Modelling malaria pathogenesis

Nicole Mideo, Troy Day, Andrew Fraser Read

Research output: Contribution to journalShort survey

20 Citations (Scopus)

Abstract

Almost 20years after the development of models of malaria pathogenesis began, we are beyond the 'proof-of-concept' phase and these models are no longer abstract mathematical exercises. They have refined our knowledge of within-host processes, and have brought insights that could not easily have been obtained from experimentation alone. There is much potential that remains to be realized, however, both in terms of informing the design of interventions and health policy, and in terms of addressing lingering questions about the basic biology of malaria. Recent research has begun to iterate theory and data in a much more comprehensive way, and the use of statistical techniques for model fitting and comparison offers a promising approach for providing a quantitative understanding of the pathogenesis of such a complex disease.

Original languageEnglish (US)
Pages (from-to)1947-1955
Number of pages9
JournalCellular Microbiology
Volume10
Issue number10
DOIs
StatePublished - Sep 16 2008

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Malaria
Statistical Models
Health Policy
Research
Health

All Science Journal Classification (ASJC) codes

  • Microbiology
  • Immunology
  • Virology

Cite this

Mideo, Nicole ; Day, Troy ; Read, Andrew Fraser. / Modelling malaria pathogenesis. In: Cellular Microbiology. 2008 ; Vol. 10, No. 10. pp. 1947-1955.
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Modelling malaria pathogenesis. / Mideo, Nicole; Day, Troy; Read, Andrew Fraser.

In: Cellular Microbiology, Vol. 10, No. 10, 16.09.2008, p. 1947-1955.

Research output: Contribution to journalShort survey

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AB - Almost 20years after the development of models of malaria pathogenesis began, we are beyond the 'proof-of-concept' phase and these models are no longer abstract mathematical exercises. They have refined our knowledge of within-host processes, and have brought insights that could not easily have been obtained from experimentation alone. There is much potential that remains to be realized, however, both in terms of informing the design of interventions and health policy, and in terms of addressing lingering questions about the basic biology of malaria. Recent research has begun to iterate theory and data in a much more comprehensive way, and the use of statistical techniques for model fitting and comparison offers a promising approach for providing a quantitative understanding of the pathogenesis of such a complex disease.

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