Comparative assessment of diverse strategies for malaria vector population control based on measured rates at which mosquitoes utilize targeted resource subsets
© Killeen et al.; licensee BioMed Central Ltd. 2014
- Received: 31 January 2014
- Accepted: 3 August 2014
- Published: 28 August 2014
Eliminating malaria requires vector control interventions that dramatically reduce adult mosquito population densities and survival rates. Indoor applications of insecticidal nets and sprays are effective against an important minority of mosquito species that rely heavily upon human blood and habitations for survival. However, complementary approaches are needed to tackle a broader diversity of less human-specialized vectors by killing them at other resource targets.
Impacts of strategies that target insecticides to humans or animals can be rationalized in terms of biological coverage of blood resources, quantified as proportional coverage of all blood resources mosquito vectors utilize. Here, this concept is adapted to enable impact prediction for diverse vector control strategies based on measurements of utilization rate s for any definable, targetable resource subset, even if that overall resource is not quantifiable.
The usefulness of this approach is illustrated by deriving utilization rate estimates for various blood, resting site, and sugar resource subsets from existing entomological survey data. Reported impacts of insecticidal nets upon human-feeding vectors, and insecticide-treated livestock upon animal-feeding vectors, are approximately consistent with model predictions based on measured utilization rates for those human and animal blood resource subsets. Utilization rates for artificial sugar baits compare well with blood resources, and are consistent with observed impact when insecticide is added. While existing data was used to indirectly measure utilization rates for a variety of resting site subsets, by comparison with measured rates of blood resource utilization in the same settings, current techniques for capturing resting mosquitoes underestimate this quantity, and reliance upon complex models with numerous input parameters may limit the applicability of this approach.
While blood and sugar consumption can be readily quantified using existing methods for detecting natural markers or artificial tracers, improved techniques for labelling mosquitoes, or other arthropod pathogen vectors, will be required to assess vector control measures which target them when they utilize non-nutritional resources such as resting, oviposition, and mating sites.
- Vector control
- Target product profile
While antiparasitic drugs and vaccines will be essential to the final stages of malaria elimination, their effectiveness as transmission control interventions will rely heavily upon unprecedented levels of vector control in highly endemic settings[1–4]. It will not be possible to eliminate malaria transmission from most of the tropics without developing scalable vector control intervention options which complement long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) by targeting adult mosquitoes when they use resources other than human blood indoors, and indoor resting sites[5–9]. Mosquitoes usually also exploit non-human blood and human blood sources outdoors, as well as sugar, outdoor resting sites, oviposition sites, and mating sites, so all of these other biological and environmental resources represent alternative targets for vector control interventions.
Faced with such an array of resource target options, the challenge is to define exactly which of these intervention targets are optimal in each of the diverse vectorial systems that exist, and to classify these settings into limited numbers of distinct categories where specific intervention combinations maximize impact. Recent analyses indicate that the impact of vector control measures targeting the blood hosts upon which mosquitoes depend can be rationalized in terms of measurements of the biological coverage of all available blood sources that is achieved, rather than merely high demographic coverage of a targeted subset, such as humans while asleep indoors[9, 11]. Blood resources are perhaps the best understood, and most readily quantified, of all the resources used by mosquitoes that could potentially be targeted with vector control measures. However, many established or emerging vector control strategies only target a specific subset of the resource that can be readily identified and treated in the field. Examples include targeting insecticides to indoor resting sites only with IRS, and artificially introduced resting sites[13–17] or sugar baits[18–20]. Furthermore, the other available forms of these resources that cannot be targeted with insecticides but compete with these subsets for the attentions of mosquitoes, such as naturally occurring sugar sources or outdoor resting sites[22, 23], are often impossible to identify or quantify. It is therefore not possible to estimate biological coverage as a fraction of all available forms of that resource. Here, the concept of biological coverage is extended beyond blood resources, and adapted to enable impact prediction for more diverse vector control strategies, based on direct measurements of coverage and utilization rates for definable, targetable subsets of less readily quantified resources that are equally important to mosquito survival, and therefore equally valid as potential targets for vector control interventions.
