A pathogen selection tool for surveillance of acute febrile illness in Nigeria
Acute febrile illness (AFI) is a common manifestation of infectious diseases and a frequent reason for seeking medical care. Because AFI is a likely clinical presentation of several infectious diseases of public health significance, inadequate diagnostic capacity can delay outbreak detection and res...
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
15 November 2025
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| In: |
Clinical infectious diseases
Year: 2025, Jahrgang: 81, Pages: S213-S220 |
| ISSN: | 1537-6591 |
| DOI: | 10.1093/cid/ciaf499 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1093/cid/ciaf499
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| Verfasserangaben: | Lauren P. Courtney, Claire A. Quiner, Cyril Erameh, Vivian Kwaghe, Jean H. Kim, Jay Osi Samuels, Claire J. Standley, and Emmanuel A. Oga |
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| 520 | |a Acute febrile illness (AFI) is a common manifestation of infectious diseases and a frequent reason for seeking medical care. Because AFI is a likely clinical presentation of several infectious diseases of public health significance, inadequate diagnostic capacity can delay outbreak detection and response. The Surveillance of Acute Febrile Illness Aetiologies in Nigeria (SAFIAN) study aimed to investigate the infectious causes of AFI in Nigeria.We used a TaqMan Array Card (TAC) technology, one of several commercially available tools based on reverse-transcription polymerase chain reaction, which can be customized for simultaneous detection of multiple pathogens. Researchers must prioritize pathogens to target for surveillance, given that it is not feasible or cost-effective to test all pathogens endemic in a region.We developed a 6-step model for pathogen selection for AFI surveillance studies. These 6 steps included (1) defining the study objectives; (2) identifying a global list of potential pathogens compatible with study objectives; (3) evaluating transmission potential of selected pathogens in region(s) of interest; (4) defining the pathogen inclusion and exclusion criteria based on surveillance interests; (5) applying inclusion and exclusion criteria to rank pathogen importance based on evidence of endemicity or alignment with surveillance interest; and (6) selecting the final list of pathogens for surveillance. Using this model, we identified and evaluated 69 potential pathogens and customized a TAC panel with 25 targets.Despite the lack of external feedback, we posit that this model for pathogen selection can be applied to future AFI surveillance studies in resource-constrained settings.This article presents a 6-step, data-driven framework for optimizing multi-pathogen screening tools, aligning pathogen selection with transmission potential. The adaptable, user-friendly tool enhances surveillance in low-resource settings, supporting early outbreak detection and response before widespread transmission occurs. | ||
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| 700 | 1 | |a Erameh, Cyril |e VerfasserIn |4 aut | |
| 700 | 1 | |a Kwaghe, Vivian |e VerfasserIn |4 aut | |
| 700 | 1 | |a Kim, Jean H |e VerfasserIn |4 aut | |
| 700 | 1 | |a Samuels, Jay Osi |e VerfasserIn |4 aut | |
| 700 | 1 | |a Standley, Claire J. |e VerfasserIn |0 (DE-588)1257097083 |0 (DE-627)1801192812 |4 aut | |
| 700 | 1 | |a Oga, Emmanuel A |e VerfasserIn |4 aut | |
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