Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications
Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications
After many decades of vaccination, measles epidemiology varies greatly between and within countries. National immunization programs are therefore encouraged to conduct regular situation analyses and to leverage models to adapt interventions to local needs. Here, we review applications of models to develop locally tailored interventions to support control and elimination efforts. In general, statistical and semi-mechanistic transmission models can be used to synthesize information from vaccination coverage, measles incidence, demographic, and/or serological data, offering a means to estimate the spatial and age-specific distribution of measles susceptibility. These estimates complete the picture provided by vaccination coverage alone, by accounting for natural immunity. Dynamic transmission models can then be used to evaluate the relative impact of candidate interventions for measles control and elimination and the expected future epidemiology. In most countries, models predict substantial numbers of susceptible individuals outside the age range of routine vaccination, which affects outbreak risk and necessitates additional intervention to achieve elimination. More effective use of models to inform both vaccination program planning and evaluation requires the development of training to enhance broader understanding of models and where feasible, building capacity for modelling in-country, pipelines for rapid evaluation of model predictions using surveillance data, and clear protocols for incorporating model results into decision-making.
Elimination, Epidemiology, Mathematical models, Measles, Measles vaccination, Rubella
979-992
Cutts, F.T.
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Dansereau, E.
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Ferrari, M.J.
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Hanson, M.
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McCarthy, K.A.
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Metcalf, C.J.E.
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Takahashi, S.
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Tatem, A.J.
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Thakkar, N.
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Truelove, S.
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Utazi, E.
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Winter, A.K.
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29 January 2020
Cutts, F.T.
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Dansereau, E.
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Ferrari, M.J.
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Hanson, M.
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McCarthy, K.A.
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Metcalf, C.J.E.
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Takahashi, S.
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Tatem, A.J.
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Thakkar, N.
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Truelove, S.
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Utazi, E.
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Winter, A.K.
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Cutts, F.T., Dansereau, E., Ferrari, M.J., Hanson, M., McCarthy, K.A., Metcalf, C.J.E., Takahashi, S., Tatem, A.J., Thakkar, N., Truelove, S., Utazi, E. and Winter, A.K.
(2020)
Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications.
Vaccine, 38 (5), .
(doi:10.1016/j.vaccine.2019.11.020).
Abstract
After many decades of vaccination, measles epidemiology varies greatly between and within countries. National immunization programs are therefore encouraged to conduct regular situation analyses and to leverage models to adapt interventions to local needs. Here, we review applications of models to develop locally tailored interventions to support control and elimination efforts. In general, statistical and semi-mechanistic transmission models can be used to synthesize information from vaccination coverage, measles incidence, demographic, and/or serological data, offering a means to estimate the spatial and age-specific distribution of measles susceptibility. These estimates complete the picture provided by vaccination coverage alone, by accounting for natural immunity. Dynamic transmission models can then be used to evaluate the relative impact of candidate interventions for measles control and elimination and the expected future epidemiology. In most countries, models predict substantial numbers of susceptible individuals outside the age range of routine vaccination, which affects outbreak risk and necessitates additional intervention to achieve elimination. More effective use of models to inform both vaccination program planning and evaluation requires the development of training to enhance broader understanding of models and where feasible, building capacity for modelling in-country, pipelines for rapid evaluation of model predictions using surveillance data, and clear protocols for incorporating model results into decision-making.
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Submitted date: 10 August 2019
Accepted/In Press date: 8 November 2019
e-pub ahead of print date: 29 November 2019
Published date: 29 January 2020
Additional Information:
Funding Information:
This work was funded in part by the Bill and Melinda Gates Foundation (BMGF). FC received consultancy fees from BMGF. AKW, CJM, MJF are supported by BMGF ( OPP1094816 ). KM and NT are supported by Bill & Melinda Gates through the Global Good Fund, Bellevue, WA, USA. AW is supported by the National Library Of Medicine of the National Institutes of Health under Award Number DP2LM013102 . AW is also support by a Career Award at the Scientific Interface by the Burroughs Wellcome Fund . A.J.T. is supported by funding from NIH/NIAID ( U19AI089674 ), the Bill & Melinda Gates Foundation ( OPP1106427 , 1032350 , OPP1134076 ), the Clinton Health Access Initiative , National Institutes of Health and a Wellcome Trust Sustaining Health Grant ( 106866/Z/15/Z ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funding agencies. The funders did not play any role in the collection, analysis, interpretation, writing of final reports, or decision to submit this research.
Publisher Copyright:
© 2019 The Authors
Keywords:
Elimination, Epidemiology, Mathematical models, Measles, Measles vaccination, Rubella
Identifiers
Local EPrints ID: 436376
URI: http://eprints.soton.ac.uk/id/eprint/436376
ISSN: 0264-410X
PURE UUID: 253ba5fc-98b8-4804-9840-c6f30257d9e2
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Date deposited: 09 Dec 2019 17:30
Last modified: 17 Mar 2024 03:29
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Contributors
Author:
F.T. Cutts
Author:
E. Dansereau
Author:
M.J. Ferrari
Author:
M. Hanson
Author:
K.A. McCarthy
Author:
C.J.E. Metcalf
Author:
S. Takahashi
Author:
N. Thakkar
Author:
S. Truelove
Author:
A.K. Winter
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