@article{fdi:010094915,
  title = {{T}he spatiotemporal ecology of {O}ropouche virus across {L}atin {A}merica : a multidisciplinary, laboratory-based, modelling study},
  author = {{F}ischer, {C}. and {F}rühauf, {A}. and {I}nchauste, {L}. and {C}assiano, {M}. {H}. {A}. and {R}amirez, {H}. {A}. and {B}arth{\'e}l{\'e}my, {K}. and {M}achicado, {L}. {B}. and {B}ozza, {F}. {A}. and {B}rites, {C}. and {C}abada, {M}. {M}. and {S}ánchez, {C}. {A}. {C}. and {R}odriguez, {A}. {C}. and de {L}amballerie, {X}. and {P}eralta, {R}. {D}. and {O}liveira, {E}. {F}. {D}. and {C}ell{\`e}s, {M}. {D}. {D}. and {F}ranco-{M}uñoz, {C}. and {M}endoza, {M}. {P}. {G}. and {N}ogueira, {M}. {G}. and {G}{\'e}lvez-{R}amirez, {R}. {M}. and {G}onzalez, {M}. {G}. and {G}otuzzo, {E}. and {K}ramer-{S}chadt, {S}. and {K}uivanen, {S}. and {L}aiton-{D}onato, {K}. and {L}ozano-{P}arra, {A}. and {M}álaga-{T}rillo, {E}. and {A}lva, {D}. {V}. {M}. and {M}iss{\'e}, {D}oroth{\'e}e and {M}oreira-{S}oto, {A}. and {S}ouza, {T}. {M}. and {M}ozo, {K}. and {N}etto, {E}. {M}. and {O}lk, {N}. and {P}achamora, {J}. {M}. and {J}orge, {C}. {P}. and {A}studillo, {A}. {M}. {P}. and {P}iche-{O}vares, {M}. and {P}riet, {S}. and {R}incón-{O}rozco, {B}. and {R}omero-{Z}úñiga, {J}. {J}. and {C}isneros, {S}. {P}. {S}. and {S}t{\¨o}cker, {A}. and {U}galde, {J}. {C}. {V}. and {C}enteno, {L}. {A}. {V}. and {W}enzler-{M}eya, {M}. and {Z}evallos, {J}. {C}. and {D}rexler, {J}. {F}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground {L}atin {A}merica has been experiencing an {O}ropouche virus ({OROV}) outbreak of unprecedented magnitude and spread since 2023-24 for unknown reasons. {W}e aimed to identify risk predictors of and areas at risk for {OROV} transmission. {M}ethods {I}n this multidisciplinary, laboratory-based, modelling study, we retrospectively tested anonymised serum samples collected between 2001 and 2022 for studies on virus epidemiology and medical diagnostics in {B}olivia, {B}razil, {C}olombia, {C}osta {R}ica, {E}cuador, and {P}eru with nucleoprotein-based commercial {ELISA}s for {OROV}-specific {I}g{G} and {I}g{M} antibodies. {S}erum samples positive for {I}g{G} from different ecological regions and sampling years were tested against {G}uaroa virus and two {OROV} glycoprotein reassortants ({I}quitos virus and {M}adre de {D}ios virus) via plaque reduction neutralisation testing ({PRNT}) to validate {I}g{G} {ELISA} specificity and support antigenic cartography. {T}hree {OROV} strains were included in the neutralisation testing, a {C}uban {OROV} isolate from the 2023-24 outbreak, a contemporary {P}eruvian {OROV} isolate taken from a patient in 2020, and a historical {OROV} isolate from {B}razil. {W}e analysed the serological data alongside age, sex, cohort, and geographical residence data for the serum samples; reported {OROV} incidence data; and vector occurrence data to explore {OROV} transmission in ecologically different regions of {L}atin {A}merica. {W}e used the {M}ax{E}nt machine learning methodology to spatially analyse and predict {OROV} infection risk across {L}atin {A}merica, fitting one model with presence-absence serological data (seropositive results were recorded as presence and seronegative results were recorded as absence) and one model with presence-only, reported incidence data from 2024. {W}e computed marginal dependency plots, variable contribution, and permutation metrics to analyse the impact of socioecological predictors and fitted a generalised linear mixed-effects model with logit link and binary error structure to