Modeling the impact of biolarvicides on malaria transmission

• Published in: Journal of theoretical biology 2018-10, Vol.454, p.396-409
• Main Authors: Pandey, Surabhi, Nanda, Seema, Vutha, Amit, Naresh, Ram
• Format: Article
• Language: English
• Subjects: Animals; Anopheles - drug effects; Anopheles - growth & development; Biolarvicide; Dynamical systems; Ecological disease management; Female; Humans; Incidence; India - epidemiology; Insect Vectors - drug effects; Insect Vectors - growth & development; Insecticides - pharmacology; Larva - drug effects; Malaria; Malaria - epidemiology; Malaria - prevention & control; Malaria - transmission; Malaria control; Models, Statistical; Models, Theoretical; Mosquito Control - methods; Mosquito Vectors - drug effects; Mosquito Vectors - growth & development; Pest Control, Biological - methods; SIS Model
• ISSN: 0022-5193; 1095-8541
• DOI: 10.1016/j.jtbi.2018.06.001

•We study use of biolarvicides for prevention of malaria using a mathematical model.•Model is calibrated to biolarvicide and malaria incidence data from endemic regions.•The R0 formula captures transitions in the analysis and is corroborated by data.•Equilibria and bifurcations reveal qualitative features of population dynamics.•We determine model sensitivity to parameters and their significance. Biolarvicides are in use in several parts of the world for malaria vector control. We propose a five compartment dynamical systems model to study malaria transmission when biolarvicides are administered, to study the impact of this environmentally safe method on malaria spread. A comprehensive analysis of the model is presented. Model analysis shows that the basic reproductive rate R is larger in the absence of biolarvicides as compared to their presence. Theoretical analysis is corroborated by data from field studies. We show that there exist intermediate parameter regimes that separate disease-free and endemic states, which can in turn be modulated by biolarvicide use. Using Latin hypercube sampling we study the sensitivity of the model to parameter value changes. Calibration of our model to mosquito population and biolarvicide data for indoor and outdoors scenarios, yield parameter values hitherto not available or measurable. We validate our model with malaria incidence data from a region in India and provide predictions for malaria incidence in the presence and absence of biolarvicide. This model provides a prognostic tool to field work involving biolarvicide use in control of malaria.