title: Optimal lead time for dengue forecast.
abstract: BACKGROUND
  A dengue early warning system aims to prevent a dengue outbreak by providing an accurate prediction of a rise in dengue cases and sufficient time to allow timely decisions and preventive measures to be taken by local authorities. This study seeks to identify the optimal lead time for warning of dengue cases in Singapore given the duration required by a local authority to curb an outbreak.
  
  
  RESULTS
  We developed a Poisson regression model to analyze relative risks of dengue cases as functions of weekly mean temperature and cumulative rainfall with lag times of 1-5 months using spline functions. We examined the duration of vector control and cluster management in dengue clusters > = 10 cases from 2000 to 2010 and used the information as an indicative window of the time required to mitigate an outbreak. Finally, we assessed the gap between forecast and successful control to determine the optimal timing for issuing an early warning in the study area. Our findings show that increasing weekly mean temperature and cumulative rainfall precede risks of increasing dengue cases by 4-20 and 8-20 weeks, respectively. These lag times provided a forecast window of 1-5 months based on the observed weather data. Based on previous vector control operations, the time needed to curb dengue outbreaks ranged from 1-3 months with a median duration of 2 months. Thus, a dengue early warning forecast given 3 months ahead of the onset of a probable epidemic would give local authorities sufficient time to mitigate an outbreak.
  
  
  CONCLUSIONS
  Optimal timing of a dengue forecast increases the functional value of an early warning system and enhances cost-effectiveness of vector control operations in response to forecasted risks. We emphasize the importance of considering the forecast-mitigation gaps in respective study areas when developing a dengue forecasting model.
authors.affiliation: Umeå Centre for Global Health Research, Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden. yienling.hii@epiph.umu.se
authors.forename: Yien Ling
authors.initials: YL
authors.lastname: Hii
authors.forename: Joacim
authors.initials: J
authors.lastname: Rocklöv
authors.forename: Stig
authors.initials: S
authors.lastname: Wall
authors.forename: Lee Ching
authors.initials: LC
authors.lastname: Ng
authors.forename: Choon Siang
authors.initials: CS
authors.lastname: Tang
authors.forename: Nawi
authors.initials: N
authors.lastname: Ng
mesh_terms: D003715:Dengue / Q000453:epidemiology* / Q000517:prevention & control*; D004196:Disease Outbreaks / Q000517:prevention & control*; D005544:Forecasting / Q000379:methods; D006801:Humans; D007300:Insect Control / Q000379:methods; D015233:Models, Statistical; D012846:Singapore / Q000453:epidemiology; D013997:Time Factors; D014887:Weather