title: Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions.
abstract: BACKGROUND
  Timely and accurate information about the onset of malaria epidemics is essential for effective control activities in epidemic-prone regions. Early warning methods that provide earlier alerts (usually by the use of weather variables) may permit control measures to interrupt transmission earlier in the epidemic, perhaps at the expense of some level of accuracy.
  
  
  METHODS
  Expected case numbers were modeled using a Poisson regression with lagged weather factors in a 4th-degree polynomial distributed lag model. For each week, the numbers of malaria cases were predicted using coefficients obtained using all years except that for which the prediction was being made. The effectiveness of alerts generated by the prediction system was compared against that of alerts based on observed cases. The usefulness of the prediction system was evaluated in cold and hot districts.
  
  
  RESULTS
  The system predicts the overall pattern of cases well, yet underestimates the height of the largest peaks. Relative to alerts triggered by observed cases, the alerts triggered by the predicted number of cases performed slightly worse, within 5% of the detection system. The prediction-based alerts were able to prevent 10-25% more cases at a given sensitivity in cold districts than in hot ones.
  
  
  CONCLUSIONS
  The prediction of malaria cases using lagged weather performed well in identifying periods of increased malaria cases. Weather-derived predictions identified epidemics with reasonable accuracy and better timeliness than early detection systems; therefore, the prediction of malarial epidemics using weather is a plausible alternative to early detection systems.
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston,MA 02115, USA. htekleha@hsph.harvard.edu
authors.forename: Hailay D
authors.initials: HD
authors.lastname: Teklehaimanot
authors.forename: Joel
authors.initials: J
authors.lastname: Schwartz
authors.forename: Awash
authors.initials: A
authors.lastname: Teklehaimanot
authors.forename: Marc
authors.initials: M
authors.lastname: Lipsitch
mesh_terms: D000465:Algorithms; D000818:Animals; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D005002:Ethiopia / Q000453:epidemiology; D005544:Forecasting; D006801:Humans; D015994:Incidence; D016778:Malaria, Falciparum / Q000453:epidemiology* / Q000517:prevention & control; D016012:Poisson Distribution; D011891:Rain; D013696:Temperature; D014887:Weather