analyse the potential effects of age, sex, or cohort type bias and interactions between age or sex and cohort type in our serological data. {W}e conducted antigenic cartography and evolutionary characterisations of all available genomic sequences for all three {OROV} genome segments from the {N}ational {C}enter for {B}iotechnology {I}nformation, including branch-specific selection pressure analysis and the construction of {OROV} phylogenetic trees. {F}indings {I}n total, 9420 serum samples were included in this study, representing 76 provinces in the six {L}atin {A}merican countries previously mentioned. {T}he sex distribution across the combined cohorts was 48% female (4237 of 8910 samples with available data) and 52% male (4673 of 8910 samples) and the mean age was 29<middle dot>5 years (range 0-95 years). {T}he samples were collected from census-based cohorts, cohorts of healthy individuals, and cohorts of febrile patients receiving routine health care. {T}he average {OROV} {I}g{G} antibody detection rate was 6<middle dot>3% (95% {CI} 5<middle dot>8-6<middle dot>8), with substantial regional heterogeneity. {T}he presence-absence, serology-based model predicted high-risk areas for {OROV} transmission in the {A}mazon {R}iver basin, around the coastal and southern areas of {B}razil, and in parts of central {A}merica and the {C}aribbean islands, consistent with case data from the 2023-24 outbreak reported by the {P}an {A}merican {H}ealth {O}rganization. {A}reas with a high predicted risk of {OROV} transmission with the serology-based model showed a statistically significant positive correlation with state-level incidence rates per 100 000 people in 2024 (generalised linear model, p=0<middle dot>0003). {T}he area under the curve estimates were 0<middle dot>79 (95% {CI} 0<middle dot>78-0<middle dot>80) for the serology-based model and 0<middle dot>66 (95% {CI} 0<middle dot>65-0<middle dot>66) for the presence-only incidence-based model. {L}ongitudinal diagnostic testing of serum samples from cohorts of febrile patients suggested constant circulation of {OROV} in endemic regions at varying intensity. {C}limate variables accounted for more than 60% of variable contribution in both the serology-based and incidence-based models. {A}ntigenic cartography, evolutionary analyses, and in-vitro growth comparisons showed clear differentiation between {OROV} and its glycoprotein reassortants, but not between the three different {OROV} strains. {PRNT} titres of {OROV}-neutralising serum samples were strongly correlated between all three tested {OROV} isolates (r>0<middle dot>83; p<0<middle dot>0001) but were not correlated with the two glycoprotein reassortants. {I}nterpretation {O}ur data suggest that climatic factors are major drivers of {OROV} spread and were potentially exacerbated during 2024 by extreme weather events. {OROV} glycoprotein reassortants, but not individual {OROV} strains, probably have distinct antigenicity. {P}reparedness for {OROV} outbreaks requires enhanced diagnostics, surveillance, and vector control in current and future endemic areas, which could all be informed by the risk predictions presented in this {A}rticle.},
  keywords = {{AMERIQUE} {LATINE} ; {BOLIVIE} ; {BRESIL} ; {COLOMBIE} ; {COSTA} {RICA} ; {EQUATEUR} ; {PEROU}},
  booktitle = {},
  journal = {{L}ancet {I}nfectious {D}iseases},
  volume = {25},
  numero = {9},
  pages = {[13 p.]},
  ISSN = {1473-3099},
  year = {2025},
  DOI = {10.1016/s1473-3099(25)00110-0},
  URL = {https://www.documentation.ird.fr/hor/fdi:010094915},
}