Toward Setting Minimum and Optimal Data to Report for Malaria Molecular Surveillance with Targeted Sequencing: The “What” and “Why”
Résumé fourni par la source
The coronavirus disease 2019 pandemic showcased the power of genomic surveillance in tracking infectious diseases, driving rapid public health responses, and global collaboration. This same infrastructure is being leveraged for malaria molecular surveillance (MMS) in Africa to address challenges such as artemisinin partial resistance and deletions in the Plasmodium falciparum histidine-rich protein 2 and 3 genes. However, variability in reporting sequencing methods and data reporting currently limits the validation, comparability, and reuse of data. To maximize the impact of MMS, minimal and optimal data that are key for validation and maximizing transparency and findable, accessible, interoperable, and reusable principles are proposed for reporting. Rather than focusing on specific data formats, in the current study, the authors propose what should be reported and why. Progressing to reporting individual infection-level polymorphism or microhaplotype data is central to maximizing the impact of MMS. Reporting must adhere to local regulatory practices and ensure proper data oversight and management, preventing data colonialism and preserving opportunities for data generators. With malaria's challenges transcending borders, reporting and adopting standardized practices are essential to advancing research and strengthening global public health efforts.
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Contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Toward Setting Minimum and Optimal Data to Report for Malaria Molecular Surveillance with Targeted Sequencing: The “What” and “Why”
- Date Crossref
- 03/12/2025
- Éditeur
- American Society of Tropical Medicine and Hygiene
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.
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