TY - JOUR
T1 - Optimal health and disease management using spatial uncertainty
T2 - a geographic characterization of emergent artemisinin-resistant Plasmodium falciparum distributions in Southeast Asia
AU - Grist, Eric P M
AU - Flegg, Jennifer
AU - Humphreys, Georgina S
AU - Mas, Ignacio Suay
AU - Anderson, Tim J C
AU - Ashley, Elizabeth A.
AU - Day, Nicholas P J
AU - Dhorda, Mehul
AU - Dondorp, Arjen M.
AU - Faiz, Mohammed Abul
AU - Gething, Peter W
AU - Hien, Tran Tinh M
AU - Hlaing, Tin Maung
AU - Imwong, Mallika
AU - Kindermans, Jean Marie
AU - Maude, Richard J.
AU - Mayxay, Mayfong
AU - McDew-White, Marina
AU - Menard, Didier
AU - Nair, Shalini
AU - Nosten, Francois H
AU - Newton, Paul N
AU - Price, Ric N
AU - Pukrittayakamee, Sasithon
AU - Takala-Harrison, Shannon
AU - Smithuis, Frank
AU - Nguyen, Thuy-Nhien
AU - Tun, Kyaw M.
AU - White, Nicholas John
AU - Witkowski, Benoit
AU - Woodrow, Charles J
AU - Fairhurst, Rick M
AU - Sibley, Carol Hopkins
AU - Guerin, Philippe Jean
PY - 2016/10/24
Y1 - 2016/10/24
N2 - Background: Artemisinin-resistant Plasmodium falciparum malaria parasites are now present across much of mainland Southeast Asia, where ongoing surveys are measuring and mapping their spatial distribution. These efforts require substantial resources. Here we propose a generic 'smart surveillance' methodology to identify optimal candidate sites for future sampling and thus map the distribution of artemisinin resistance most efficiently. Methods: The approach uses the 'uncertainty' map generated iteratively by a geostatistical model to determine optimal locations for subsequent sampling. Results: The methodology is illustrated using recent data on the prevalence of the K13-propeller polymorphism (a genetic marker of artemisinin resistance) in the Greater Mekong Subregion. Conclusion: This methodology, which has broader application to geostatistical mapping in general, could improve the quality and efficiency of drug resistance mapping and thereby guide practical operations to eliminate malaria in affected areas.
AB - Background: Artemisinin-resistant Plasmodium falciparum malaria parasites are now present across much of mainland Southeast Asia, where ongoing surveys are measuring and mapping their spatial distribution. These efforts require substantial resources. Here we propose a generic 'smart surveillance' methodology to identify optimal candidate sites for future sampling and thus map the distribution of artemisinin resistance most efficiently. Methods: The approach uses the 'uncertainty' map generated iteratively by a geostatistical model to determine optimal locations for subsequent sampling. Results: The methodology is illustrated using recent data on the prevalence of the K13-propeller polymorphism (a genetic marker of artemisinin resistance) in the Greater Mekong Subregion. Conclusion: This methodology, which has broader application to geostatistical mapping in general, could improve the quality and efficiency of drug resistance mapping and thereby guide practical operations to eliminate malaria in affected areas.
KW - Artemisinin
KW - Drug resistance
KW - Greater Mekong Subregion
KW - Malaria
KW - Surveillance
UR - http://www.scopus.com/inward/record.url?scp=84992360038&partnerID=8YFLogxK
UR - http://download.springer.com/static/pdf/726/art%253A10.1186%252Fs12942-016-0064-6.pdf?originUrl=http%3A%2F%2Fij-healthgeographics.biomedcentral.com%2Farticle%2F10.1186%2Fs12942-016-0064-6&token2=exp=1479788269~acl=%2Fstatic%2Fpdf%2F726%2Fart%25253A10.1186%25252Fs12942-016-0064-6.pdf*~hmac=c60022e7b7803ff6e480ac0311379e85448a22914817d1255757ed428489f673
U2 - 10.1186/s12942-016-0064-6
DO - 10.1186/s12942-016-0064-6
M3 - Article
AN - SCOPUS:84992360038
SN - 1476-072X
VL - 15
SP - 1
EP - 10
JO - International Journal of Health Geographics
JF - International Journal of Health Geographics
IS - 1
M1 - 37
ER -
