@article{fdi:010086477,
  title = {{S}patio-temporal variability of malaria incidence in the health district of {K}ati, {M}ali, 2015-2019},
  author = {{K}atile, {A}. and {S}agara, {I}. and {C}issoko, {M}. and {B}ationo, {C}. {S}. and {D}olo, {M}. and {T}hera, {I}. and {T}raore, {S}. and {K}one, {M}. and {D}embele, {P}. and {B}ocoum, {D}. and {S}idibe, {I}. and {S}imaga, {I}. and {S}issoko, {M}. {S}. and {L}andier, {J}ordi and {G}audart, {J}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}ntroduction: {D}espite the implementation of control strategies at the national scale, the malaria burden remains high in {M}ali, with more than 2.8 million cases reported in 2019. {I}n this context, a new approach is needed, which accounts for the spatio-temporal variability of malaria transmission at the local scale. {T}his study aimed to describe the spatio-temporal variability of malaria incidence and the associated meteorological and environmental factors in the health district of {K}ati, {M}ali. {M}ethods: {D}aily malaria cases were collected from the consultation records of the 35 health areas of {K}ati's health district, for the period 2015-2019. {D}ata on rainfall, relative humidity, temperature, wind speed, the normalized difference vegetation index, air pressure, and land use-land cover were extracted from open-access remote sensing sources, while data on the {N}iger {R}iver's height and flow were obtained from the {N}ational {D}epartment of {H}ydraulics. {T}o reduce the dimension and account for collinearity, strongly correlated meteorological and environmental variables were combined into synthetic indicators ({SI}), using a principal component analysis. {A} generalized additive model was built to determine the lag and the relationship between the main {SI}s and malaria incidence. {T}he transmission periods were determined using a change-point analysis. {H}igh-risk clusters (hotspots) were detected using the {S}at{S}can method and were ranked according to risk level, using a classification and regression tree analysis. {R}esults: {T}he peak of the malaria incidence generally occurred in {O}ctober. {P}eak incidence decreased from 60 cases per 1000 person-weeks in 2015, to 27 cases per 1000 person-weeks in 2019. {T}he relationship between the first {SI} (river flow and height, relative humidity, and rainfall) and malaria incidence was positive and almost linear. {A} non-linear relationship was found between the second {SI} (air pressure and temperature) and malaria incidence. {T}wo transmission periods were determined per year: a low transmission period from {J}anuary to {J}uly-corresponding to a persisting transmission during the dry season-and a high transmission period from {J}uly to {D}ecember. {T}he spatial distribution of malaria hotspots varied according to the transmission period. {D}iscussion: {O}ur study confirmed the important variability of malaria incidence and found malaria transmission to be associated with several meteorological and environmental factors in the {K}ati district. {T}he persistence of malaria during the dry season and the spatio-temporal variability of malaria hotspots reinforce the need for innovative and targeted strategies.},
  keywords = {malaria ; environmental factors ; hotspot ; spatio-temporal dynamics ; geoepidemiology ; {MALI}},
  booktitle = {},
  journal = {{I}nternational {J}ournal of {E}nvironmental {R}esearch and {P}ublic {H}ealth},
  volume = {19},
  numero = {21},
  pages = {14361 [18 ]},
  year = {2022},
  DOI = {10.3390/ijerph192114361},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086477},
}