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Background  Malaria, being a mosquito-borne infectious disease, is still one of the most devastating global health issues. The malaria vector  Anopheles vagus  is widely distributed in Asia and a dominant vector in Bandarban, Bangladesh. However, despite its wide distribution, no agent based model (ABM) of  An. vagus  has yet been developed. Additionally, its response to combined vector control interventions has not been examined.    Methods  A spatial ABM, denoted as ABM   \documentclass[12pt]{minimal} 				\usepackage{amsmath} 				\usepackage{wasysym}  				\usepackage{amsfonts}  				\usepackage{amssymb}  				\usepackage{amsbsy} 				\usepackage{mathrsfs} 				\usepackage{upgreek} 				\setlength{\oddsidemargin}{-69pt} 				\begin{document}$$_{vagus}$$\end{document}v  a  g  u  s, was designed and implemented based on the biological attributes of  An. vagus  by modifying an established, existing ABM of  Anopheles gambiae . Environmental factors such as temperature and rainfall were incorporated into ABM   \documentclass[12pt]{minimal} 				\usepackage{amsmath} 				\usepackage{wasysym}  				\usepackage{amsfonts}  				\usepackage{amssymb}  				\usepackage{amsbsy} 				\usepackage{mathrsfs} 				\usepackage{upgreek} 				\setlength{\oddsidemargin}{-69pt} 				\begin{document}$$_{vagus}$$\end{document}v  a  g  u  susing daily weather profiles. Real-life field data of Bandarban were used to generate landscapes which were used in the simulations. ABM   \documentclass[12pt]{minimal} 				\usepackage{amsmath} 				\usepackage{wasysym}  				\usepackage{amsfonts}  				\usepackage{amssymb}  				\usepackage{amsbsy} 				\usepackage{mathrsfs} 				\usepackage{upgreek} 				\setlength{\oddsidemargin}{-69pt} 				\begin{document}$$_{vagus}$$\end{document}v  a  g  u  swas verified and validated using several standard techniques and against real-life field data. Using artificial landscapes, the individual and combined efficacies of existing vector control interventions are modeled, applied, and examined.    Results  Simulated female abundance curves generated by ABM   \documentclass[12pt]{minimal} 				\usepackage{amsmath} 				\usepackage{wasysym}  				\usepackage{amsfonts}  				\usepackage{amssymb}  				\usepackage{amsbsy} 				\usepackage{mathrsfs} 				\usepackage{upgreek} 				\setlength{\oddsidemargin}{-69pt} 				\begin{document}$$_{vagus}$$\end{document}v  a  g  u  sclosely follow the patterns observed in the field. Due to the use of daily temperature and rainfall data, ABM   \documentclass[12pt]{minimal} 				\usepackage{amsmath} 				\usepackage{wasysym}  				\usepackage{amsfonts}  				\usepackage{amssymb}  				\usepackage{amsbsy} 				\usepackage{mathrsfs} 				\usepackage{upgreek} 				\setlength{\oddsidemargin}{-69pt} 				\begin{document}$$_{vagus}$$\end{document}v  a  g  u  swas able to generate seasonal patterns for a particular area. When two interventions were applied with parameters set to mid-ranges, ITNs/LLINs with IRS produced better results compared to the other cases. Moreover, any intervention combined with ITNs/LLINs yielded better results. Not surprisingly, three interventions applied in combination generate best results compared to any two interventions applied in combination.    Conclusions  Output of ABM   \documentclass[12pt]{minimal} 				\usepackage{amsmath} 				\usepackage{wasysym}  				\usepackage{amsfonts}  				\usepackage{amssymb}  				\usepackage{amsbsy} 				\usepackage{mathrsfs} 				\usepackage{upgreek} 				\setlength{\oddsidemargin}{-69pt} 				\begin{document}$$_{vagus}$$\end{document}v  a  g  u  sshowed high sensitivity to real-life field data of the environmental factors and the landscape of a particular area. Hence, it is recommended to use the model for a given area in connection to its local field data. For applying combined interventions, three interventions altogether are highly recommended whenever possible. It is also suggested that ITNs/LLINs with IRS can be applied when three interventions are not available.    Electronic supplementary material  The online version of this article (doi:10.1186/s12936-017-2075-6) contains supplementary material, which is available to authorized users.

A spatial agent-based model of Anopheles vagus for malaria epidemiology: examining the impact of vector control interventions