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Real-time Zika risk assessment in the United States

Lauren A Castro, Spencer J Fox, Xi Chen, Kai Liu, Steve Bellan, Nedialko B Dimitrov, Alison P Galvani, Lauren Ancel Meyers
doi: https://doi.org/10.1101/056648
Lauren A Castro
1Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
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Spencer J Fox
1Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
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  • For correspondence: spncrfx@gmail.com
Xi Chen
2Graduate Program in Operations Research Industrial Engineering, The University of Texas at Austin, Austin, TX, USA
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Kai Liu
3Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, USA
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Steve Bellan
4Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX, USA
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Nedialko B Dimitrov
2Graduate Program in Operations Research Industrial Engineering, The University of Texas at Austin, Austin, TX, USA
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Alison P Galvani
5Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA
6Department of Ecology and Evolution, Yale University, New Haven, CT, USA
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Lauren Ancel Meyers
1Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
7The Santa Fe Institute, Santa Fe, NM, USA
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Abstract

Background The southern United States (US) may be vulnerable to outbreaks of Zika Virus (ZIKV), given the broad distribution of ZIKV vector species and periodic ZIKV introductions by travelers returning from affected regions. If autochthonous (locally-acquired) cases appear within the US, policymakers will seek early and accurate indicators of self-sustaining transmission to inform intervention efforts. However, given ZIKV’s low reporting rates and the geographic variability in both importations and transmission potential, a small cluster of reported cases may reflect diverse scenarios, ranging from multiple self-limiting but independent introductions to a self-sustaining local epidemic.

Methods and Findings We developed a stochastic model that captures variation and uncertainty in ZIKV case reporting, importations, and transmission, and applied it to assess county-level risk throughout the state of Texas. For each of the 254 counties, we estimated the future epidemic risk as a function of reported autochthonous cases and evaluated a national recommendation to trigger interventions immediately following the first two reported cases of locally-transmitted ZIKV. Our analysis suggests that the regions of greatest risk for sustained ZIKV transmission include 21 Texas counties along the Texas-Mexico border, in the Houston Metro Area, and throughout the I-35 Corridor from San Antonio to Waco. Variation in vector habitat suitability drives epidemic risk variation, and can be exacerbated by uncertainty in reporting rate. Upon detection of a second locally transmitted case, the threat of epidemic expansion will depend critically on local vulnerability. For high risk Texas counties, we estimate this likelihood to be 64%, assuming an August 2016 risk projection and a 20% reporting rate.

Conclusions With reliable estimates of key epidemiological parameters, including reporting rates and vector abundance, this framework can help optimize the timing and spatial allocation of public health resources to fight ZIKV in the US.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 26, 2016.
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Real-time Zika risk assessment in the United States
Lauren A Castro, Spencer J Fox, Xi Chen, Kai Liu, Steve Bellan, Nedialko B Dimitrov, Alison P Galvani, Lauren Ancel Meyers
bioRxiv 056648; doi: https://doi.org/10.1101/056648
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Real-time Zika risk assessment in the United States
Lauren A Castro, Spencer J Fox, Xi Chen, Kai Liu, Steve Bellan, Nedialko B Dimitrov, Alison P Galvani, Lauren Ancel Meyers
bioRxiv 056648; doi: https://doi.org/10.1101/056648

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