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A framework for Real-Time Zika Assessment in the United States

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2018 Annual Meeting of the Council of Sponsoring Institutions
Lauren Castro
The University of Texas at Austin

Published in: Government & Nonprofit
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A framework for Real-Time Zika Assessment in the United States

  1. 1. A framework for Real-Time Zika Assessment in the United States Lauren Castro The University of Texas at Austin March 7, 2018 73rd Annual Meeting of the ORAU Council of Sponsoring Institutions
  2. 2. Zika Virus (ZIKV) Spread in the Americas World Health Organization : Zika Timeline
  3. 3. Aedes aegypti puts Texas at risk for arbovirus transmission • Dengue • 23 imported cases in 2015 • 2013 outbreak in Southern Texas — 53 positive case-patients • Chikungunya • 38 imported cases in 2015 • First Texas-acquired case in Cameron County Nov 2015 • Zika • 27 importations within the first 90 days of 2016, most in the Houston area
  4. 4. Modeling can help answer questions for preparedness and decision support 1.What is the baseline distribution of ZIKV transmission risk in Texas? 2. If cases should appear, how does the number of detected cases change the perceived risk of an epidemic?
  5. 5. Zika Transmission Cycle Importation Sustained Transmission
  6. 6. Asymptomatic Cases Symptomatic Cases Transmission Importations Reported Cases Hidden dynamics present a public health challenge
  7. 7. Three step framework 1. Build predictive models and analyze baseline risk •Identify and model important components of transmission 2. Conduct hypothetical scenarios •Simulate outbreak trajectories under different starting conditions 3. Analyze scenarios for preparedness •Derive epidemic risk assessments based on the number of locally detected cases
  8. 8. Importation pressure across Texas Historical Importations 2002-2016 10 Socioeconomic and Environmental Variables •Total $ Spending on Traveling •Population holding Graduate or professional degree •183 DENV, 38 CHIKV, and 31 ZIKV Importations
  9. 9. Relative sustained transmission rates Many factors contribute to estimating the suitability of local transmission: • Mosquito lifespan* • Mosquito abundance • Human-mosquito interactions • Incubation period of ZIKV* • Temperature - August
  10. 10. County level ZIKV scenarios • Simulations of ZIKV transmission • Begin with a single undetected infected individual (importation) • New cases arise from importations or local transmission • Infectious cases are detected according to a reporting rate • Ends when there are either no current infections or cumulative local infections exceed 2000 • Scenarios are defined by importation, transmission, and reporting rates
  11. 11. By day 75, same number of detected cases but different outbreak trajectories
  12. 12. The number of reported cases can be used to derive risk assessments 10% Reporting Rate 20% Reporting Rate
  13. 13. ZIKV epidemic risk upon reporting 2 local cases is heterogenous across Texas
  14. 14. Surveillance triggers based on a 50% epidemic risk threshold range from 1- 21 cases
  15. 15. Lessons learned and challenges moving forward • Modeling can support the development of risk-based response guidelines • Universal guidelines are pragmatic, but variation in baseline risk may allow for a targeted approach • Risk assessment frameworks should be flexible and allow for scenario- based modeling • Limitations on data resolution affect the granularity of risk assessments • Modeling highlights important areas for further research: • ZIKV parameters • Ae. aegypti abundance • socioeconomic modulation of mosquito-human interaction
  16. 16. Importations can be used to update ZIKV R0s Fox SJ et al. Downgrading disease transmission risk estimates using terminal importations. bioRxiv. 2018
  17. 17. Lauren Ancel Meyers Research Group • Texas Pandemic Flu ToolKit (Texas DSHS) • Influenza and Dengue Surveillance (DSHS, CDC) • Optimizing data systems for outbreak surveillance (DTRA)
  18. 18. Acknowledgements Meyers Research Lab Lauren Ancel Meyers Spencer Fox Kai Liu Carol Chen Collaborators Alison Galvani (Yale) Steven Bellan (UGA) Ned Dimitrov Alex Perkins (Notre Dame) Michael Johansson (CDC)

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