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The role of mathematical modelling in malaria elimination and eradication (Comment on: Can malaria be eliminated?)

Authors
Journal
Transactions of the Royal Society of Tropical Medicine and Hygiene
0035-9203
Publisher
Oxford University Press
Publication Date
Volume
103
Issue
6
Identifiers
DOI: 10.1016/j.trstmh.2009.01.027
Keywords
  • Correspondence
Disciplines
  • Biology
  • Mathematics
  • Medicine
  • Pharmacology

Abstract

For many countries, as Professor Greenwood points out, the answer to his question is a resounding ‘yes’.1 Perhaps the more pertinent question is, with so many more interventions now available, ‘how?’. The two most important new tools that have been added to our malaria control arsenal since the 1950s are artemisinin combination therapies and insecticide-treated bed nets. These are most likely to be effective when used together, in combination with indoor residual spraying where there are suitable vectors. The problem we are faced with is a lack of experience of using such an elimination strategy on a large scale, leading to a lack of data, particularly on the best way to roll out ACTs. In the past, mass screening and treatment, mass drug administration and large-scale replacement of first-line therapies have all been attempted, with varying degrees of success. In the context of newly arisen artemisinin resistance and the diminishing effectiveness of the pyrethroid insecticides, particular care must be taken to preserve the effectiveness of these compounds for as long as possible, whilst achieving maximum impact in a wide variety of epidemiological settings. Another powerful tool that we did not have in the 1950s is sophisticated computer-based mathematical modelling. This facilitates the use of the limited data currently available to make predictions about future events. In other words, it provides a rational framework on which to base decisions made using limited but diverse and complex inter-related information, such as population demographics, pharmacokinetics and pharmacodynamics, treatment-seeking behaviour, spatially distributed risk factors, the presence of antimalarial resistance, etc. Identifying, preparing and calibrating such data sets for each country for the application of mathematical models is a huge challenge in itself.2 The accuracy of modelling predictions improves in an iterative process as more data become available. Hence, the more intense the malaria elimination efforts are and as l

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