@article{fdi:010088327,
  title = {{B}ias-corrected {CMIP}5 projections for climate change and assessments of impact on {M}alaria in {S}enegal under the {VECTRI} {M}odel},
  author = {{F}all, {P}. and {D}iouf, {I}. and {D}eme, {A}. and {D}iouf, {S}. and {S}ene, {D}. and {S}ultan, {B}enjamin and {F}amien, {A}.{M}. and {J}anicot, {S}erge},
  editor = {},
  language = {{ENG}},
  abstract = {{O}n the climate-health issue, studies have already attempted to understand the influence of climate change on the transmission of malaria. {E}xtreme weather events such as floods, droughts, or heat waves can alter the course and distribution of malaria. {T}his study aims to understand the impact of future climate change on malaria transmission using, for the first time in {S}enegal, the {ICTP}'s community-based vector-borne disease model, {TRI}este ({VECTRI}). {T}his biological model is a dynamic mathematical model for the study of malaria transmission that considers the impact of climate and population variability. {A} new approach for {VECTRI} input parameters was also used. {A} bias correction technique, the cumulative distribution function transform ({CDF}-t) method, was applied to climate simulations to remove systematic biases in the {C}oupled {M}odel {I}ntercomparison {P}roject {P}hase 5 ({CMIP}5) global climate models ({GCM}s) that could alter impact predictions. {B}eforehand, we use reference data for validation such as {CPC} global unified gauge-based analysis of daily precipitation ({CPC} for {C}limate {P}rediction {C}enter), {ERA}5-land reanalysis, {C}limate {H}azards {I}nfra{R}ed {P}recipitation with {S}tation data ({CHIRPS}), and {A}frican {R}ainfall {C}limatology 2.0 ({ARC}2). {T}he results were analyzed for two {CMIP}5 scenarios for the different time periods: assessment: 1983-2005; near future: 2006-2028; medium term: 2030-2052; and far future: 2077-2099). {T}he validation results show that the models reproduce the annual cycle well. {E}xcept for the {IPSL}-{CM}5{B} model, which gives a peak in {A}ugust, all the other models ({ACCESS}1-3, {C}an{ESM}2, {CSIRO}, {CMCC}-{CM}, {CMCC}-{CMS}, {CNRM}-{CM}5, {GFDL}-{CM}3, {GFDL}-{ESM}2{G}, {GFDL}-{ESM}2{M}, inmcm4, and {IPSL}-{CM}5{B}) agree with the validation data on a maximum peak in {S}eptember with a period of strong transmission in {A}ugust-{O}ctober. {W}ith spatial variation, the {CMIP}5 model simulations show more of a difference in the number of malaria cases between the south and the north. {M}alaria transmission is much higher in the south than in the north. {H}owever, the results predicted by the models on the occurrence of malaria by 2100 show differences between the {RCP}8.5 scenario, considered a high emission scenario, and the {RCP}4.5 scenario, considered an intermediate mitigation scenario. {T}he {C}an{ESM}2, {CMCC}-{CM}, {MCC}-{CMS}, inmcm4, and {IPSL}-{CM}5{B} models predict decreases with the {RCP}4.5 scenario. {H}owever, {ACCESS}1-3, {CSIRO}, {NRCM}-{CM}5, {GFDL}-{CM}3, {GFDL}-{ESM}2{G}, and {GFDL}-{ESM}2{M} predict increases in malaria under all scenarios ({RCP}4.5 and {RCP}8.5). {T}he projected decrease in malaria in the future with these models is much more visible in the {RCP}8.5 scenario. {T}he results of this study are of paramount importance in the climate-health field. {T}hese results will assist in decision-making and will allow for the establishment of preventive surveillance systems for local climate-sensitive diseases, including malaria, in the targeted regions of {S}enegal.},
  keywords = {{SENEGAL}},
  booktitle = {},
  journal = {{T}ropical {M}edicine and {I}nfectious {D}isease},
  volume = {8},
  numero = {6},
  pages = {310 [29 ]},
  ISSN = {2414-6366},
  year = {2023},
  DOI = {10.3390/tropicalmed8060310},
  URL = {https://www.documentation.ird.fr/hor/fdi:010088327},
}