title: Testing a multi-malaria-model ensemble against 30 years of data in the Kenyan highlands.
abstract: BACKGROUND
  Multi-model ensembles could overcome challenges resulting from uncertainties in models' initial conditions, parameterization and structural imperfections. They could also quantify in a probabilistic way uncertainties in future climatic conditions and their impacts.
  
  
  METHODS
  A four-malaria-model ensemble was implemented to assess the impact of long-term changes in climatic conditions on Plasmodium falciparum malaria morbidity observed in Kericho, in the highlands of Western Kenya, over the period 1979-2009. Input data included quality controlled temperature and rainfall records gathered at a nearby weather station over the historical periods 1979-2009 and 1980-2009, respectively. Simulations included models' sensitivities to changes in sets of parameters and analysis of non-linear changes in the mean duration of host's infectivity to vectors due to increased resistance to anti-malarial drugs.
  
  
  RESULTS
  The ensemble explained from 32 to 38% of the variance of the observed P. falciparum malaria incidence. Obtained R²-values were above the results achieved with individual model simulation outputs. Up to 18.6% of the variance of malaria incidence could be attributed to the +0.19 to +0.25°C per decade significant long-term linear trend in near-surface air temperatures. On top of this 18.6%, at least 6% of the variance of malaria incidence could be related to the increased resistance to anti-malarial drugs. Ensemble simulations also suggest that climatic conditions have likely been less favourable to malaria transmission in Kericho in recent years.
  
  
  CONCLUSIONS
  Long-term changes in climatic conditions and non-linear changes in the mean duration of host's infectivity are synergistically driving the increasing incidence of P. falciparum malaria in the Kenyan highlands. User-friendly, online-downloadable, open source mathematical tools, such as the one presented here, could improve decision-making processes of local and regional health authorities.
authors.affiliation: International Research Institute for Climate and Society, Lamont Doherty Earth Observatory, Columbia University in the City of New York, 61 Route 9 W, Palisades, PO Box 1000, New York 10964-8000, USA. pfcarlos@iri.columbia.edu.
authors.forename: Daniel
authors.initials: D
authors.lastname: Ruiz
authors.forename: Cyrille
authors.initials: C
authors.lastname: Brun
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Connor
authors.forename: Judith A
authors.initials: JA
authors.lastname: Omumbo
authors.forename: Bradfield
authors.initials: B
authors.lastname: Lyon
authors.forename: Madeleine C
authors.initials: MC
authors.lastname: Thomson
mesh_terms: D002980:Climate; D006801:Humans; D007630:Kenya / Q000453:epidemiology; D016778:Malaria, Falciparum / Q000453:epidemiology*; D015233:Models, Statistical; D011891:Rain; D013696:Temperature