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Using Serosurveys to Optimize Surveillance for Zoonotic Pathogens.

E Clancey1, S L Nuismer2, S N Seifert3

  • 1Paul G. Allen School for Global Health, Washington State University, Pullman, WA, 99164, USA. erin.clancey@wsu.edu.

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Summary
This summary is machine-generated.

Identifying animal reservoirs for zoonotic pathogens is crucial. This study presents a mathematical model using serosurveillance data to predict peak pathogen prevalence, aiding in risk assessment and sampling strategies for elusive diseases.

Keywords:
infectious diseasemathematical modelreservoir ecologyspilloversurveillance

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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.