About: Detecting geospatial patterns of Plasmodium falciparum parasite migration in Cambodia using optimized estimated effective migration surfaces

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Detecting geospatial patterns of Plasmodium falciparum parasite migration in Cambodia using optimized estimated effective migration surfaces Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Detecting geospatial patterns of Plasmodium falciparum parasite migration in Cambodia using optimized estimated effective migration surfaces
Creator	Lon, Chanthap Saunders, David Pukrittayakamee, Sasithon Li, Yao Dondorp, Arjen Hien, Tran Fukuda, Mark Fairhurst, Rick Plowe, Christopher Shetty, Amol Spring, Michele Stewart, Kathleen Takala-Harrison, Shannon O'connor, Timothy
topic	covid:2db5849f31613681bea16d531009fbe087b8a0c7#this
source	PMC
abstract	BACKGROUND: Understanding the genetic structure of natural populations provides insight into the demographic and adaptive processes that have affected those populations. Such information, particularly when integrated with geospatial data, can have translational applications for a variety of fields, including public health. Estimated effective migration surfaces (EEMS) is an approach that allows visualization of the spatial patterns in genomic data to understand population structure and migration. In this study, we developed a workflow to optimize the resolution of spatial grids used to generate EEMS migration maps and applied this optimized workflow to estimate migration of Plasmodium falciparum in Cambodia and bordering regions of Thailand and Vietnam. METHODS: The optimal density of EEMS grids was determined based on a new workflow created using density clustering to define genomic clusters and the spatial distance between genomic clusters. Topological skeletons were used to capture the spatial distribution for each genomic cluster and to determine the EEMS grid density; i.e., both genomic and spatial clustering were used to guide the optimization of EEMS grids. Model accuracy for migration estimates using the optimized workflow was tested and compared to grid resolutions selected without the optimized workflow. As a test case, the optimized workflow was applied to genomic data generated from P. falciparum sampled in Cambodia and bordering regions, and migration maps were compared to estimates of malaria endemicity, as well as geographic properties of the study area, as a means of validating observed migration patterns. RESULTS: Optimized grids displayed both high model accuracy and reduced computing time compared to grid densities selected in an unguided manner. In addition, EEMS migration maps generated for P. falciparum using the optimized grid corresponded to estimates of malaria endemicity and geographic properties of the study region that might be expected to impact malaria parasite migration, supporting the validity of the observed migration patterns. CONCLUSIONS: Optimized grids reduce spatial uncertainty in the EEMS contours that can result from user-defined parameters, such as the resolution of the spatial grid used in the model. This workflow will be useful to a broad range of EEMS users as it can be applied to analyses involving other organisms of interest and geographic areas.
has issue date	2020-04-10(xsd:dateTime)
bibo:doi	10.1186/s12942-020-00207-3
bibo:pmid	32276636
has license	cc-by
sha1sum (hex)	2db5849f31613681bea16d531009fbe087b8a0c7
schema:url	https://doi.org/10.1186/s12942-020-00207-3
resource representing a document's title	Detecting geospatial patterns of Plasmodium falciparum parasite migration in Cambodia using optimized estimated effective migration surfaces
has PubMed Central identifier	PMC7149848
has PubMed identifier	32276636
schema:publication	Int J Health Geogr
resource representing a document's body	covid:2db5849f31613681bea16d531009fbe087b8a0c7#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/2db5849f31613681bea16d531009fbe087b8a0c7
is schema:about of	named entity 'optimize' named entity 'Vietnam' named entity 'migration' named entity 'Plasmodium' named entity 'Understanding' named entity 'parasite' named entity 'workflow' named entity 'regions' named entity 'genomic' named entity 'effective' named entity 'genomic data' named entity 'Plasmodium falciparum' named entity 'Cambodia' named entity 'Plasmodium falciparum' named entity 'parasite' named entity 'Pursat' named entity 'Linux' named entity 'malaria parasite' named entity 'Pailin District' named entity 'malaria' named entity 'Mekong River' named entity 'Vietnam' named entity 'malaria parasites' named entity 'Canis lupus' named entity 'parasite' named entity 'P. falciparum' named entity 'Gene Expression Omnibus' named entity 'Malaria Atlas Project' named entity 'drug resistance' named entity 'endemicity' named entity 'genomic data' named entity 'multidimensional scaling' named entity 'Andes Mountains' named entity 'subpopulation' named entity 'blunt-nosed leopard lizard' named entity 'deme' named entity 'Cambodia' named entity 'Koh Kong' named entity 'EEMS' named entity 'time frame' named entity 'Battambang' named entity 'cardamom' named entity 'P. falciparum' named entity 'Laos' named entity 'genetic dissimilarity' named entity 'SNPs' named entity 'P. falciparum' named entity 'EEMS' named entity 'parasite' named entity 'P. falciparum' named entity 'maximum likelihood' named entity 'EEMS' named entity 'Thailand' named entity 'Genomic data' named entity 'P. falciparum' named entity 'influenza virus' named entity 'Kampong Thom' named entity 'Tonle Sap Lake' named entity 'population structure' named entity 'P. falciparum' named entity 'genetic diversity' named entity 'Thailand' named entity 'Thailand' named entity 'spatial grid' named entity 'P. falciparum' named entity 'endemicity'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software