TITLE

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

AUTHOR(S)
Fitzgibbon, W. E.; Morgan, J. J.; Webb, G. F.
PUB. DATE
March 2017
SOURCE
Theoretical Biology & Medical Modelling;3/27/2017, Vol. 14, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Background: A deterministic model is developed for the spatial spread of an epidemic disease in a geographical setting. The disease is borne by vectors to susceptible hosts through criss-cross dynamics. The model is focused on an outbreak that arises from a small number of infected hosts imported into a subregion of the geographical setting. The goal is to understand how spatial heterogeneity of the vector and host populations influences the dynamics of the outbreak, in both the geographical spread and the final size of the epidemic. Methods: Partial differential equations are formulated to describe the spatial interaction of the hosts and vectors. The partial differential equations have reaction-diffusion terms to describe the criss-cross interactions of hosts and vectors. The partial differential equations of the model are analyzed and proven to be well-posed. A local basic reproduction number for the epidemic is analyzed. Results: The epidemic outcomes of the model are correlated to the spatially dependent parameters and initial conditions of the model. The partial differential equations of the model are adapted to seasonality of the vector population, and applied to the 2015-2016 Zika seasonal outbreak in Rio de Janeiro Municipality in Brazil. Conclusions: The results for the model simulations of the 2015-2016 Zika seasonal outbreak in Rio de Janeiro Municipality indicate that the spatial distribution and final size of the epidemic at the end of the season are strongly dependent on the location and magnitude of local outbreaks at the beginning of the season. The application of the model to the Rio de Janeiro Municipality Zika 2015-2016 outbreak is limited by incompleteness of the epidemic data and by uncertainties in the parametric assumptions of the model.
ACCESSION #
122399295

 

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