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Insight into "Calculated Risk": An Application to the Prioritization of Emerging Infectious Diseases for Blood

R E J Neslo1, W Oei1, M P Janssen1,2

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Summary
This summary is machine-generated.

Emerging infectious diseases (EIDs) pose risks to blood safety. Uncertainty about EID characteristics, especially asymptomatic phases and disease impact, drives perceived risk when data is limited, influencing blood safety measures.

Keywords:
Blood transfusion safetyemerging infectious diseasesprobabilistic inversionrisk rankinguncertainty

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Area of Science:

  • Infectious Diseases
  • Public Health
  • Blood Transfusion Safety

Background:

  • Emerging infectious diseases (EIDs) are increasingly identified as potential threats to blood transfusion safety.
  • Uncertainty regarding the characteristics of EIDs complicates risk assessment and the implementation of effective safety measures.

Purpose of the Study:

  • To determine the relative importance of various disease and transmission characteristics in assessing the risk of EIDs to blood safety.
  • To understand how uncertainty in disease characteristics influences the perceived risk of EID transmission via blood transfusion.

Main Methods:

  • Experts in infectious diseases and blood transfusion ranked hypothetical diseases with varying uncertainty in characteristics.
  • Probabilistic inversion was used to derive probability distributions for the weight of each risk characteristic.

Main Results:

  • When data is scarce, uncertainty in the asymptomatic phase and disease impact are key drivers of perceived risk.
  • When disease characteristics are well-established, infection prevalence and transmissibility by transfusion are the primary risk drivers.
  • A risk prioritization model was developed using expert assessment and limited information.

Conclusions:

  • The derived model offers a rational expert assessment for prioritizing EIDs based on blood transfusion risk.
  • This model can guide proportional responses to emerging infectious diseases, particularly when specific data is limited.