title: Modelling optimum use of attractive toxic sugar bait stations for effective malaria vector control in Africa.
abstract: BACKGROUND
  The development of insecticide resistance and the increased outdoor-biting behaviour of malaria vectors reduce the efficiency of indoor vector control methods. Attractive toxic sugar baits (ATSBs), a method targeting the sugar-feeding behaviours of vectors both indoors and outdoors, is a promising supplement to indoor tools. The number and configuration of these ATSB stations needed for malaria control in a community needs to be determined.
  
  
  METHODS
  A hypothetical village, typical of those in sub-Saharan Africa, 600 × 600 m, consisting of houses, humans and essential resource requirements of Anopheles gambiae (sugar sources, outdoor resting sites, larval habitats) was simulated in a spatial individual-based model. Resource-rich and resource-poor environments were simulated separately. Eight types of configurations and different densities of ATSB stations were tested. Anopheles gambiae population size, human biting rate (HBR) and entomological inoculation rates (EIR) were compared between different ATSB configurations and densities. Each simulated scenario was run 50 times.
  
  
  RESULTS
  Compared to the outcomes not altered by ATSB treatment in the control scenario, in resource-rich and resource-poor environments, respectively, the optimum ATSB treatment reduced female abundance by 98.22 and 91.80 %, reduced HBR by 99.52 and 98.15 %, and reduced EIR by 99.99 and 100 %. In resource-rich environments, n × n grid design, stations at sugar sources, resting sites, larval habitats, and random locations worked better in reducing vector population and HBRs than other configurations (P < 0.0001). However, there was no significant difference of EIR reductions between all ATSB configurations (P > 0.05). In resource-poor environments, there was no significant difference of female abundances, HBRs and EIRs between all ATSB configurations (P > 0.05). The optimum number of ATSB stations was about 25 for resource-rich environments and nine for resource-poor environments.
  
  
  CONCLUSIONS
  ATSB treatment reduced An. gambiae population substantially and reduced EIR to near zero regardless of environmental resource availability. In resource-rich environments, dispersive configurations worked better in reducing vector population, and stations at or around houses worked better in preventing biting and parasite transmission. In resource-poor environments, all configurations worked similarly. Optimum numbers of bait stations should be adjusted according to seasonality when resource availability changes.
authors.affiliation: Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA. l.zhu3@med.miami.edu.
authors.forename: Lin
authors.initials: L
authors.lastname: Zhu
authors.affiliation: Divisions of Biostatistics and Epidemiology, School of Public Health, University of California, Berkeley, CA, USA. john.marshall@berkeley.edu.
authors.forename: John M
authors.initials: JM
authors.lastname: Marshall
authors.affiliation: Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA. w.qualls@med.miami.edu.
authors.forename: Whitney A
authors.initials: WA
authors.lastname: Qualls
authors.affiliation: Department of Microbiology and Molecular Genetics, Faculty of Medicine, IMRIC, Kuvin Centre for the Study of Infectious and Tropical Diseases, Hebrew University, Jerusalem, Israel. yosefs@ekmd.huji.ac.il.
authors.forename: Yosef
authors.initials: Y
authors.lastname: Schlein
authors.affiliation: Department of Marine Biology and Ecology, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA. jmcmanus@rsmas.miami.edu.
authors.forename: John W
authors.initials: JW
authors.lastname: McManus
authors.affiliation: Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA. KArheart@biostat.med.miami.edu.
authors.forename: Kris L
authors.initials: KL
authors.lastname: Arheart
authors.affiliation: Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA. whlaing@med.miami.edu.
authors.forename: WayWay M
authors.initials: WM
authors.lastname: Hlaing
authors.affiliation: Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Odonto-Stomatology, University of Bamako, BP 1805, Bamako, Mali. cheick@icermali.org.
authors.forename: Sekou F
authors.initials: SF
authors.lastname: Traore
authors.affiliation: Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Odonto-Stomatology, University of Bamako, BP 1805, Bamako, Mali. sdoumbi@icermali.org.
authors.forename: Seydou
authors.initials: S
authors.lastname: Doumbia
authors.affiliation: Department of Microbiology and Molecular Genetics, Faculty of Medicine, IMRIC, Kuvin Centre for the Study of Infectious and Tropical Diseases, Hebrew University, Jerusalem, Israel. guntercmuller@hotmail.com.
authors.forename: Günter C
authors.initials: GC
authors.lastname: Müller
authors.affiliation: Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA. JBeier@med.miami.edu.
authors.forename: John C
authors.initials: JC
authors.lastname: Beier
mesh_terms: D000349:Africa; D000818:Animals; D000852:Anopheles; D002241:Carbohydrates; D005247:Feeding Behavior / Q000187:drug effects; D005260:Female; D006801:Humans; D008136:Longevity / Q000187:drug effects; D008288:Malaria / Q000188:drug therapy / Q000517:prevention & control; D008962:Models, Theoretical; D009032:Mosquito Control / Q000191:economics; D011156:Population Density
chemical_list: D002241:Carbohydrates