title: Climate-based models for understanding and forecasting dengue epidemics.
abstract: BACKGROUND
  Dengue dynamics are driven by complex interactions between human-hosts, mosquito-vectors and viruses that are influenced by environmental and climatic factors. The objectives of this study were to analyze and model the relationships between climate, Aedes aegypti vectors and dengue outbreaks in Noumea (New Caledonia), and to provide an early warning system.
  
  
  RESULTS
  Epidemiological and meteorological data were analyzed from 1971 to 2010 in Noumea. Entomological surveillance indices were available from March 2000 to December 2009. During epidemic years, the distribution of dengue cases was highly seasonal. The epidemic peak (March-April) lagged the warmest temperature by 1-2 months and was in phase with maximum precipitations, relative humidity and entomological indices. Significant inter-annual correlations were observed between the risk of outbreak and summertime temperature, precipitations or relative humidity but not ENSO. Climate-based multivariate non-linear models were developed to estimate the yearly risk of dengue outbreak in Noumea. The best explicative meteorological variables were the number of days with maximal temperature exceeding 32°C during January-February-March and the number of days with maximal relative humidity exceeding 95% during January. The best predictive variables were the maximal temperature in December and maximal relative humidity during October-November-December of the previous year. For a probability of dengue outbreak above 65% in leave-one-out cross validation, the explicative model predicted 94% of the epidemic years and 79% of the non epidemic years, and the predictive model 79% and 65%, respectively.
  
  
  CONCLUSIONS
  The epidemic dynamics of dengue in Noumea were essentially driven by climate during the last forty years. Specific conditions based on maximal temperature and relative humidity thresholds were determinant in outbreaks occurrence. Their persistence was also crucial. An operational model that will enable health authorities to anticipate the outbreak risk was successfully developed. Similar models may be developed to improve dengue management in other countries.
authors.affiliation: UMR190, Emergence of Viral Pathologies, Institute of Research for the Development, Aix-Marseille University, Marseille, France. elodie.descloux@hotmail.com
authors.forename: Elodie
authors.initials: E
authors.lastname: Descloux
authors.forename: Morgan
authors.initials: M
authors.lastname: Mangeas
authors.forename: Christophe Eugène
authors.initials: CE
authors.lastname: Menkes
authors.forename: Matthieu
authors.initials: M
authors.lastname: Lengaigne
authors.forename: Anne
authors.initials: A
authors.lastname: Leroy
authors.forename: Temaui
authors.initials: T
authors.lastname: Tehei
authors.forename: Laurent
authors.initials: L
authors.lastname: Guillaumot
authors.forename: Magali
authors.initials: M
authors.lastname: Teurlai
authors.forename: Ann-Claire
authors.initials: AC
authors.lastname: Gourinat
authors.forename: Justus
authors.initials: J
authors.lastname: Benzler
authors.forename: Anne
authors.initials: A
authors.lastname: Pfannstiel
authors.forename: Jean-Paul
authors.initials: JP
authors.lastname: Grangeon
authors.forename: Nicolas
authors.initials: N
authors.lastname: Degallier
authors.forename: Xavier
authors.initials: X
authors.lastname: De Lamballerie
mesh_terms: D000330:Aedes / Q000254:growth & development*; D000818:Animals; D002980:Climate; D003715:Dengue / Q000453:epidemiology*; D004196:Disease Outbreaks; D004199:Disease Vectors; D005260:Female; D006801:Humans; D006813:Humidity; D015233:Models, Statistical; D009510:New Caledonia / Q000453:epidemiology; D011891:Rain; D012621:Seasons; D013696:Temperature