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Journal of Immunology Research
Volume 2015, Article ID 738030, 21 pages
http://dx.doi.org/10.1155/2015/738030
Review Article

Current Mathematical Models for Analyzing Anti-Malarial Antibody Data with an Eye to Malaria Elimination and Eradication

1London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
2Centro de Estatística da Universidade de Lisboa, Faculdade de Ciências, Universidade de Lisboa, Bloco C6, Piso 4, Campo Grande, 1749-016 Lisboa, Portugal
3MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, Medical School Building, Norfolk Place, London W2 1PG, UK
4Division of Population Health and Immunity, Walter and Eliza Hall Institute, 1G Royal Parade, Parkville, VIC 3052, Australia
5Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia

Received 28 August 2015; Accepted 19 October 2015

Academic Editor: Francesco Pappalardo

Copyright © 2015 Nuno Sepúlveda et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The last decade has witnessed a steady reduction of the malaria burden worldwide. With various countries targeting disease elimination in the near future, the popular parasite infection or entomological inoculation rates are becoming less and less informative of the underlying malaria burden due to a reduced number of infected individuals or mosquitoes at the time of sampling. To overcome such problem, alternative measures based on antibodies against specific malaria antigens have gained recent interest in malaria epidemiology due to the possibility of estimating past disease exposure in absence of infected individuals. This paper aims then to review current mathematical models and corresponding statistical approaches used in antibody data analysis. The application of these models is illustrated with three data sets from Equatorial Guinea, Brazilian Amazonia region, and western Kenyan highlands. A brief discussion is also carried out on the future challenges of using these models in the context of malaria elimination.