Volume 9 Supplement 2

Parasite to Prevention: Advances in the understanding of malaria

Open Access

Modeling the effects of vector control interventions in reducing malaria transmission, morbidity and mortality

  • Nakul Chitnis1, 2,
  • Diggory Hardy1, 2,
  • Guillaume Gnaegi1, 2,
  • Konstantina Boutsika1, 2,
  • Nicolas Maire1, 2,
  • Richard Steketee3,
  • Allan Schapira1, 2 and
  • Tom Smith1, 2
Malaria Journal20109(Suppl 2):O7

https://doi.org/10.1186/1475-2875-9-S2-O7

Published: 20 October 2010

Malaria interventions are usually prioritized using efficacy estimates from intervention trials, without considering the context of existing intervention packages or long-term dynamics. We use numerical simulation of mathematical models of malaria in humans and mosquitoes to provide robust quantitative predictions of effectiveness of different strategies in reducing transmission, morbidity and mortality.

We can simulate indoor residual spraying (IRS) and insecticide-treated nets (ITNs), used singly and in combination with each other and with other interventions such as improved case management, intermittent preventive treatment (IPT). We can estimate reductions in entomological inoculation rate (EIR), clinical cases, prevalence and malaria deaths from simulations of different coverage levels ITNs and IRS with different properties, and at different transmission and health system settings.

Our results suggest that sustained coverage of one or two interventions reduces malaria prevalence in two to three years but does not lead to further gains (Figure 1). However, in some settings, even with sustained coverage, clinical incidence of malaria increases as the population loses its naturally acquired immunity. In some low to medium transmission settings, our simulations suggest that high coverage of both interventions can lead to interruption of transmission.
Figure 1

Model predictions of the effect of IRS with DDT on malaria prevalence. We assume two annual IRS DDT spray rounds (each with 95% coverage) for 12 years; simulations are of 1000 humans exposed to seasonal transmission based on a Tanzanian setting, with an initial EIR of 320 infectious bites per person per annum. The blue line is the median prevalence from four runs of each of 15 different model paramerizations for malaria in humans; grey area: interquartile range; dashed lines: maxima and minima.

Authors’ Affiliations

(1)
Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute
(2)
University of Basel
(3)
MACEPA-PATH

References

  1. Smith T, Killeen DF, Maire N, Ross A, Molineaux L, Tediosi F, Hutton G, Utzinger J, Dietz K, Tanner M: Mathematical modeling of the impact of malaria vaccines on the clinical epidemiology and natural history of Plasmodium falciparum malaria: overview. Am J Trop Med Hyg. 2006, 75 (2 Suppl): 1-10.PubMedGoogle Scholar
  2. Chitnis N, Smith T, Steketee RW: A mathematical model for the dynamics of malaria in mosquitoes feeding on a heterogeneous host population. J Biol Dyn. 2008, 2: 259-285. 10.1080/17513750701769857.View ArticlePubMedGoogle Scholar
  3. Chitnis N, Schapira A, Smith T, Steketee R: Comparing the effectiveness of malaria vector control interventions through a mathematical model. Am J Trop Med Hyg. 2010, 83: 230-40. 10.4269/ajtmh.2010.09-0179.PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Boutsika et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Advertisement