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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, 13 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 its broad distribution of ZIKV vector species and periodic ZIKV introductions by travelers returning from affected regions. As cases mount within the US, policymakers seek early and accurate indicators of self-sustaining local transmission to inform intervention efforts. However, given ZIKV's low reporting rates and 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 outbreak.

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 identified surveillance triggers (i.e., cumulative reported case thresholds) that robustly indicate further epidemic expansion. Regions of greatest risk for sustained ZIKV transmission include 33 Texas counties along the Texas-Mexico border, in the Houston Metro Area, and throughout the I-35 Corridor from San Antonio to Waco. Across this region, variation in reporting rates, ZIKV introductions, and vector habitat suitability drives variation in the recommended surveillance triggers for public health response. For high risk Texas counties, we found that, for a reporting rate of 20%, a trigger of two cumulative reported cases corresponds to a 60% chance of an ongoing local transmission.

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 June 07, 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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