Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information.

Area of Science:

• Veterinary Epidemiology
• Disease Modeling
• Public Health Policy

Background:

• Livestock movements are key drivers for infectious disease spread, including Rift Valley fever (RVF).
• Accurate livestock movement data are crucial for effective disease control interventions like vaccination.
• Data scarcity and imperfections in East Africa hinder Rift Valley fever (RVF) control efforts.

Purpose of the Study:

• To evaluate the impact of imperfect livestock movement data on targeting vaccination strategies for Rift Valley fever (RVF).
• To assess the effectiveness of targeted vaccination versus random vaccination in controlling RVF spread.
• To demonstrate the utility of network modeling for informing animal and public health policies.

Main Methods:

• Utilized a network simulation model to simulate RVF spread across 398 wards in northern Tanzania.
• Incorporated cattle movement data, including imperfect market movement permit information.
• Evaluated the impact of targeting vaccination based on varying levels of movement data reliability.

Main Results:

• Pre-emptive vaccination guided by market movement data can significantly prevent large Rift Valley fever (RVF) outbreaks.
• Targeted vaccination, even with imperfect data, outperforms random vaccination in controlling disease introduction and transmission.
• Increased reliability of movement data enhances the effectiveness of targeted vaccination strategies.

Conclusions:

• Targeted interventions using imperfect livestock movement data are a viable strategy for Rift Valley fever (RVF) control.
• This approach is particularly valuable in resource-limited settings with data collection challenges.
• Network modeling provides a robust framework for informing disease control planning and public health policy.