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An outbreak vector-host epidemic model with spatial structure: the 2015-2016 Zika outbreak in Rio De Janeiro.

W E Fitzgibbon1, J J Morgan1, G F Webb2

  • 1Department of Mathematics, University of Houston, Houston, 77204, TX, USA.

Theoretical Biology & Medical Modelling
|March 29, 2017
PubMed
Summary
This summary is machine-generated.

This study models epidemic spatial spread, showing early outbreak locations and sizes significantly impact the epidemic

Keywords:
Criss-cross dynamicsLocal reproduction numberZika epidemic

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Spatial Dynamics

Background:

  • A deterministic model is developed to study the spatial spread of vector-borne epidemic diseases.
  • The model focuses on outbreaks originating from imported infected hosts within a geographical region.
  • It investigates how spatial variations in vector and host populations affect epidemic spread and final size.

Purpose of the Study:

  • To understand the influence of spatial heterogeneity on epidemic dynamics.
  • To analyze the geographical spread and final size of an epidemic.
  • To model vector-borne disease transmission considering host-vector interactions.

Main Methods:

  • Formulation of partial differential equations with reaction-diffusion terms to model host-vector spatial interactions.
  • Analysis of the partial differential equations to prove well-posedness.
  • Calculation of a local basic reproduction number for the epidemic.

Main Results:

  • Epidemic outcomes are correlated with spatially dependent parameters and initial conditions.
  • The model was adapted for vector population seasonality and applied to the 2015-2016 Zika outbreak in Rio de Janeiro.
  • Simulations indicated that early outbreak characteristics strongly influence the epidemic's spatial distribution and final size.

Conclusions:

  • The spatial distribution and final size of the 2015-2016 Zika epidemic in Rio de Janeiro were highly dependent on initial outbreak conditions.
  • Model application was constrained by incomplete epidemic data and parametric uncertainties.
  • The study highlights the importance of initial conditions in predicting epidemic trajectories.