Structural and Practical Identifiability Analysis of Zika Epidemiological Models.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• The Zika virus (ZIKV) epidemic poses a significant global health threat, necessitating advanced control strategies.
• Epidemiological models are crucial tools for understanding and managing arbovirus disease outbreaks.
• Previous models often simplified transmission dynamics or did not account for specific ZIKV characteristics.

Purpose of the Study:

• To develop and analyze six distinct epidemiological models for Zika virus (ZIKV) transmission.
• To assess the identifiability of model parameters, including direct and vector-borne transmission routes.
• To evaluate the impact of asymptomatic and pregnant infectious classes on ZIKV spread dynamics.

Main Methods:

• Construction of six compartmental epidemiological models for ZIKV, incorporating vector-borne and direct transmission, asymptomatic/pregnant classes, and loss of immunity.
• Testing structural and practical identifiability of model parameters using time-series data from Florida Department of Health reports.
• Utilizing Monte Carlo simulations to compute Average Relative Estimation Errors (AREs) for parameter identifiability analysis.

Main Results:

• Direct transmission rates were found to be not practically identifiable, while fixed recovery rates enhanced overall model identifiability.
• Low AREs across models, with slight increases for models including a pregnant class, confirmed ZIKV epidemic viability in Florida.
• Analysis of reproduction numbers indicated that mosquito-to-human ratio, lifespan, and biting rate are critical factors for ZIKV control.

Conclusions:

• Epidemiological modeling provides valuable insights into ZIKV transmission dynamics and informs public health interventions.
• Targeting mosquito populations is essential for effective control of Zika virus outbreaks.
• Further refinement of models incorporating specific demographic factors can improve prediction and prevention strategies.