Modeling Zika Virus Transmission Dynamics: Parameter Estimates, Disease Characteristics, and Prevention

Abstract

Because of limited data, much remains uncertain about parameters related to transmission dynamics of Zika virus (ZIKV). Estimating a large number of parameters from the limited information in data may not provide useful knowledge about the ZIKV. Here, we developed a method that utilizes a mathematical model of ZIKV dynamics and the complex-step derivative approximation technique to identify parameters that can be estimated from the available data. Applying our method to epidemic data from the ZIKV outbreaks in French Polynesia and Yap Island, we identified the parameters that can be estimated from these island data. Our results suggest that the parameters that can be estimated from a given data set, as well as the estimated values of those parameters, vary from Island to Island. Our method allowed us to estimate some ZIKV-related parameters with reasonable confidence intervals. We also computed the basic reproduction number to be from 2.03 to 3.20 across islands. Furthermore, using our model, we evaluated potential prevention strategies and found that peak prevalence can be reduced to nearly 10% by reducing mosquito-to-human contact by at least 60% or increasing mosquito death by at least a factor of three of the base case. With these preventions, the final outbreak-size is predicted to be negligible, thereby successfully controlling ZIKV epidemics.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jul 22, 2019
Source ID
10.1038/s41598-019-46218-4

Entities

People

  • H. Thomas Banks
  • Kidist Bekele-maxwell
  • Leanna L. Cates
  • Munsur Rahman
  • Naveen K. Vaidya

Organizations

  • Air Force Office of Scientific Research
  • National Institute on Alcohol Abuse and Alcoholism
  • National Science Foundation Division of Mathematical Sciences
  • San Diego State University

Tags

Fields of Study

  • Biology

Readers

  • Regression Analysis.
  • Urban Planning and Geography.
  • Virology (or Medical Virology).