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      Modeling malaria genomics reveals transmission decline and rebound in Senegal.

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          Abstract

          To study the effects of malaria-control interventions on parasite population genomics, we examined a set of 1,007 samples of the malaria parasite Plasmodium falciparum collected in Thiès, Senegal between 2006 and 2013. The parasite samples were genotyped using a molecular barcode of 24 SNPs. About 35% of the samples grouped into subsets with identical barcodes, varying in size by year and sometimes persisting across years. The barcodes also formed networks of related groups. Analysis of 164 completely sequenced parasites revealed extensive sharing of genomic regions. In at least two cases we found first-generation recombinant offspring of parents whose genomes are similar or identical to genomes also present in the sample. An epidemiological model that tracks parasite genotypes can reproduce the observed pattern of barcode subsets. Quantification of likelihoods in the model strongly suggests a reduction of transmission from 2006-2010 with a significant rebound in 2012-2013. The reduced transmission and rebound were confirmed directly by incidence data from Thiès. These findings imply that intensive intervention to control malaria results in rapid and dramatic changes in parasite population genomics. The results also suggest that genomics combined with epidemiological modeling may afford prompt, continuous, and cost-effective tracking of progress toward malaria elimination.

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          Author and article information

          Journal
          Proc. Natl. Acad. Sci. U.S.A.
          Proceedings of the National Academy of Sciences of the United States of America
          1091-6490
          0027-8424
          Jun 2 2015
          : 112
          : 22
          Affiliations
          [1 ] Departments of Immunology and Infectious Diseases and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138; rdaniels@hsph.harvard.edu dhartl@oeb.harvard.edu dfwirth@hsph.harvard.edu.
          [2 ] Broad Institute, Cambridge, MA 02142;
          [3 ] Institute for Disease Modeling, Bellevue, WA 98005;
          [4 ] Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, MA 02115; Epidemiology and.
          [5 ] Departments of Immunology and Infectious Diseases and.
          [6 ] Faculty of Medicine and Pharmacy, Cheikh Anta Diop University, Dakar, Senegal;
          [7 ] Senegal National Malaria Control Program, BP 25 270 Dakar-Fann, Senegal;
          [8 ] College of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824; Blantyre Malaria Project, University of Malawi College of Medicine, Blantyre, Malawi; and.
          [9 ] Departments of Immunology and Infectious Diseases and Broad Institute, Cambridge, MA 02142; School of Nursing and Health Sciences, Simmons College, Boston, MA, 02115.
          [10 ] Departments of Immunology and Infectious Diseases and Broad Institute, Cambridge, MA 02142; rdaniels@hsph.harvard.edu dhartl@oeb.harvard.edu dfwirth@hsph.harvard.edu.
          Article
          1505691112
          10.1073/pnas.1505691112
          25941365
          4169b5cc-cb39-443c-9d79-d74eccd97257
          History

          epidemiology,genomics,malaria
          epidemiology, genomics, malaria

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