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A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India.

Emily S NightingaleLloyd A C ChapmanSridhar SrikantiahSwaminathan SubramanianPurushothaman JambulingamJohannes BracherMary M CameronGraham F Medley
Published in: PLoS neglected tropical diseases (2020)
The model developed is informed by routinely-collected surveillance data as it accumulates, and predictions are sufficiently accurate and precise to be useful. Such forecasts could, for example, be used to guide stock requirements for rapid diagnostic tests and drugs. More comprehensive data on factors thought to influence geographic variation in VL burden could be incorporated, and might better explain the heterogeneity between blocks and improve uniformity of predictive performance. Integration of the approach in the management of the VL programme would be an important step to ensuring continued successful control.
Keyphrases
  • electronic health record
  • big data
  • public health
  • high resolution
  • risk factors
  • data analysis
  • clinical trial
  • artificial intelligence
  • mass spectrometry