Award Abstract # 1641130
RAPID: Overcoming uncertainty to enable estimation and forecasting of Zika virus transmission

NSF Org: DEB
Division Of Environmental Biology
Recipient: UNIVERSITY OF NOTRE DAME DU LAC
Initial Amendment Date: May 9, 2016
Latest Amendment Date: May 9, 2016
Award Number: 1641130
Award Instrument: Standard Grant
Program Manager: Samuel Scheiner
DEB  Division Of Environmental Biology
BIO  Directorate for Biological Sciences
Start Date: May 1, 2016
End Date: April 30, 2018 (Estimated)
Total Intended Award Amount: $200,000.00
Total Awarded Amount to Date: $200,000.00
Funds Obligated to Date: FY 2016 = $200,000.00
History of Investigator:
  • Alex Perkins (Principal Investigator)
     taperkins@nd.edu
  • Robert Reiner Jr. (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Notre Dame
 940 GRACE HALL
 NOTRE DAME
 IN  US  46556-5708
(574)631-7432
Sponsor Congressional District: 02
Primary Place of Performance: University of Notre Dame
 IN  US  46556-5612
Primary Place of Performance
Congressional District:		 02
Unique Entity Identifier (UEI): FPU6XGFXMBE9
Parent UEI: FPU6XGFXMBE9
NSF Program(s): Ecology of Infectious Diseases
Primary Program Source: 01001617DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 024Z, 7914
Program Element Code(s): 724200
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.074

ABSTRACT

This RAPID award will develop new modeling tools and data on mosquito locations that will be use to improve Zika transmission forecasting. The assessment of infectious disease forecasts is critical for improving predications and translating the results from the models into accurate public health strategies. This project will provide estimates of mosquito density across the Americas for Aedes aegypti, the primary mosquito that transmits Zika. The project also will update human population data for detailed predictions about Zika-associated microcephaly. This information will be used by policymakers for decisions concerning resource allocation to improve public health. Results from this project will be relevant to the Zika public health emergency, and the researchers have set in place mechanisms to share quality-assured interim and final data as rapidly and widely as possible, including with public health and research communities.

This project will generate spatiotemporal maps of mosquito-to-human ratios to determine patterns of mosquito population dynamics for pathogen transmission models. It will expand Zika transmission modeling to consider mosquito abundance as a function of geographic limits and seasonal changes combined with temporal dynamics for mosquitos. The project will refine pregnancies and birth counts using age-sex structure and age-specific fertility rates to account for variation within countries. This will provide a baseline estimate of what reduction Zika has on the numbers of pregnancies. The model developed will also incorporate dengue and chikungunya cases to account for Zika misclassification, ultimately comparing models for inferring factors that drive spatial and temporal variation in disease incidence. Model outputs will allow users to obtain online reported cases and estimated incidences by location for Zika, dengue, and chikungunya to improve forecasts of disease transmission and prevalence.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 12)
Alex Perkins, T. and Siraj, Amir S. and Ruktanonchai, Corrine W. and Kraemer, Moritz U. and Tatem, Andrew J. "Model-based projections of Zika virus infections in childbearing women in the Americas" Nature Microbiology , v.1 , 2016 10.1038/nmicrobiol.2016.126 Citation Details
España, Guido and Grefenstette, John and Perkins, Alex and Torres, Claudia and Campo Carey, Alfonso and Diaz, Hernando and de la Hoz, Fernando and Burke, Donald S. and van Panhuis, Willem G. "Exploring scenarios of chikungunya mitigation with a data-driven agent-based model of the 2014?2016 outbreak in Colombia" Scientific Reports , v.8 , 2018 10.1038/s41598-018-30647-8 Citation Details
Fox, Spencer J. and Bellan, Steven E. and Perkins, T. Alex and Johansson, Michael A. and Meyers, Lauren Ancel and Churcher, Thomas S. "Downgrading disease transmission risk estimates using terminal importations" PLOS Neglected Tropical Diseases , v.13 , 2019 10.1371/journal.pntd.0007395 Citation Details
Grubaugh, Nathan D. and Saraf, Sharada and Gangavarapu, Karthik and Watts, Alexander and Tan, Amanda L. and Oidtman, Rachel J. and Ladner, Jason T. and Oliveira, Glenn and Matteson, Nathaniel L. and Kraemer, Moritz U.G. and Vogels, Chantal B.F. and Hentof "Travel Surveillance and Genomics Uncover a Hidden Zika Outbreak during the Waning Epidemic" Cell , v.178 , 2019 10.1016/j.cell.2019.07.018 Citation Details
Manore, Carrie A. and Ostfeld, Richard S. and Agusto, Folashade B. and Gaff, Holly and LaDeau, Shannon L. and Scarpino, Samuel V. "Defining the Risk of Zika and Chikungunya Virus Transmission in Human Population Centers of the Eastern United States" PLOS Neglected Tropical Diseases , v.11 , 2017 10.1371/journal.pntd.0005255 Citation Details
Oidtman, Rachel J. and Lai, Shengjie and Huang, Zhoujie and Yang, Juan and Siraj, Amir S. and Reiner, Robert C. and Tatem, Andrew J. and Perkins, T. Alex and Yu, Hongjie "Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China" Nature Communications , v.10 , 2019 10.1038/s41467-019-09035-x Citation Details
Perkins, T. Alex "Retracing Zika?s footsteps across the Americas with computational modeling" Proceedings of the National Academy of Sciences , v.114 , 2017 10.1073/pnas.1705969114 Citation Details
Perkins, T. Alex and Rodriguez-Barraquer, Isabel and Manore, Carrie and Siraj, Amir S. and España, Guido and Barker, Christopher M. and Johansson, Michael A. and Reiner, Robert C. "Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data" Epidemics , 2019 10.1016/j.epidem.2019.100357 Citation Details
Shutt, Deborah P. and Manore, Carrie A. and Pankavich, Stephen and Porter, Aaron T. and Del Valle, Sara Y. "Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America" Epidemics , v.21 , 2017 10.1016/j.epidem.2017.06.005 Citation Details
Siraj, Amir S. and Oidtman, Rachel J. and Huber, John H. and Kraemer, Moritz U. and Brady, Oliver J. and Johansson, Michael A. and Perkins, T. Alex and Althouse, Benjamin "Temperature modulates dengue virus epidemic growth rates through its effects on reproduction numbers and generation intervals" PLOS Neglected Tropical Diseases , v.11 , 2017 10.1371/journal.pntd.0005797 Citation Details
Siraj, Amir S and Perkins, T Alex "Assessing the population at risk of Zika virus in Asia ? is the emergency really over?" BMJ Global Health , v.2 , 2017 10.1136/bmjgh-2017-000309 Citation Details
(Showing: 1 - 10 of 12)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

