Using Serosurveys to Optimize Surveillance for Zoonotic Pathogens

Area of Science:

• Epidemiology
• Mathematical Biology
• Wildlife Health

Background:

• Zoonotic pathogens pose significant risks to human health, causing chronic diseases and epidemics.
• Identifying animal reservoirs for these pathogens is challenging, especially when prevalence is seasonal.
• Current methods struggle to pinpoint peak pathogen prevalence in reservoir populations.

Purpose of the Study:

• To develop a general mathematical model for predicting peak pathogen prevalence in animal reservoirs.
• To optimize field sampling strategies for elusive zoonotic pathogens.
• To guide predictions of zoonotic spillover risk.

Main Methods:

• Development of a general mathematical model.
• Leveraging routinely collected serosurveillance data.
• Testing with simulated data and real-world surveillance data from straw-colored fruit bats (Eidolon helvum).

Main Results:

• The methodology reliably identifies times of expected peak pathogen prevalence.
• Demonstrated successful implementation using bat surveillance data.
• The model is broadly applicable to various reservoir species with seasonal prevalence patterns.

Conclusions:

• The developed mathematical model effectively predicts seasonal pathogen prevalence in animal reservoirs.
• This approach enhances the ability to identify high-risk periods for zoonotic spillover.
• The method offers a simple, generalizable tool for wildlife disease surveillance and management.