Parameter symbols and definitions
Previous formulations to predict impact based on of biological coverage of all blood resources
Availability of all protected (p) human (h) blood hosts for attack, expressed as the rate at which they are collectively encountered and attacked while protected by an LLIN or other prevention measure per host-seeking mosquito per night[11, 24–30]
Proportional coverage of all available blood resources that mosquito population utilizes (A) with a protective intervention (p)
Reformulation to predict impact based on coverage and utilization rates of resource subsets
Utilization rate for an entire given resource (R, which may be specified as blood (v), resting sites (r), sugar (s) or any other resource mosquitoes use), defined as the rate at which individual mosquitoes attempt to utilize all forms of that resource per gonotrophic cycle
Utilization rate for a defined subset of a given resource (R, which may be specified as blood (v), resting sites (r), sugar (s) or any other resource mosquitoes use) that can be identified and targeted with an intervention (x) in the field (R x ), defined as the rate at which individual mosquitoes attempt to utilize the subset per gonotrophic cycle
Utilization rate for a defined subset (x) of a given resource (R, which may be specified as blood (v), resting sites (r), sugar (s) or any other resource mosquitoes use) that can be identified and targeted with an intervention (x) in the field during the subset of times (y) when it can be effectively protected by a given intervention (R x,y ), defined as the rate at which individual mosquitoes attempt to utilize that subset at times when it can be protected per gonotrophic cycle
Utilization rate for a defined subset of a given resource (R, which may be specified as blood (v), resting sites (r), sugar (s) or any other resource mosquitoes use) that has been identified, targeted (x) and covered (c) with an intervention during the subset of times (y) when it can be effectively protected by a given intervention (R x,y,c ), defined as the rate at which individual mosquitoes attempt to utilize that covered subset at times and places at which it can be protected per gonotrophic cycle
Utilization rates for a defined subset of a given resource (R, which may be specified as blood (v), resting sites (r), sugar (s) or any other resource mosquitoes use) that has been identified, can be targeted with an intervention (x) and has been surveyed entomologically (z) in the field (R x,z ), defined as the rate at which individual mosquitoes attempt to utilize that sample of that subset per gonotrophic cycle
Utilization rate for all available blood resources (v), defined as the rate at which individual mosquitoes attempt to utilize any source of blood per gonotrophic cycle
Utilization rate for a defined subset (x) of all blood resources (v) that has been identified and surveyed entomologically (z) in the field (v x,z ), defined as the rate at which individual mosquitoes attempt to utilize that sample of that blood source subset per gonotrophic cycle
Mean lifetime total number of bloodmeals acquired per emerging mosquito
Mean mosquito biting rates experienced by individual livestock (l), defined as the number of bites per head per night
Coverage of all available forms of a given resource (R) with a vector control intervention
Coverage of all available forms of an identifiable, targetable subset (x) of a given resource (R) with a vector control intervention
Gonotrophic age, expressed as the number of gonotrophic cycles completed
Relative availability of an individual mosquito traps (t) for attack by host-seeking mosquitoes attempting to utilize it as a source of blood, compared to a single unprotected human
Absolute size of the mosquito population in a given setting, defined in terms of the number of individuals present
Rate at which the mosquito population utilizes a defined, entomologically surveyed sample subset (z) of any identifiable and targetable subset (x) of a given resource (R x,z ), expressed as the number of utilization attempt events per night
Rate at which the mosquito population utilizes a defined, entomologically surveyed sample (z) of human (h) blood resources (v h,z ), expressed as the number of utilization attempt events per night
Rate at which the mosquito population utilizes a defined, entomologically surveyed sample (z) of livestock (l) blood resources (v l,z ), expressed as the number of utilization attempt events per night.
