Evaluating the performance of Plasmodium falciparum genetics for inferring National Malaria Control Program reported incidence in Senegal.
| العنوان: | Evaluating the performance of Plasmodium falciparum genetics for inferring National Malaria Control Program reported incidence in Senegal. |
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| المؤلفون: | Wong W; Harvard T. H. Chan School of Public Health., Schaffner SF; The Broad Institute., Thwing J; Centers for Disease Control and Prevention., Seck MC; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Gomis J; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Diedhiou Y; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Sy N; Section de Lutte Anti-Parasitaire (SLAP) Clinic., Ndiop M; Programme National de Lutte Contre le Paludisme., Ba F; Programme National de Lutte Contre le Paludisme., Diallo I; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Sene D; Programme National de Lutte Contre le Paludisme., Diallo MA; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Ndiaye YD; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Sy M; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Sene A; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Sow D; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Dieye B; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Tine A; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Ribado J; Institute for Disease Modeling, Bill and Melinda Gates Foundation., Suresh J; Institute for Disease Modeling, Bill and Melinda Gates Foundation., Lee A; Institute for Disease Modeling, Bill and Melinda Gates Foundation., Battle KE; Institute for Disease Modeling, Bill and Melinda Gates Foundation., Proctor JL; Institute for Disease Modeling, Bill and Melinda Gates Foundation., Bever CA; Institute for Disease Modeling, Bill and Melinda Gates Foundation., MacInnis B; The Broad Institute., Ndiaye D; Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)., Hartl DL; Harvard University., Wirth DF; Harvard T. H. Chan School of Public Health., Volkman SK; Harvard T. H. Chan School of Public Health. |
| المصدر: | Research square [Res Sq] 2023 Nov 01. Date of Electronic Publication: 2023 Nov 01. |
| نوع المنشور: | Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101768035 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: Res Sq Subsets: PubMed not MEDLINE |
| مستخلص: | Genetic surveillance of the Plasmodium falciparum parasite shows great promise for helping National Malaria Control Programs (NMCPs) assess parasite transmission. Genetic metrics such as the frequency of polygenomic (multiple strain) infections, genetic clones, and the complexity of infection (COI, number of strains per infection) are correlated with transmission intensity. However, despite these correlations, it is unclear whether genetic metrics alone are sufficient to estimate clinical incidence. Here, we examined parasites from 3,147 clinical infections sampled between the years 2012-2020 through passive case detection (PCD) across 16 clinic sites spread throughout Senegal. Samples were genotyped with a 24 single nucleotide polymorphism (SNP) molecular barcode that detects parasite strains, distinguishes polygenomic (multiple strain) from monogenomic (single strain) infections, and identifies clonal infections. To determine whether genetic signals can predict incidence, we constructed a series of Poisson generalized linear mixed-effects models to predict the incidence level at each clinical site from a set of genetic metrics designed to measure parasite clonality, superinfection, and co-transmission rates. We compared the model-predicted incidence with the reported standard incidence data determined by the NMCP for each clinic and found that parasite genetic metrics generally correlated with reported incidence, with departures from expected values at very low annual incidence (<10/1000/annual [‰]). When transmission is greater than 10 cases per 1000 annual parasite incidence (annual incidence >10 ‰), parasite genetics can be used to accurately infer incidence and is consistent with superinfection-based hypotheses of malaria transmission. When transmission was <10 ‰, we found that many of the correlations between parasite genetics and incidence were reversed, which we hypothesize reflects the disproportionate impact of importation and focal transmission on parasite genetics when local transmission levels are low. Competing Interests: Additional Declarations: No competing interests reported. |
| التعليقات: | Update in: Malar J. 2024 Mar 5;23(1):68. (PMID: 38443939) |
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| معلومات مُعتمدة: | R21 AI141843 United States AI NIAID NIH HHS |
| تواريخ الأحداث: | Date Created: 20231114 Latest Revision: 20240318 |
| رمز التحديث: | 20240318 |
| مُعرف محوري في PubMed: | PMC10635402 |
| DOI: | 10.21203/rs.3.rs-3516287/v1 |
| PMID: | 37961451 |
| قاعدة البيانات: | MEDLINE |
| DOI: | 10.21203/rs.3.rs-3516287/v1 |
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