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Bats, objectivity, and viral spillover risk.

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Modeling zoonotic disease risks, crucial for pandemic prediction, faces challenges when underlying assumptions rapidly change. Taxonomic classifications can impact risk assessment objectivity, leading to potentially flawed policy decisions.

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Area of Science:

  • Epidemiology
  • Evolutionary Biology
  • Risk Assessment

Background:

  • Anticipating future pandemics requires robust modeling of zoonotic disease risks.
  • Current modeling approaches face challenges due to unsettled or rapidly evolving background assumptions.
  • The reliability of phylogenetic inference and taxonomic classifications is central to this challenge.

Purpose of the Study:

  • To explore best practices for modeling zoonotic disease risks in dynamic environments.
  • To investigate how evolving assumptions impact the objectivity of zoonotic risk assessments.
  • To inform more reliable policy decisions for pandemic preparedness.

Main Methods:

  • Analysis of current methodologies in phylogenetic inference.
  • Evaluation of the impact of taxonomic assumptions on risk assessment models.
  • Literature review on modeling approaches for zoonotic diseases.

Main Results:

  • Different taxonomic assumptions can significantly destabilize zoonotic risk assessments.
  • The objectivity of risk assessments is challenged by the fluidity of background assumptions.
  • Inconsistent and overconfident policy decisions may arise from unstable risk models.

Conclusions:

  • Best practices must account for the inherent uncertainty in taxonomic and phylogenetic data.
  • Developing adaptable modeling frameworks is essential for accurate zoonotic risk assessment.
  • Addressing the impact of evolving assumptions is critical for effective pandemic preparedness and policy making.