Mortality probability associated with exposure to an intervention-covered (c) form of a given resource (R) through a single utilization attempt event
Number of persons directly sampled by an entomological survey (z) of mosquitoes attacking human (h) hosts
Number of persons residing in all houses sampled by an entomological survey (Ω) of mosquitoes attacking human (h) hosts
Number of mosquito traps (t) present in a defined setting
Probability of a mosquito surviving all attempts to utilize intervention-covered forms of the targeted resource per gonotrophic cycle
Probability of a mosquito surviving all utilization attempt events for all resources per gonotrophic cycle in the absence of any intervention
Proportion of all available bloodmeals (v) that originate from a specific livestock (l) host species subset
The total availability of all forms of a given resource, which may be specified as blood (v), resting sites (r), sugar (s) or any other resource mosquitoes use, defined as the per night rate at which individual mosquitoes encounter and attempt to utilize that resource
The total availability of a subset (x) of a given resource (R which may be specified as blood (v), resting sites (r), sugar (s) or any other resource mosquitoes use) that can be identified and targeted with an intervention, defined as the per night rate at which individual mosquitoes encounter and attempt to utilize that subset
The total availability of a subset (x) of given resource (R which may be specified as blood (v), resting sites (r), sugar (s) or any other resource mosquitoes use) that can be identified and targeted with an intervention during the subset of times (y) when it can be effectively protected by that intervention, defined as the per night rate at which individual mosquitoes encounter and attempt to utilize that subset at times when it can be effectively covered with that intervention
The total availability of all intervention-covered (c) forms of a targetable subset (x) of given resource (R which may be specified as blood (v), resting sites (r), sugar (s) or any other resource mosquitoes use) during the subset of times (y) when it can be effectively protected by that intervention, defined as the per night rate at which individual mosquitoes encounter and attempt to utilize the covered forms of that subset at times when it is effectively covered with that intervention.
The total availability of an entomologically surveyed sample (z) of a targetable subset (x) of given resource (R which may be specified as blood (v), resting sites (r), sugar (s) or any other resource mosquitoes use), defined as the per night rate at which individual mosquitoes encounter and attempt to utilize it.
The total availability of all forms of resting sites, defined as the rate at which individual mosquitoes encounter and attempt to utilize resting sites per night
The total availability of all forms of sugar, defined as the rate at which individual mosquitoes encounter and attempt to utilize sugar per night
Mean number of nights individual mosquitoes spend resting and gestating indoors following a bloodmeal inside a house
The total availability of all forms of blood, defined as the rate at which individual mosquitoes encounter and attempt to utilize blood per night
A subset of a given resource that may be identified and targeted with a vector control intervention
A subset of a given resource that may be effectively covered with a vector control intervention at times and places when mosquitoes encounter and attempt to utilize it
A sample of a given resource that has been surveyed entomologically
Humans in a sampled set of households
Defining biological coverage based on the example of blood resources
where the total attack availabilities of the all hosts (A), and covered hosts at times and places when they are actually protected (A p ), are defined kinetically[25, 42] as the rates per night at which an individual host-seeking mosquito respectively encounters and attacks either all hosts or all hosts that are protected at the time of the encounter and attack events.
where C v is the proportion of all mosquito attacks upon real (live vertebrate hosts) or perceived (artificial odor-baited traps, sometimes referred to as pseudo-hosts) blood resources to which effective coverage with a vector control intervention applies at that time and place, v is the total rate at which individual mosquitoes encounter and attack all hosts and pseudo hosts, and v c is the total rate at which individual mosquitoes encounter and attack all hosts and pseudo hosts at times and places when they are effectively covered with a vector control intervention.
