@article{fdi:010088801,
  title = {{R}ainfall-driven resuspension of pathogenic {L}eptospira in a leptospirosis hotspot},
  author = {{T}hibeaux, {R}. and {G}enthon, {P}ierre and {G}ovan, {R}. and {S}elmaoui-{F}olcher, {N}. and {T}ramier, {C}. and {K}ainiu, {M}. and {S}oup{\'e}-{G}ilbert, {M}. {E}. and {W}ijesuriya, {K}avya and {G}oarant, {C}.},
  editor = {},
  language = {{ENG}},
  abstract = {{L}eptospirosis is a zoonosis caused by {L}eptospira bacteria present in the urine of mammals. {L}eptospira is able to survive in soils and can be resuspended during rain events. {H}ere, we analyzed the pathogenic {L}eptospira concentration as a function of hydrological variables in a leptospirosis hot spot. {A} total of 226 samples were collected at the outlet of a 3 km2 watershed degraded by ungulate mammals (deer and feral pigs) and rats which are reservoirs for leptospirosis. {W}ater samples collected at the beginning of a rain event following a dry period contained high concentrations of pathogenic {L}eptospira. {T}he concentration was generally correlated with the water level and the suspended matter concentration ({SMC}) during the main flood event. {A} secondary peak of pathogenic {L}eptospira was sometimes detected after the main flood and in slightly turbid waters. {L}astly, the pathogenic {L}eptospira concentration was extremely high at the end of a wet season. {T}he pathogenic {L}eptospira concentrations could not be explained by a linear combination of hydrological variables (e.g. the rainfall, water level, {SMC} and soil moisture). {H}owever, nonlinear machine learning models of rainfall data only provided a fair fit to the observations and explained 75 % of the variance in the log10-transformed pathogenic {L}eptospira concentration. {A} comparison of identical machine learning models for the water level, {SMC} and pathogenic {L}eptospira concentration showed that the residual error in the {L}eptospira concentration was due to not only the small dataset but also the intrinsic characteristics of the signal. {O}ur results support the hypothesis whereby pathogenic {L}eptospira survive at different depths in soils and superficial river sediments (depending on their water saturation) and are transferred to surface water during erosion. {T}hese results might help to refine leptospirosis warnings given to the local population. {F}uture research should be focused on larger watersheds in more densely populated areas.},
  keywords = {{L}eptospirosis ; {H}ydrology ; {M}achine learning ; {H}ydro{T}hiem observatory ; {N}ew {C}aledonia ; {NOUVELLE} {CALEDONIE}},
  booktitle = {},
  journal = {{S}cience of the {T}otal {E}nvironment},
  volume = {911},
  numero = {},
  pages = {168700 [12 ]},
  ISSN = {0048-9697},
  year = {2024},
  DOI = {10.1016/j.scitotenv.2023.168700},
  URL = {https://www.documentation.ird.fr/hor/fdi:010088801},
}