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

Background Confirmed local transmission of Zika Virus (ZIKV) in Texas and Florida have heightened the need for early and accurate indicators of self-sustaining transmission in high risk areas across the southern United States. Given ZIKV’s low reporting rates and the geographic variability in suitable conditions, a cluster of reported cases may reflect diverse scenarios, ranging from independent introductions to a self-sustaining local epidemic.

Methods We present a quantitative framework for real-time ZIKV risk assessment that captures uncertainty in case reporting, importations, and vector-human transmission dynamics.

Results We assessed county-level risk throughout Texas, as of summer 2016, and found that importation risk was concentrated in large metropolitan regions, while sustained ZIKV transmission risk is concentrated in the southeastern counties including the Houston metropolitan region and the Texas-Mexico border (where the sole autochthonous cases have occurred in 2016). We found that counties most likely to detect cases are not necessarily the most likely to experience epidemics, and used our framework to identify triggers to signal the start of an epidemic based on a policymakers propensity for risk.

Conclusions This framework can inform the strategic timing and spatial allocation of public health resources to combat ZIKV throughout the US, and highlights the need to develop methods to obtain reliable estimates of key epidemiological parameters.

Footnotes

  • Author email addresses: LAC (lacastro{at}utexas.edu), XC (carol.chen{at}utexas.edu), KL (kai.liu{at}utexas.edu), SB (Steve.Bellan{at}uga.edu), NBD (ned{at}austin.utexas.edu), APG (alison.galvani{at}gmail.com), and LAM (laurenmeyers{at}austin.utexas.edu)

  • List of abbreviations

    ZIKV
    Zika virus
    DENV
    Dengue Virus
    CHIKV
    Chikungunya Virus
    SEIR model
    Susceptible-Exposed-Infectious-Recovered epidemiological model
    WHO
    World Health Organization
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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    Posted March 22, 2017.
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    Assessing real-time Zika risk 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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    Assessing real-time Zika risk 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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