Adapting the concept of biological coverage to rationalize vector control impact based on utilization rates of diverse resource targets
Predicting intervention impact based on resource subset coverage and utilization rates
It is therefore not necessary to know the proportion of that total resource which the targeted subset represents, or the coverage (C R ) or utilization rate (α R ) for all available forms of a resource. Impact can be predicted directly so long as the coverage of the targeted subset itself, and utilization rates for that subset under conditions that enable the intervention to protect it against safe utilization by the mosquito, can be measured.
This approach to predicting the survival probability assumes that utilization attempt events are randomly, and independently, distributed across all resource units and mosquitoes. Specifically, the number of times one mosquito utilizes a resource (or resource subset) in one gonotrophic cycle is assumed to be a non-negative integer valued random variable (0, 1, 2, 3…) since the mosquito may not necessarily use the resource or, alternatively, may access it multiple times. Hence, the utilization rate of these resources should be understood as an expected value depending on random events that may be expressed as a mean. This is clearly not the case in relation to obligate utilization of blood from one of all available blood resources (R = v). Each mosquito must utilize one of these available resources to complete the gonotrophic cycle, so complete coverage of all blood resources (C R = 1) that are utilized at a mean rate of once per gonotrophic cycle (α R = 1) with an insecticide which induces comprehensive fatality would deterministically result in reduction of survival probability to zero, rather than merely reduced to the minor proportion of mosquitoes that are inaccurately assumed by Equation 16 to have completed a gonotrophic cycle without taking any bloodmeal. However, for a covered subset of a resource (Equation 17), rather than for all available forms of that resource (Equation 16), it is realistic to assume that the number of utilization attempt events per gonotrophic cycle is a random variable for individual mosquitoes and utilization rates per gonotrophic cycle are expected values (expressed as a mean), even for obligate blood resource utilization behaviours.
Measuring utilization rates for subsets of undefined resources by comparison with those for quantifiable blood resources
Note that the emergence and mean longevity terms cancel each other out so that estimates of these parameters are not required to estimate the relative rate of utilization of a resource compared with blood, as described below.
where R x,z /R x is the proportion of all available forms of the targeted non-blood resource subset that was surveyed entomologically to measure the rate per night at which the entire mosquito population attempts to utilize it, where N h,z /N h and N l,z /N l are the proportions of all humans or livestock that were respectively surveyed to measure the rate at which mosquitoes attempted to utilize their blood, and where and are the proportions of bloodmeals the vector population obtains from all available humans and livestock, respectively.
Literature review and utilization rate estimate extraction
Studies, or sets of studies, were identified which presented sufficient parameter estimate data for utilization rates of specific, intervention-targetable resource subsets to be calculated for specific malaria vector species in specific, distinct locations. In addition to the authors’ archives of literature and unpublished data, the Pubmed database was also queried with the search term ‘Anopheles AND ((pyrethrum spray OR aspirator) OR (insecticide AND (cattle OR livestock)) OR odour-baited OR sugar)’. For utilization of livestock blood and sugar, consideration was limited to studies in settings where trials of insecticide-treated livestock or sugar baits, respectively, have been either implemented or specifically suggested. To avoid cluttering of the second figure presented in the Results section, consideration of studies enabling estimation of indoor resting site utilization was restricted to recent unpublished studies of our own and those published in the last decade. Where results for a species complex or group were reported, these are attributed to the most common sibling species identified in that population.
Dependence of impact upon utilization rates of intervention-targeted resource subsets
Field estimates of utilization rates for defined, targetable resource subsets
Apart from blood, the other important nutrition source that facilitates mosquito survival and malaria transmission is plant-derived sugar[21, 65, 66]. Estimated minimum utilization rates for dye-labelled sugar baits by Anopheles claviger, Anopheles sergenti, and An. gambiae, in three distinct settings appear to be at least comparable with those for utilization of human blood indoors by very anthropophagic vectors, and for utilization of animal blood by zoophagic vectors (Figure 2). The impressive impacts upon all three of these vector populations that have been achieved by adding insecticides to such sugar baits[18–20] are therefore consistent with the predictions outlined in Figure 1, as well as other recent modelling analyses. Given the widespread dependence of mosquitoes upon sugar[21, 65, 66], many important vector populations probably use it at similarly high rates, especially when infected with malaria parasites. Mosquitoes should therefore be at least as amenable to control with this approach as anthropophagic vectors are to LLINs, and as zoophagic vectors are to insecticide-treated livestock.