 

The goal of this project was to develop, refine, and disseminate new modeling tools and derived data products that would enhance all stages of the workflow between data acquisition and Zika forecasting. This project involved several early-career researchers from a range of academic and government institutions. Two postdoctoral researchers were closely involved in the project, and both graduate and undergraduate researchers have used project activities as an opportunity to gain research experience and to network with other researchers. Project activities consisted of regular teleconferences and several meetings among this network of researchers.
The first category of results from this project include enhancements of workflows for modeling Zika virus transmission in a spatial context. This includes a data paper that assembled fine-scale data on Zika incidence from Colombia combined with numerous covariates necessary for modeling transmission, such as population size, weather variables, and indices of mosquito occurrence, land use, and economic activity. The formal description of these variables and the workflow used to assemble them is significant, because it offers an alternative to an ad hoc process replicated many times over in many different ways by many different researchers. In addition, project activities contributed to the preparation of novel spatial products for modeling Zika virus transmission, including subnational estimates of births, modeled spatiotemporal densities of Aedes aegypti mosquitoes, and modeled human movement patterns at spatial scales finer than those at which empirical estimates are widely available.
The second category of results from this project include analyses of spatiotemporal Zika incidence patterns from Colombia. These analyses involved novel applications of classification algorithms to characterize variability in subnational patterns of epidemic trajectories. In particular, epidemic patterns from a local perspective in Colombia were found to be very different from the overall epidemic pattern at the country level, even though many analyses often focus on nationally aggregated data. Complementary simulation analyses showed that the classification algorithm was capable of identifying variation in epidemic patterns attributable to variation in underlying ecological drivers of transmission. Subsequent analyses of these data are being led by graduate and undergraduate researchers, who are focusing on developing new methods for forecasting epidemics of this nature. Preliminary analyses of alternative forecasting approaches indicate that forecasting performance tends to be enhanced when using subnational epidemic time series rather than nationally aggregated time series, consistent with other findings from this project. 
The third category of results from this project include application of model-based projections resulting from the project at early stages of the Zika epidemic. In particular, these projections have been used to make recommendations to vaccine developers about areas that could be promising for carrying out vaccine efficacy trials. Numerous Zika vaccines were under development during the Zika epidemic, and as the epidemic waned across the Americas this question became more pressing. Throughout the epidemic, projections of Zika epidemic size derived from this project were compared to estimates of Zika epidemic size based on passive surveillance data reported by countries to the Pan American Health Organization. These projections have been disseminated to interested parties in the federal government and openly online through a biorxiv preprint.

 


Last Modified: 08/31/2018
Modified by: Troy A Perkins

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