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BMC Med. 2018 Oct 18;16(1):195. doi: 10.1186/s12916-018-1185-5.

Quantifying the risk of local Zika virus transmission in the contiguous US during the 2015-2016 ZIKV epidemic.

Author information

1
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, 02115, USA.
2
Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, 32611, USA.
3
Bruno Kessler Foundation, 38123, Trento, Italy.
4
Institute for Scientific Interchange Foundation, 10126, Turin, Italy.
5
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, 98109, USA.
6
Department of Biostatistics, University of Washington, Seattle, 98195, USA.
7
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, 02115, USA. a.vespignani@neu.edu.
8
Institute for Scientific Interchange Foundation, 10126, Turin, Italy. a.vespignani@neu.edu.

Abstract

BACKGROUND:

Local mosquito-borne Zika virus (ZIKV) transmission has been reported in two counties in the contiguous United States (US), prompting the issuance of travel, prevention, and testing guidance across the contiguous US. Large uncertainty, however, surrounds the quantification of the actual risk of ZIKV introduction and autochthonous transmission across different areas of the US.

METHODS:

We present a framework for the projection of ZIKV autochthonous transmission in the contiguous US during the 2015-2016 epidemic using a data-driven stochastic and spatial epidemic model accounting for seasonal, environmental, and detailed population data. The model generates an ensemble of travel-related case counts and simulates their potential to have triggered local transmission at the individual level in the 2015-2016 ZIKV epidemic.

RESULTS:

We estimate the risk of ZIKV introduction and local transmission at the county level and at the 0.025° × 0.025° cell level across the contiguous US. We provide a risk measure based on the probability of observing local transmission in a specific location during a ZIKV epidemic modeled after the epidemic observed during the years 2015-2016. The high spatial and temporal resolution of the model allows us to generate statistical estimates of the number of ZIKV introductions leading to local transmission in each location. We find that the risk was spatially heterogeneously distributed and concentrated in a few specific areas that account for less than 1% of the contiguous US population. Locations in Texas and Florida that have actually experienced local ZIKV transmission were among the places at highest risk according to our results. We also provide an analysis of the key determinants for local transmission and identify the key introduction routes and their contributions to ZIKV transmission in the contiguous US.

CONCLUSIONS:

This framework provides quantitative risk estimates, fully captures the stochasticity of ZIKV introduction events, and is not biased by the under-ascertainment of cases due to asymptomatic cases. It provides general information on key risk determinants and data with potential uses in defining public health recommendations and guidance about ZIKV risk in the US.

KEYWORDS:

Computational modeling; Risk assessment; Zika virus

PMID:
30336778
PMCID:
PMC6194624
DOI:
10.1186/s12916-018-1185-5
[Indexed for MEDLINE]
Free PMC Article

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