Vectorial capacity and vector control: reconsidering sensitivity to parameters for malaria elimination

• Main Authors: Brady, Oliver J. (Author), Godfray, H. Charles J. (Author), Tatem, Andrew J. (Author), Gething, Peter W. (Author), Cohen, Justin M. (Author), McKenzie, F. Ellis (Author), Perkins, T. Alex (Author), Reiner, Robert C. (Author), Tusting, Lucy S. (Author), Sinka, Marianne E. (Author), Moyes, Catherine L. (Author), Eckhoff, Philip A. (Author), Scott, Thomas W. (Author), Lindsay, Steven W. (Author), Hay, Simon I. (Author), Smith, David L. (Author)
• Format: Article
• Language: English
• Published: 2016-02.
• Subjects: Article
• Online Access: Get fulltext

 Background: Major gains have been made in reducing malaria transmission in many parts of the world, principally by scaling-up coverage with long-lasting insecticidal nets and indoor residual spraying. Historically, choice of vector control intervention has been largely guided by a parameter sensitivity analysis of George Macdonald's theory of vectorial capacity that suggested prioritizing methods that kill adult mosquitoes. While this advice has been highly successful for transmission suppression, there is a need to revisit these arguments as policymakers in certain areas consider which combinations of interventions are required to eliminate malaria. Methods and Results: Using analytical solutions to updated equations for vectorial capacity we build on previous work to show that, while adult killing methods can be highly effective under many circumstances, other vector control methods are frequently required to fill effective coverage gaps. These can arise due to pre-existing or developing mosquito physiological and behavioral refractoriness but also due to additive changes in the relative importance of different vector species for transmission. Furthermore, the optimal combination of interventions will depend on the operational constraints and costs associated with reaching high coverage levels with each intervention. Conclusions: Reaching specific policy goals, such as elimination, in defined contexts requires increasingly non-generic advice from modelling. Our results emphasize the importance of measuring baseline epidemiology, intervention coverage, vector ecology and program operational constraints in predicting expected outcomes with different combinations of interventions.