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Proc Natl Acad Sci U S A. 2017 May 30;114(22):E4334-E4343. doi: 10.1073/pnas.1620161114. Epub 2017 Apr 25.

Spread of Zika virus in the Americas.

Author information

1
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115.
2
Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32611.
3
Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32611.
4
Bruno Kessler Foundation, 38123 Trento, Italy.
5
Dondena Centre for Research on Social Dynamics and Public Policy, Universitá Commerciale L. Bocconi, 20136 Milan, Italy.
6
Institute for Scientific Interchange Foundation, 10126 Turin, Italy.
7
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109.
8
Department of Biostatistics, University of Washington, Seattle, WA 98195.
9
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115; a.vespignani@northeastern.edu.

Abstract

We use a data-driven global stochastic epidemic model to analyze the spread of the Zika virus (ZIKV) in the Americas. The model has high spatial and temporal resolution and integrates real-world demographic, human mobility, socioeconomic, temperature, and vector density data. We estimate that the first introduction of ZIKV to Brazil likely occurred between August 2013 and April 2014 (90% credible interval). We provide simulated epidemic profiles of incident ZIKV infections for several countries in the Americas through February 2017. The ZIKV epidemic is characterized by slow growth and high spatial and seasonal heterogeneity, attributable to the dynamics of the mosquito vector and to the characteristics and mobility of the human populations. We project the expected timing and number of pregnancies infected with ZIKV during the first trimester and provide estimates of microcephaly cases assuming different levels of risk as reported in empirical retrospective studies. Our approach represents a modeling effort aimed at understanding the potential magnitude and timing of the ZIKV epidemic and it can be potentially used as a template for the analysis of future mosquito-borne epidemics.

KEYWORDS:

Zika virus; computational epidemiology; metapopulation network model; vector-borne diseases

PMID:
28442561
PMCID:
PMC5465916
DOI:
10.1073/pnas.1620161114
[Indexed for MEDLINE]
Free PMC Article

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