4.6 Article

malERA: An updated research agenda for combination interventions and modelling in malaria elimination and eradication

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PLOS MEDICINE
卷 14, 期 11, 页码 -

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PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pmed.1002453

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资金

  1. Bill & Melinda Gates Foundation [OPP1034591]
  2. MRC [MR/L012189/1] Funding Source: UKRI
  3. Bill and Melinda Gates Foundation [OPP1034591] Funding Source: Bill and Melinda Gates Foundation

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This paper summarises key advances and priorities since the 2011 presentation of the Malaria Eradication Research Agenda (malERA), with a focus on the combinations of intervention tools and strategies for elimination and their evaluation using modelling approaches. With an increasing number of countries embarking on malaria elimination programmes, national and local decisions to select combinations of tools and deployment strategies directed at malaria elimination must address rapidly changing transmission patterns across diverse geographic areas. However, not all of these approaches can be systematically evaluated in the field. Thus, there is potential for modelling to investigate appropriate 'packages' of combined interventions that include various forms of vector control, case management, surveillance, and population-based approaches for different settings, particularly at lower transmission levels. Modelling can help prioritise which intervention packages should be tested in field studies, suggest which intervention package should be used at a particular level or stratum of transmission intensity, estimate the risk of resurgence when scaling down specific interventions after local transmission is interrupted, and evaluate the risk and impact of parasite drug resistance and vector insecticide resistance. However, modelling intervention package deployment against a heterogeneous transmission background is a challenge. Further validation of malaria models should be pursued through an iterative process, whereby field data collected with the deployment of intervention packages is used to refine models and make them progressively more relevant for assessing and predicting elimination outcomes.

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