Evaluating the performance of Plasmodium falciparum genetic metrics for inferring National Malaria Control Programme reported incidence in Senegal
Abstract Background Genetic surveillance of the Plasmodium falciparum parasite shows great promise for helping National Malaria Control Programmes (NMCPs) assess parasite transmission. Genetic metrics such as the frequency of polygenomic (multiple strain) infections, genetic clones, and the complexi...
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BMC
2024-03-01
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Series: | Malaria Journal |
Online Access: | https://doi.org/10.1186/s12936-024-04897-z |
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author | Wesley Wong Stephen F. Schaffner Julie Thwing Mame Cheikh Seck Jules Gomis Younouss Diedhiou Ngayo Sy Medoune Ndiop Fatou Ba Ibrahima Diallo Doudou Sene Mamadou Alpha Diallo Yaye Die Ndiaye Mouhamad Sy Aita Sene Djiby Sow Baba Dieye Abdoulaye Tine Jessica Ribado Joshua Suresh Albert Lee Katherine E. Battle Joshua L. Proctor Caitlin A. Bever Bronwyn MacInnis Daouda Ndiaye Daniel L. Hartl Dyann F. Wirth Sarah K. Volkman |
author_facet | Wesley Wong Stephen F. Schaffner Julie Thwing Mame Cheikh Seck Jules Gomis Younouss Diedhiou Ngayo Sy Medoune Ndiop Fatou Ba Ibrahima Diallo Doudou Sene Mamadou Alpha Diallo Yaye Die Ndiaye Mouhamad Sy Aita Sene Djiby Sow Baba Dieye Abdoulaye Tine Jessica Ribado Joshua Suresh Albert Lee Katherine E. Battle Joshua L. Proctor Caitlin A. Bever Bronwyn MacInnis Daouda Ndiaye Daniel L. Hartl Dyann F. Wirth Sarah K. Volkman |
author_sort | Wesley Wong |
collection | DOAJ |
description | Abstract Background Genetic surveillance of the Plasmodium falciparum parasite shows great promise for helping National Malaria Control Programmes (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. Methods This study examined parasites from 3147 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, a series of Poisson generalized linear mixed-effects models were constructed 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. Results Model-predicted incidence was compared 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 [‰]). Conclusions 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‰, many of the correlations between parasite genetics and incidence were reversed, which may reflect the disproportionate impact of importation and focal transmission on parasite genetics when local transmission levels are low. |
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spelling | doaj.art-5608145da9cd4c09a5c64e5e6c7f65e02024-03-10T12:06:22ZengBMCMalaria Journal1475-28752024-03-0123111310.1186/s12936-024-04897-zEvaluating the performance of Plasmodium falciparum genetic metrics for inferring National Malaria Control Programme reported incidence in SenegalWesley Wong0Stephen F. Schaffner1Julie Thwing2Mame Cheikh Seck3Jules Gomis4Younouss Diedhiou5Ngayo Sy6Medoune Ndiop7Fatou Ba8Ibrahima Diallo9Doudou Sene10Mamadou Alpha Diallo11Yaye Die Ndiaye12Mouhamad Sy13Aita Sene14Djiby Sow15Baba Dieye16Abdoulaye Tine17Jessica Ribado18Joshua Suresh19Albert Lee20Katherine E. Battle21Joshua L. Proctor22Caitlin A. Bever23Bronwyn MacInnis24Daouda Ndiaye25Daniel L. Hartl26Dyann F. Wirth27Sarah K. Volkman28Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public HealthInfectious Disease and Microbiome Program, The Broad InstituteMalaria Branch, Division of Parasitic Diseases and Malaria, Global Health Center, Centers for Disease Control and PreventionCentre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)Section de Lutte Anti-Parasitaire (SLAP) ClinicProgramme National de Lutte contre le Paludisme (PNLP)Programme National de Lutte contre le Paludisme (PNLP)Programme National de Lutte contre le Paludisme (PNLP)Programme National de Lutte contre le Paludisme (PNLP)Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)Centre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)Institute for Disease Modeling at the Bill and Melinda Gates FoundationInstitute for Disease Modeling at the Bill and Melinda Gates FoundationInstitute for Disease Modeling at the Bill and Melinda Gates FoundationInstitute for Disease Modeling at the Bill and Melinda Gates FoundationInstitute for Disease Modeling at the Bill and Melinda Gates FoundationInstitute for Disease Modeling at the Bill and Melinda Gates FoundationInfectious Disease and Microbiome Program, The Broad InstituteCentre International de recherche, de formation en Genomique Appliquee et de Surveillance Sanitaire (CIGASS)Department of Organismic and Evolutionary Biology, Harvard UniversityDepartment of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public HealthDepartment of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public HealthAbstract Background Genetic surveillance of the Plasmodium falciparum parasite shows great promise for helping National Malaria Control Programmes (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. Methods This study examined parasites from 3147 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, a series of Poisson generalized linear mixed-effects models were constructed 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. Results Model-predicted incidence was compared 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 [‰]). Conclusions 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‰, many of the correlations between parasite genetics and incidence were reversed, which may reflect the disproportionate impact of importation and focal transmission on parasite genetics when local transmission levels are low.https://doi.org/10.1186/s12936-024-04897-z |
spellingShingle | Wesley Wong Stephen F. Schaffner Julie Thwing Mame Cheikh Seck Jules Gomis Younouss Diedhiou Ngayo Sy Medoune Ndiop Fatou Ba Ibrahima Diallo Doudou Sene Mamadou Alpha Diallo Yaye Die Ndiaye Mouhamad Sy Aita Sene Djiby Sow Baba Dieye Abdoulaye Tine Jessica Ribado Joshua Suresh Albert Lee Katherine E. Battle Joshua L. Proctor Caitlin A. Bever Bronwyn MacInnis Daouda Ndiaye Daniel L. Hartl Dyann F. Wirth Sarah K. Volkman Evaluating the performance of Plasmodium falciparum genetic metrics for inferring National Malaria Control Programme reported incidence in Senegal Malaria Journal |
title | Evaluating the performance of Plasmodium falciparum genetic metrics for inferring National Malaria Control Programme reported incidence in Senegal |
title_full | Evaluating the performance of Plasmodium falciparum genetic metrics for inferring National Malaria Control Programme reported incidence in Senegal |
title_fullStr | Evaluating the performance of Plasmodium falciparum genetic metrics for inferring National Malaria Control Programme reported incidence in Senegal |
title_full_unstemmed | Evaluating the performance of Plasmodium falciparum genetic metrics for inferring National Malaria Control Programme reported incidence in Senegal |
title_short | Evaluating the performance of Plasmodium falciparum genetic metrics for inferring National Malaria Control Programme reported incidence in Senegal |
title_sort | evaluating the performance of plasmodium falciparum genetic metrics for inferring national malaria control programme reported incidence in senegal |
url | https://doi.org/10.1186/s12936-024-04897-z |
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