brazil.

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|November 25, 2014
PubMed
Summary
This summary is machine-generated.

Mathematical models are crucial for evaluating vector-borne infection control strategies. This paper debates model complexity, pitfalls in prediction models, and inappropriate assumptions in intuition-checking models for diseases like dengue.

Keywords:
Denguemathematical modellingvector-borne infections

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • Mathematical models are increasingly used to evaluate control strategies for vector-borne infections.
  • The complexity of these models must align with their intended purpose, whether for intuition checking or future prediction.
  • Potential pitfalls exist in the application and interpretation of these models.

Purpose of the Study:

  • To debate the role and appropriate complexity of mathematical models in evaluating vector-borne infection control.
  • To examine potential pitfalls associated with different classes of mathematical models.
  • To discuss the limitations of specific models, including those with inappropriate biological assumptions for diseases like dengue.

Main Methods:

  • Analysis and critique of two distinct classes of mathematical models: those for intuition checking and those for prediction.
  • Simplification of complex mathematical models for vector-borne infections.
  • Examination of a predictive model from recent literature to highlight dangers of unconsidered limitations.
  • Review of an intuition-checking model with potentially inappropriate biological assumptions for dengue.

Main Results:

  • Models intended for prediction require significant complexity and carry risks if their limitations are ignored.
  • Models designed to check intuition may contain biological assumptions unsuitable for specific diseases, such as dengue.
  • Simplifying complex models is essential for clear communication and application.

Conclusions:

  • Mathematical models are valuable tools but require careful consideration of their complexity, limitations, and biological relevance.
  • Misgivings regarding the application of mathematical modeling in public health decision-making are presented for further debate.
  • The choice of model must be carefully matched to the research question and the specific disease context.