Evaluating the performance of Plasmodium falciparum genetics for inferring National Malaria Control Program reported incidence in Senegal Article Swipe
Wesley Wong
,
S. F. Schaffner
,
Julie Thwing
,
Mame Cheikh Seck
,
Jules Gomis
,
Younouss Diédhiou
,
Ngayo Sy
,
Médoune Ndiop
,
Fatou Ba
,
Ibrahima Diallo
,
Doudou Sène
,
Mamadou Alpha Diallo
,
Yaye Dié Ndiaye
,
Mouhamad Sy
,
Aita Sène
,
Djiby Sow
,
Baba Dièye
,
Abdoulaye Tine
,
Jessica V. Ribado
,
Joshua Suresh
,
Albert Lee
,
Katherine E. Battle
,
Joshua L. Proctor
,
Caitlin Bever
,
Bronwyn MacInnis
,
Daouda Ndiaye
,
Daniel L. Hartl
,
Dyann F. Wirth
,
Sarah K. Volkman
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3516287/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3516287/v1
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3516287/v1
- https://www.researchsquare.com/article/rs-3516287/latest.pdf
- OA Status
- green
- Cited By
- 7
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388124206
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4388124206Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-3516287/v1Digital Object Identifier
- Title
-
Evaluating the performance of Plasmodium falciparum genetics for inferring National Malaria Control Program reported incidence in SenegalWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-01Full publication date if available
- Authors
-
Wesley Wong, S. F. Schaffner, Julie Thwing, Mame Cheikh Seck, Jules Gomis, Younouss Diédhiou, Ngayo Sy, Médoune Ndiop, Fatou Ba, Ibrahima Diallo, Doudou Sène, Mamadou Alpha Diallo, Yaye Dié Ndiaye, Mouhamad Sy, Aita Sène, Djiby Sow, Baba Dièye, Abdoulaye Tine, Jessica V. Ribado, Joshua Suresh, Albert Lee, Katherine E. Battle, Joshua L. Proctor, Caitlin Bever, Bronwyn MacInnis, Daouda Ndiaye, Daniel L. Hartl, Dyann F. Wirth, Sarah K. VolkmanList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-3516287/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-3516287/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-3516287/latest.pdfDirect OA link when available
- Concepts
-
Transmission (telecommunications), Incidence (geometry), Biology, Plasmodium falciparum, Malaria, Superinfection, Parasite hosting, Genetics, Virology, Immunology, Virus, Engineering, Electrical engineering, Optics, Computer science, Physics, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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