Using serosurveys to optimize surveillance for zoonotic pathogens.

Area of Science:

• Epidemiology
• Wildlife Health
• Disease Ecology

Background:

• Zoonotic pathogens pose significant global health risks, causing diseases, epidemics, and pandemics.
• Identifying animal reservoirs for zoonotic pathogens is challenging, especially when pathogen prevalence is seasonal.
• Effective surveillance requires optimized field sampling strategies.

Purpose of the Study:

• To develop a general statistical model for predicting peak pathogen prevalence in animal populations.
• To optimize field sampling efforts for detecting elusive zoonotic pathogens.
• To guide future surveillance and potentially predict spillover events.

Main Methods:

• Developed a general statistical model leveraging routine serosurveillance data.
• Validated the model using simulated datasets to assess its reliability in predicting peak prevalence.
• Applied the model to publicly available serosurveillance data from Ebolavirus reservoir bat species (Eidolon helvum and Hypsignathus monstrosus).

Main Results:

• The developed methodology reliably identifies time windows of expected peak pathogen prevalence.
• The model successfully predicted peak prevalence periods using simulated and real-world bat serosurveillance data.
• Demonstrated the model's applicability to Ebolavirus reservoirs.

Conclusions:

• The model provides a broadly applicable and simple method for optimizing field sampling for elusive pathogens.
• Predicting seasonal peaks in prevalence can enhance the detection of active infections in reservoir species.
• The methodology can inform strategies to mitigate zoonotic disease risks and potentially forecast spillover events.