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      Use cases for genetic epidemiology in malaria elimination

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          Abstract

          Background

          While traditional epidemiological approaches have supported significant reductions in malaria incidence across many countries, higher resolution information about local and regional malaria epidemiology will be needed to efficiently target interventions for elimination. The application of genetic epidemiological methods for the analysis of parasite genetics has, thus far, primarily been confined to research settings. To illustrate how these technical methods can be used to advance programmatic and operational needs of National Malaria Control Programmes (NMCPs), and accelerate global progress to eradication, this manuscript presents seven use cases for which genetic epidemiology approaches to parasite genetic data are informative to the decision-making of NMCPs.

          Methods

          The use cases were developed through a highly iterative process that included an extensive review of the literature and global guidance documents, including the 2017 World Health Organization’s Framework for Malaria Elimination, and collection of stakeholder input. Semi-structured interviews were conducted with programmatic and technical experts about the needs and opportunities for genetic epidemiology methods in malaria elimination.

          Results

          Seven use cases were developed: Detect resistance, Assess drug resistance gene flow, Assess transmission intensity, Identify foci, Determine connectivity of parasite populations, Identify imported cases, and Characterize local transmission chains. The method currently used to provide the information sought, population unit for implementation, the pre-conditions for using these approaches, and post-conditions intended as a product of the use case were identified for each use case.

          Discussion

          This framework of use cases will prioritize research and development of genetic epidemiology methods that best achieve the goals of NMCPs, and ultimately, inform the establishment of normative policy guidance for their uses. With significant engagement of stakeholders from malaria endemic countries and collaboration with local programme experts to ensure strategic implementation, genetic epidemiological approaches have tremendous potential to accelerate global malaria elimination efforts.

          Electronic supplementary material

          The online version of this article (10.1186/s12936-019-2784-0) contains supplementary material, which is available to authorized users.

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          Most cited references30

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          Modeling malaria genomics reveals transmission decline and rebound in Senegal.

          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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            Quantifying connectivity between local Plasmodium falciparum malaria parasite populations using identity by descent

            With the rapidly increasing abundance and accessibility of genomic data, there is a growing interest in using population genetic approaches to characterize fine-scale dispersal of organisms, providing insight into biological processes across a broad range of fields including ecology, evolution and epidemiology. For sexually recombining haploid organisms such as the human malaria parasite P. falciparum, however, there have been no systematic assessments of the type of data and methods required to resolve fine scale connectivity. This analytical gap hinders the use of genomics for understanding local transmission patterns, a crucial goal for policy makers charged with eliminating this important human pathogen. Here we use data collected from four clinics with a catchment area spanning approximately 120 km of the Thai-Myanmar border to compare the ability of divergence (F ST ) and relatedness based on identity by descent (IBD) to resolve spatial connectivity between malaria parasites collected from proximal clinics. We found no relationship between inter-clinic distance and F ST , likely due to sampling of highly related parasites within clinics, but a significant decline in IBD-based relatedness with increasing inter-clinic distance. This association was contingent upon the data set type and size. We estimated that approximately 147 single-infection whole genome sequenced parasite samples or 222 single-infection parasite samples genotyped at 93 single nucleotide polymorphisms (SNPs) were sufficient to recover a robust spatial trend estimate at this scale. In summary, surveillance efforts cannot rely on classical measures of genetic divergence to measure P. falciparum transmission on a local scale. Given adequate sampling, IBD-based relatedness provides a useful alternative, and robust trends can be obtained from parasite samples genotyped at approximately 100 SNPs.
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              Phenotypic and genotypic characterisation of drug-resistant Plasmodium vivax.

              In this review we present recent developments in the analysis of Plasmodium vivax clinical trials and ex vivo drug-susceptibility assays, as well approaches currently being used to identify molecular markers of drug resistance. Clinical trials incorporating the measurement of in vivo drug concentrations and parasite clearance times are needed to detect early signs of resistance. Analysis of P. vivax growth dynamics ex vivo have defined the criteria for acceptable assay thresholds for drug susceptibility testing, and their subsequent interpretation. Genotyping and next-generation sequencing studies in P. vivax field isolates are set to transform our understanding of the molecular mechanisms of drug resistance. Copyright © 2012 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                erin.stuckey@gatesfoundation.org
                Journal
                Malar J
                Malar. J
                Malaria Journal
                BioMed Central (London )
                1475-2875
                7 May 2019
                7 May 2019
                2019
                : 18
                : 163
                Affiliations
                [1 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Epidemiology, , University of Washington, ; Seattle, WA USA
                [2 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Global Health, , University of Washington, ; Seattle, WA USA
                [3 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Health Services, , University of Washington, ; Seattle, WA USA
                [4 ]ISNI 0000000122986657, GRID grid.34477.33, Strategic Analysis Research and Training Center, , University of Washington, ; Seattle, WA USA
                [5 ]ISNI 0000 0000 8990 8592, GRID grid.418309.7, Bill and Melinda Gates Foundation, ; Seattle, WA USA
                Author information
                http://orcid.org/0000-0002-8047-9212
                Article
                2784
                10.1186/s12936-019-2784-0
                6503548
                31064369
                2ed96b5d-42cd-40e7-a09f-2aa6caa7c908
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 14 December 2018
                : 22 April 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000865, Bill and Melinda Gates Foundation;
                Award ID: OPP1155935
                Categories
                Review
                Custom metadata
                © The Author(s) 2019

                Infectious disease & Microbiology
                malaria,use case,genetic epidemiology,drug resistance,nmcp,gene flow,eradication,surveillance,policy development,transmission

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