Utilization rate estimates for indoor resting sites (Figure 2) are generally lower than that required to explain (Figure 1) the often massive impact of IRS and insecticidal wall linings on many target vector species. However, this is not entirely surprising because even the best techniques for capturing mosquitoes resting indoors, such as pyrethrum spray catch and backpack aspirators, are known to consistently under-sample them. Nevertheless, the estimates of resting site utilization presented in Figure 2 are clearly useful for comparison of different potential resting site targets, confirming that comprehensive spraying or lining of entire rooms and houses is probably superior to targeted treatment of pots, boxes (some of which were baited with host odours) or screening barriers, all of which were placed indoors for endophilic vectors or outdoors for exophilic ones[13–17]. However, perhaps the most important observation in relation to these estimates of resting site subset utilization rates is that they rely on upon quite complicated calculations requiring at least five distinct input parameters (Equations 25a, 25b and 30), many of which have to be assumed based on best guesses or literature values from a different setting (Additional file1). In fact, none of the estimates presented in Figure 2 are based entirely upon local estimates for all the input parameters (Additional file1), and are therefore not entirely independent of each other or representative of the full range of values for any of the vector species described.
Defining and surveying targetable resource subsets
Estimating coverage and utilization of a resource subset primarily depends upon defining it in a quantifiable manner that can be readily surveyed and targeted, or artificially created in the field. The most obvious and familiar examples are the human populations targeted for universal coverage with LLINs to protect the blood resource they represent to mosquitoes[28, 43, 71]. While wild animals are difficult to survey or deliver interventions to, livestock represent blood resources that can be readily quantified and targeted with interventions. It is even easier to track numbers and functionality of artificial odour-baited traps, which mimic and compete with natural blood sources for the attentions of host-seeking mosquitoes, so their potential impact can also be predicted as a function of biological coverage of all available hosts and pseudo-hosts.
The subset of all resting sites represented by the inner surfaces of human dwellings (walls, ceilings and even furniture) are the defined target for IRS[12, 71], as well as insecticide-treated tents, shelters[73, 74], and wall linings, so coverage can be quantified as the proportion of residential structures treated. On the other hand, artificially introduced pots, boxes, curtains, linings or screening barriers compete with natural resting sites[13–17]. Mosquitoes can be captured relatively efficiently on these well-defined, convenient, standardized surfaces, so it has been suggested that these could also be treated with toxic insecticides to improve control efficiency[13–17]. While it remains difficult to consistently identify and define sugar, oviposition site or mating site resources, recent progress with observational[75, 76], trapping, tracing and labelling[18–20, 22, 23, 78–81] methods for mosquitoes is encouraging.
Adapting entomological survey techniques to measure resource utilization rates
Comparing the range of utilization rates described in Figure 2 with the predictions of potential impact illustrated in Figure 1 confirms that, despite their known limitations[52, 82], existing entomological field methods may be very useful for designing and evaluating a wide diversity of vector control products. Both blood and sugar meals can be readily identified using a variety of naturally-occurring markers and artificially added tracers[18–20, 22, 23, 78–81], thus enabling very direct, robust measurement of label uptake as a function of time or age. Utilization rates can therefore be estimated directly for subsets of these naturally occurring resources (Equations 26, 27, and 29) or indirectly for artificially introduced subsets such as odour-baited traps (Equation 28).
