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Towards malaria control and elimination in Ghana: challenges and decision making tools to guide planning.

  • Awine, Timothy1, 2
  • Malm, Keziah3
  • Bart-Plange, Constance3
  • Silal, Sheetal P1, 4
  • 1 a Modelling and Simulation Hub, Africa, Department of Statistical Sciences , University of Cape Town , Cape Town , South Africa. , (South Africa)
  • 2 b South African Department of Science and Technology/National Research Foundation Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA) , University of Stellenbosch , Stellenbosch , South Africa. , (South Africa)
  • 3 c National Malaria Control Program , Ministry of Health , Accra , Ghana. , (Ghana)
  • 4 d Tropical Disease Modelling, Nuffield Department of Medicine , University of Oxford , Oxford , UK.
Published Article
Global Health Action
Informa UK (Taylor & Francis)
Publication Date
Jan 01, 2017
DOI: 10.1080/16549716.2017.1381471
PMID: 29035160


Ghana is classified as being in the malaria control phase, according to the global malaria elimination program. With many years of policy development and control interventions, malaria specific mortality among children less than 5 years old has declined from 14.4% in 2000 to 0.6% in 2012. However, the same level of success has not been achieved with malaria morbidity. The recently adopted 2015-2020 Ghana strategic action plan aims to reduce the burden of malaria by 75.0%. Planning and policy development has always been guided by evidence from field studies, and mathematical models that are able to investigate malaria transmission dynamics have not played a significant role in supporting policy development. The objectives of this study are to describe the malaria situation in Ghana and give a brief account of how mathematical modelling techniques could support a more informed malaria control effort in the Ghanaian context. A review is carried out of some mathematical models investigating the dynamics of malaria transmission in sub-Saharan African countries, including Ghana. The applications of these models are then discussed, considering the gaps that still remain in Ghana for which further mathematical model development could be supportive. Because of the collaborative approach adopted in their development, some model examples Ghana could benefit from are also discussed. Collaboration between malaria control experts and modellers will allow for more appropriate mathematical models to be developed. Packaging these models with user-friendly interfaces and making them available at various levels of malaria control management could help provide the decision making tools needed for planning and a platform for monitoring and evaluation of interventions in Ghana.

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