@article{fdi:010083243,
  title = {{S}patio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in {D}iebougou health district, {B}urkina {F}aso, 2016-2017},
  author = {{B}ationo, {C}. {S}. and {G}audart, {J}. and {D}ieng, {S}. and {C}issoko, {M}. and {T}aconet, {P}aul and {O}uedraogo, {B}. and {S}ome, {A}. and {Z}ongo, {I}. and {S}oma, {D}. {D}. and {T}ougri, {G}. and {D}abire, {R}. {K}. and {K}offi, {A}. and {P}ennetier, {C}{\'e}dric and {M}oiroux, {N}icolas},
  editor = {},
  language = {{ENG}},
  abstract = {{M}alaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. {T}his study aimed to identify the spatial distribution of malaria hotspots at the village level in {D}iebougou health district, {B}urkina {F}aso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centres ({HC}s). {C}ase data for 27 villages were collected in 13 {HC}s. {M}eteorological data were obtained through remote sensing. {T}wo synthetic meteorological indicators ({SMI}s) were created to summarize meteorological variables. {S}patial hotspots were detected using the {K}ulldorf scanning method. {A} {G}eneral {A}dditive {M}odel was used to determine the time lag between cases and {SMI}s and to evaluate the effect of {SMI}s and distance to {HC} on the temporal evolution of malaria cases. {T}he multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak. {O}verall, the incidence rate in the area was 429.13 cases per 1000 person-year with important spatial and temporal heterogeneities. {F}our spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1000 person-year. {T}he hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1750.75 cases per 1000 person-years. {T}he multivariate analysis found greater variability in incidence between {HC}s than between villages linked to the same {HC}. {T}he time lag that generated the better predictions of cases was 9 weeks for {SMI}1 (positively correlated with precipitation variables) and 16 weeks for {SMI}2 (positively correlated with temperature variables. {T}he prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks. {T}his analysis of malaria cases in {D}iebougou health district, {B}urkina {F}aso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns.},
  keywords = {{BURKINA} {FASO} ; {DIEBOUGOU}},
  booktitle = {},
  journal = {{S}cientific {R}eports - {N}ature},
  volume = {11},
  numero = {1},
  pages = {20027 [12 p.]},
  ISSN = {2045-2322},
  year = {2021},
  DOI = {10.1038/s41598-021-99457-9},
  URL = {https://www.documentation.ird.fr/hor/fdi:010083243},
}