RAPID: Overcoming uncertainty to enable estimation and forecasting of Zika virus transmission

University Of Notre Dame, Notre Dame IN

Investigators

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.