Utilization rates for resting site subsets (Equation 30), or indeed any other non-blood resource (Equation 25a and 25b), can also be estimated indirectly by calibrating against measurable utilization rates for quantifiable, preferred blood sources. However, the complexity of these models, and their reliance upon local measurements of several entomological input parameters, all of which have limited precision and accuracy, may well limit broader application of this approach beyond the crude application to existing data presented in Figure 2. Recent attempts to rejuvenate and improve existing entomological survey methodology for detecting resource utilization attempt events with electrified grids[84–86], sticky traps[77, 87], mechanized aspirators, and high resolution cameras[75, 76], should enable improved sensitivity of utilization attempt event detection at surveyed samples of resource subset targets. However, while such technical advances may well address the inaccuracies of attempts to estimate utilization rates for subsets of resting sites or other non-nutritional resources by improving event detection sensitivity, they are unlikely to improve their precision because considerable uncertainty arises from the need for relatively complex models (Equations 25a, 25b and 30) that require correspondingly numerous measurements of input parameters.
Fortunately, a wide range of more sensitive chemical, biochemical, genetic and biological markers, that could be applied to labelling mosquitoes when they use these other resources, are now available but these remain to be fully exploited. In fact, field studies using artificial tracers to label of both mosquitoes feeding upon sugar[18–20] and sand flies feeding upon rodent blood[88, 89], in which addition of insecticide removed almost all labelled insects from these vector populations, clearly demonstrate the validity of this strategy as a means to estimate biological coverage or utilization rates. The major advantage of labelling mosquitoes when they utilize a resource subset, rather than trapping or observing them, is that the measured proportions of marked insects can be readily analyzed with robust off-the-shelf statistical methods for binary outcomes, and are relatively precise because they have a nominator and denominator which both vary in proportion to population size or event detection sensitivity. The largest caveat to this approach is that essentially all targeted forms of that resource must be labelled on geographic scales large enough to negate the effect that mosquito dispersal has upon measurements of label uptake: immigration of unlabelled mosquitoes into the study area will increase the denominator while emigration will reduce the nominator, so that true local utilization rates will be systematically underestimated[52, 90, 91]. However, this phenomenon could also be exploited to great advantage if multiple distinct labels for various treatment arms were used to measure, and correct for, the effects of mosquito upon impact distribution in large-scale trials of vector control interventions.
The conceptual framework and entomological measurement priorities outlined here should be readily and directly applicable to almost any population of mosquitoes, vectors or other pest. It should therefore be possible to simultaneously tackle multiple vectors with integrated vector management programmes that prioritize interventions based on simultaneous, comparative field assessment of respective utilization rates for each potential target species. Recent demonstrations of the usefullness of dummy bait products containing appropriate labels but no insecticide[18–20, 88, 89] illustrate how cost-effective, robust measurements of utilization rates could be used to select and optimize available technologies for immediate use or new prototypes for development.
The concept of biological coverage can be extended to enable prediction of intervention impact for diverse vector control strategies based on estimated utilization rates for any definable, targetable resource subset. Indeed the applicability of this approach has been demonstrated here using existing entomological measurement methods to rationalize the observed impacts of LLINs, insecticide-treated livestock, and attractive toxic sugar baits upon malaria vectors. The development of improved and diversified technologies for controlling transmission of malaria, as well as a diversity of other vector-borne pathogens, could therefore be accelerated, rationalized and streamlined based on field measurements of the rates at which mosquitoes utilize targetable biological resource subsets.
While blood and sugar consumption can be readily quantified using existing methods for detecting natural markers or artificial tracers, improved techniques for labelling mosquitoes will be required to assess and optimize vector control measures which target them when they utilize resting, oviposition and mating sites. All mosquito species need sugar, resting sites, oviposition sites, and mating sites, as indeed do most arthropods of medical and veterinary importance. These resources are therefore important potential targets for the new or improved vector control methods that are clearly needed to eliminate malaria, and also a variety of other vector-borne pathogens. To enable comparative assessment of all potential resource subset targets, including sites which mosquitoes rest, oviposit or mate at, existing tracer technologies need be adapted to enable reliable, non-toxic, non-disruptive labelling of mosquitoes when they utilize these non-nutritional resource subset targets.
