@inproceedings{fdi:010089916,
  title = {{M}alaria risk mapping in cross-border area between {F}rench {G}uiana and {B}razil : supporting malaria elimination plans [poster]},
  author = {{R}oux, {E}mmanuel and {M}oua, {Y}i and {D}o {S}ocorro {M}endon{\c{c}}a {G}omes, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{C}ross-border malaria is a major obstacle for malaria elimination as stated by {W}angdi et al. (2015). {I}n residual transmission foci of the cross-border areas, the high spatial resolution risk mapping would help defining and/or evaluating context-dependent surveillance, control and elimination actions. {H}igh spatial resolution remote sensing ({RS}) permits to characterize the environment, landscape and human settlements independently of international limits. {H}owever, {RS}-based risk mapping of vectorborne diseases rarely specifies, in a complete and standard way, the underlying risk model.  {H}ere, we propose to explicit the different risks, their links and determinants, to better specify and justify the malaria risk estimation process and better evaluate the results significance. {W}ith the help of the actors involved in malaria surveillance and control in the region, we first built and formalized in {U}nified {M}odeling {L}anguage ({UML}) a systemic and holistic conceptual model of the malaria risks and their determinants. {T}hen, spatialized and qualified indicators related to the presence probability of the main malaria vector in the region, the susceptibility of the landscape to favor human-vector exposure, and the human presence, density and activities, were combined by means of a multiplicative aggregation procedure. {T}he conceptual model permitted to our mapping approach to be better justified, positioned and qualified. {W}e state this approach can help the {RS}-based risk mapping to actually enter the public health practice, by facilitating the understanding and informed interpretation of the method and results by the health actors, especially in cross-border contexts where {RS} data are particularly relevant.},
  keywords = {{GUYANE} {FRANCAISE} ; {BRESIL}},
  numero = {},
  pages = {1  multigr.},
  booktitle = {},
  year = {2022},
  URL = {https://www.documentation.ird.fr/hor/fdi:010089916},
}