@article{fdi:010068360,
  title = {{D}ynamical mapping of {A}nopheles darlingi densities in a residual malaria transmission area of {F}rench {G}uiana by using remote sensing and meteorological data},
  author = {{A}dde, {A}. and {R}oux, {E}mmanuel and {M}angeas, {M}organ and {D}essay, {N}adine and {N}acher, {M}. and {D}usfour, {I}. and {G}irod, {R}. and {B}riolant, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{L}ocal variation in the density of {A}nopheles mosquitoes and the risk of exposure to bites are essential to explain the spatial and temporal heterogeneities in the transmission of malaria. {V}ector distribution is driven by environmental factors. {B}ased on variables derived from satellite imagery and meteorological observations, this study aimed to dynamically model and map the densities of {A}nopheles darlingi in the municipality of {S}aint-{G}eorges de {I}'{O}yapock ({F}rench {G}uiana). {L}ongitudinal sampling sessions of {A}n. darlingi densities were conducted between {S}eptember 2012 and {O}ctober 2014. {L}andscape and meteorological data were collected and processed to extract a panel of variables that were potentially related to {A}n. darlingi ecology. {B}ased on these data, a robust methodology was formed to estimate a statistical predictive model of the spatial-temporal variations in the densities of {A}n. darlingi in {S}aint-{G}eorges de l'{O}yapock. {T}he final cross-validated model integrated two landscape variables dense forest surface and built surface together with four meteorological variables related to rainfall, evapotranspiration, and the minimal and maximal temperatures. {E}xtrapolation of the model allowed the generation of predictive weekly maps of {A}n. darlingi densities at a resolution of 10-m. {O}ur results supported the use of satellite imagery and meteorological data to predict malaria vector densities. {S}uch fine-scale modeling approach might be a useful tool for health authorities to plan control strategies and social communication in a cost-effective, targeted, and timely manner.},
  keywords = {{GUYANE} {FRANCAISE}},
  booktitle = {},
  journal = {{P}los {O}ne},
  volume = {11},
  numero = {10},
  pages = {e0164685 [20 p.]},
  ISSN = {1932-6203},
  year = {2016},
  DOI = {10.1371/journal.pone.0164685},
  URL = {https://www.documentation.ird.fr/hor/fdi:010068360},
}