We thank Dr. K Aultman and Dr. D Malone for discussions that stimulated and influenced the content of this manuscript. We thank Prof T A Smith for guidance on the probablistic basis of exponential decay models, and Dr. T R Burkot for critical comments on the manuscript, as well as providing population size data for Haleta. We are also grateful to three anonymous reviewers, whose comments had a substantive influence on the final interpretation and conclusions. This work was funded by the Bill & Melinda Gates Foundation (Award numbers 45114, 52644 and OPP1032350).
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Wenger EA, Eckhoff PA: A mathematical model of the impact of present and future malaria vaccines. Malar J. 2013, 12: 126-10.1186/1475-2875-12-126.PubMed CentralView ArticlePubMedGoogle Scholar
- Eckhoff PA: Mathematical models of within-host and transmission dynamics to determine effects of malaria Interventions in a variety of transmission settings. Am J Trop Med Hyg. 2013, 88: 817-827. 10.4269/ajtmh.12-0007.PubMed CentralView ArticlePubMedGoogle Scholar
- Okell LC, Griffin JT, Kleinschmidt I, Hollingsworth TD, Churcher TS, White MT, Bousema T, Drakeley CJ, Ghani AC: The potential contribution of mass treatment to the control of Plasmodium falciparum malaria. PLoS One. 2011, 6: e20179-10.1371/journal.pone.0020179.PubMed CentralView ArticlePubMedGoogle Scholar
- Killeen GF: A second chance to tackle African malaria vector mosquitoes that avoid houses and don’t take drugs. Am J Trop Med Hyg. 2013, 88: 809-816. 10.4269/ajtmh.13-0065.PubMed CentralView ArticlePubMedGoogle Scholar
- Ferguson HM, Dornhaus A, Beeche A, Borgemeister C, Gottlieb M, Mulla MS, Gimnig JE, Fish D, Killeen GF: Ecology: a prerequisite for malaria elimination and eradication. PLoS Med. 2010, 7: e1000303-10.1371/journal.pmed.1000303.PubMed CentralView ArticlePubMedGoogle Scholar
- Russell TL, Beebe NW, Cooper RD, Lobo NF, Burkot TR: Successful malaria elimination strategies require interventions that target changing vector behaviours. Malar J. 2013, 12: 56-10.1186/1475-2875-12-56.PubMed CentralView ArticlePubMedGoogle Scholar
- Durnez L, Coosemans M: Residual Transmission of Malaria: An Old Issue for New Approaches. Anopheles Mosquitoes – New Insights into Malaria Vectors. Edited by: Manguin S. 2013, Rijeka: Intech, 671-704.Google Scholar
- Govella NJ, Chaki PP, Killeen GF: Entomological surveillance of behavioural resilience and resistance in residual malaria vector populations. Malar J. 2013, 12: 124-10.1186/1475-2875-12-124.PubMed CentralView ArticlePubMedGoogle Scholar
- Killeen GF, Seyoum A, Sikaala CH, Zomboko AS, Gimnig JE, Govella NJ, White MT: Eliminating malaria vectors. Parasit Vectors. 2013, 6: 172-10.1186/1756-3305-6-172.PubMed CentralView ArticlePubMedGoogle Scholar
- Sinka ME, Bangs MJ, Manguin S, Rubio-Palis Y, Chareonviriyaphap T, Coetzee M, Mbogo CM, Hemingway J, Patil AP, Temperley WH, Gething PW, Kabaria CW, Burkot TR, Harbach RE, Hay SI: A global map of dominant malaria vectors. Parasit Vectors. 2012, 5: 69-10.1186/1756-3305-5-69.PubMed CentralView ArticlePubMedGoogle Scholar
