Modelling the risk of transfusion transmission from travelling donors
BMC Infectious Diseases·
- DOI
- 10.1186/s12879-016-1452-z
- PMID
- 27038919
- PMCID
- PMC4818889
- OpenAlex
- W2324262540
- Study type
- Journal article
- Publisher
- Springer Science and Business Media LLC
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The model provides decision-makers with quantitative estimates of past versus future transfusion transmission risk from traveling donors, supporting evidence-based decisions about public communication and blood safety interventions during emerging disease outbreaks.
Structured evidence summary
Research question
The study aimed to extend the EUFRAT methodology to quantify transfusion transmission risk from donors who traveled to areas with emerging infectious disease outbreaks.
Study design
A generic mathematical model was developed to estimate transfusion transmission risk from traveling donors, distinguishing projected future transmissions from those already occurred. The model was illustrated using historical outbreak data from chikungunya in Italy (2007) and Q fever in the Netherlands (2007-2009).
Population and setting
The model applies to blood donors who traveled to emerging infectious disease risk areas. Illustrative applications focused on donors potentially exposed during the 2007 chikungunya outbreak in Italy and the 2007-2009 Q fever outbreak in the Netherlands.
Main findings
For the chikungunya outbreak, early intervention at week 7 (after 19% of cases) would have prevented only 41% of expected transmissions at that time. For Q fever, even at the end of the third annual outbreak peak, 47% of chronic Q fever transmissions could still be prevented.
Public-health relevance
The model provides decision-makers with quantitative estimates of past versus future transfusion transmission risk from traveling donors, supporting evidence-based decisions about public communication and blood safety interventions during emerging disease outbreaks.
Important limitations
This summary relies on the supplied abstract and metadata from a single modeling study. The model's performance depends on outbreak parameter estimates and assumptions about donor travel patterns. Full assessment of model validation, sensitivity analyses, and operational constraints requires review of the complete published article.
GIDS interpretation
This article is discoverable through searches for transfusion safety, travel-associated disease transmission, and outbreak response modeling. The chikungunya and Q fever classifiers reflect the illustrative case studies used to demonstrate the model, not an assessment of current transmission risk.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 12 auditable classifier relationships to diseases, places, topics, and study design.