title: Development and validation of climate and ecosystem-based early malaria epidemic prediction models in East Africa.
abstract: BACKGROUND
  Malaria epidemics remain a serious threat to human populations living in the highlands of East Africa where transmission is unstable and climate sensitive. An existing early malaria epidemic prediction model required further development, validations and automation before its wide use and application in the region. The model has a lead-time of two to four months between the detection of the epidemic signal and the evolution of the epidemic. The validated models would be of great use in the early detection and prevention of malaria epidemics.
  
  
  METHODS
  Confirmed inpatient malaria data were collected from eight sites in Kenya, Tanzania and Uganda for the period 1995-2009. Temperature and rainfall data for the period 1960-2009 were collected from meteorological stations closest to the source of the malaria data. Process-based models were constructed for computing the risk of an epidemic in two general highland ecosystems using temperature and rainfall data. The sensitivity, specificity and positive predictive power were used to validate the models.
  
  
  RESULTS
  Depending on the availability and quality of the malaria and meteorological data, the models indicated good functionality at all sites. Only two sites in Kenya had data that met the criteria for the full validation of the models. The additive model was found most suited for the poorly drained U-shaped valley ecosystems while the multiplicative model was most suited for the well-drained V-shaped valley ecosystem. The +18°C model was adaptable to any of the ecosystems and was designed for conditions where climatology data were not available. The additive model scored 100% for sensitivity, specificity and positive predictive power. The multiplicative model had a sensitivity of 75% specificity of 99% and a positive predictive power of 86%.
  
  
  CONCLUSIONS
  The additive and multiplicative models were validated and were shown to be robust and with high climate-based, early epidemic predictive power. They are designed for use in the common, well- and poorly drained valley ecosystems in the highlands of East Africa.
authors.affiliation: Kenya Medical Research Institute, Centre for Global Health Research, (KEMRI) Climate and Human Health Research Unit, PO Box 1578, Kisumu, Kenya. githeko@yahoo.com.
authors.forename: Andrew K
authors.initials: AK
authors.lastname: Githeko
authors.forename: Laban
authors.initials: L
authors.lastname: Ogallo
authors.forename: Martha
authors.initials: M
authors.lastname: Lemnge
authors.forename: Michael
authors.initials: M
authors.lastname: Okia
authors.forename: Ednah N
authors.initials: EN
authors.lastname: Ototo
mesh_terms: D002980:Climate; D017753:Ecosystem; D058872:Epidemics; D004812:Epidemiologic Methods; D005544:Forecasting / Q000379:methods*; D006801:Humans; D007630:Kenya / Q000453:epidemiology; D008288:Malaria / Q000453:epidemiology*; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D013636:Tanzania / Q000453:epidemiology; D013696:Temperature; D014454:Uganda / Q000453:epidemiology