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

Area of Science:

• Epidemiology
• Mathematical Biology
• Infectious Disease Dynamics

Background:

• Limited data hinders understanding of Zika virus (ZIKV) transmission dynamics and parameter estimation.
• Estimating numerous parameters from sparse data can lead to unreliable insights into ZIKV.
• Accurate parameter estimation is crucial for predicting ZIKV spread and evaluating control strategies.

Purpose of the Study:

• To develop a method for identifying estimable parameters in ZIKV transmission models using limited epidemic data.
• To apply this method to ZIKV outbreak data from French Polynesia and Yap Island.
• To evaluate the impact of potential prevention strategies on ZIKV epidemic control.

Main Methods:

• Development of a mathematical model for ZIKV transmission dynamics.
• Utilization of the complex-step derivative approximation technique to identify estimable parameters.
• Application of the method to real-world epidemic data from island outbreaks.

Main Results:

• Identified key ZIKV transmission parameters that can be reliably estimated from island-specific data.
• Demonstrated that estimable parameters and their values vary significantly between different island outbreaks.
• Calculated the basic reproduction number (R0) ranging from 2.03 to 3.20 across studied islands.
• Estimated ZIKV-related parameters with reasonable confidence intervals.
• Evaluated prevention strategies, showing that reducing mosquito-to-human contact by 60% or increasing mosquito mortality threefold can reduce peak prevalence to ~10%.

Conclusions:

• The developed method effectively identifies estimable ZIKV transmission parameters from limited data, acknowledging island-to-island variability.
• Effective control strategies, particularly those targeting mosquito populations and contact rates, can significantly mitigate ZIKV epidemics, potentially leading to negligible outbreak sizes.
• The findings provide valuable insights for public health interventions and future ZIKV surveillance.