Smith, Thomas A. and Hancock, Penelope A.. (2009) Combining Fungal Biopesticides and Insecticide-Treated Bednets to Enhance Malaria Control. PLoS Computational Biology, Vol.5 (No.10). Article No.e1000525. ISSN 1553-7358

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Abstract

In developing strategies to control malaria vectors, there is increased interest in biological methods that do not cause instant vector mortality, but have sublethal and lethal effects at different ages and stages in the mosquito life cycle. These techniques, particularly if integrated with other vector control interventions, may produce substantial reductions in malaria transmission due to the total effect of alterations to multiple life history parameters at relevant points in the life-cycle and transmission-cycle of the vector. To quantify this effect, an analytically tractable gonotrophic cycle model of mosquito-malaria interactions is developed that unites existing continuous and discrete feeding cycle approaches. As a case study, the combined use of fungal biopesticides and insecticide treated bednets (ITNs) is considered. Low values of the equilibrium EIR and human prevalence were obtained when fungal biopesticides and ITNs were combined, even for scenarios where each intervention acting alone had relatively little impact. The effect of the combined interventions on the equilibrium EIR was at least as strong as the multiplicative effect of both interventions. For scenarios representing difficult conditions for malaria control, due to high transmission intensity and widespread insecticide resistance, the effect of the combined interventions on the equilibrium EIR was greater than the multiplicative effect, as a result of synergistic interactions between the interventions. Fungal biopesticide application was found to be most effective when ITN coverage was high, producing significant reductions in equilibrium prevalence for low levels of biopesticide coverage. By incorporating biological mechanisms relevant to vectorial capacity, continuous-time vector population models can increase their applicability to integrated vector management.

Item Type:	Journal Article
Subjects:	Q Science > Q Science (General)
Divisions:	Faculty of Science > Life Sciences (2010- )
Journal or Publication Title:	PLoS Computational Biology
Publisher:	Public Library of Science
ISSN:	1553-7358
Date:	2009
Volume:	Vol.5
Number:	No.10
Page Range:	Article No.e1000525
Identification Number:	10.1371/journal.pcbi.1000525
Status:	Not Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	National Environment Research Council (NERC)
URI:	http://wrap.warwick.ac.uk/id/eprint/42576

Data sourced from Thomson Reuters' Web of Knowledge

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