@article{fdi:010066865,
  title = {{M}apping a knowledge-based malaria hazard index related to landscape using remote sensing : application to the cross-border area between {F}rench {G}uiana and {B}razil},
  author = {{L}i, {Z}. {C}. and {R}oux, {E}mmanuel and {D}essay, {N}adine and {G}irod, {R}. and {S}tefani, {A}. and {N}acher, {M}. and {M}oiret, {A}drien and {S}eyler, {F}r{\'e}d{\'e}rique},
  editor = {},
  language = {{ENG}},
  abstract = {{M}alaria remains one of the most common vector-borne diseases in the world and the definition of novel control strategies can benefit from the modeling of transmission processes. {H}owever, data-driven models are often difficult to build, as data are very often incomplete, heterogeneous in nature and in quality, and/or biased. {I}n this context, a knowledge-based approach is proposed to build a robust and general landscape-based hazard index for malaria transmission that is tailored to the {A}mazonian region. {A} partial knowledge-based model of the risk of malaria transmission in the {A}mazonian region, based on landscape features and extracted from a systematic literature review, was used. {S}patialization of the model was obtained by generating land use and land cover maps of the cross-border area between {F}rench {G}uiana and {B}razil, followed by computing and combining landscape metrics to build a set of normalized landscape-based hazard indices. {A}n empirical selection of the best index was performed by comparing the indices in terms of adequacy with the knowledge-based model, intelligibility and correlation with {P}. falciparum incidence rates. {T}he selected index is easy to interpret and successfully represents the current knowledge about the role played by landscape patterns in malaria transmission within the study area. {I}t was significantly associated with {P}. falciparum incidence rates, using the {P}earson and {S}pearman correlation coefficients (up to 0.79 and 0.75, respectively; p-value <0.001), and the linear regression coefficient of determination (reaching 0.63; p-values <0.001). {T}his study establishes a spatial knowledge-driven, landscape-based hazard malaria index using remote sensing that can be easily produced on a regular basis and might be useful for malaria prediction, surveillance, and control.},
  keywords = {remote sensing ; land use and land cover ; landscape metric ; knowledge-based hazard modeling ; malaria ; cross-border area between {F}rench {G}uiana and {B}razil ; {GUYANE} {FRANCAISE} ; {BRESIL}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {8},
  numero = {4},
  pages = {art. 319 [22 p.]},
  ISSN = {2072-4292},
  year = {2016},
  DOI = {10.3390/rs8040319},
  URL = {https://www.documentation.ird.fr/hor/fdi:010066865},
}