- Kiware SS, Chitnis N, Devine GJ, Moore SJ, Majambere S, Killeen GF: Biologically meaningfull coverage indicators for eliminating malaria transmission. Biol Lett. 2012, 8: 874-877. 10.1098/rsbl.2012.0352.PubMed CentralView ArticlePubMedGoogle Scholar
- Pluess B, Tanser FC, Lengeler C, Sharp BL: Indoor residual spraying for preventing malaria. Cochrane Database Syst Rev. 2010, 4: CD006657Google Scholar
- Scholte EJ, Ng’habi K, Kihonda J, Takken W, Paaijmans K, Abdulla S, Killeen GF, Knols BG: An entomopathogenic fungus for control of adult African malaria mosquitoes. Science. 2005, 308: 1641-1642. 10.1126/science.1108639.View ArticlePubMedGoogle Scholar
- Odiere M, Bayoh MN, Gimnig J, Vulule J, Irungu L, Walker E: Sampling outdoor, resting Anopheles gambiae and other mosquitoes (Diptera: Culicidae) in Western Kenya with clay pots. J Med Entomol. 2007, 44: 14-22. 10.1603/0022-2585(2007)44[14:SORAGA]2.0.CO;2.PubMed CentralView ArticlePubMedGoogle Scholar
- Farenhorst M, Farina D, Scholte EJ, Takken W, Hunt RH, Coetzee M, Knols BGJ: African water storage pots for the delivery of the entomopathogenic fungus Metarhizium anisopliae to the malaria vectors Anopheles gambiae s.s. and Anopheles funestus. Am J Trop Med Hyg. 2008, 78: 910-916.PubMedGoogle Scholar
- van den Bijllaardt W, Ter Braak R, Shekalaghe S, Otieno S, Mahande A, Sauerwein R, Takken W, Bousema T: The suitability of clay pots for indoor sampling of mosquitoes in an arid area in northern Tanzania. Acta Trop. 2009, 111: 197-199. 10.1016/j.actatropica.2009.04.003.View ArticlePubMedGoogle Scholar
- Müller G, Schlein Y: Sugar-questing mosquitoes in arid areas gather on scarce blossoms that can be used for control. Int J Parasitol. 2006, 36: 1077-1080. 10.1016/j.ijpara.2006.06.008.View ArticlePubMedGoogle Scholar
- Burkot TR, Russell TL, Reimer LJ, Bugoro H, Beebe NW, Cooper RD, Sukawati S, Collins FH, Lobo NF:Barrier screens: a method to sample blood-fed and host-seeking exophilic mosquitoes. Malar J. 2013, 12: 49-10.1186/1475-2875-12-49.PubMed CentralView ArticlePubMedGoogle Scholar
- Müller G, Schlein Y: Efficacy of toxic sugar baits against cistern-dwelling Anopheles claviger. Trans R Soc Trop Med Hyg. 2008, 102: 480-484. 10.1016/j.trstmh.2008.01.008.View ArticlePubMedGoogle Scholar
- Müller GC, Beier JC, Traore SF, Toure MB, Traore MM, Bah S, Doumbia S, Schlein Y:Successful field trial of attractive toxic sugar bait (ATSB) plant-spraying methods against malaria vectors in theAnopheles gambiaecomplex in Mali, West Africa.Malar J. 2010, 9: 210-10.1186/1475-2875-9-210.PubMed CentralView ArticlePubMedGoogle Scholar
- Foster WA: Mosquito sugar feeding and reproductive energetics. Annu Rev Entomol. 1995, 40: 443-474. 10.1146/annurev.en.40.010195.002303.View ArticlePubMedGoogle Scholar
- Garrett-Jones C: The human blood index of malarial vectors in relationship to epidemiological assessment. Bull World Health Organ. 1964, 30: 241-261.PubMed CentralPubMedGoogle Scholar
- Garrett-Jones C, Boreham P, Pant CP: Feeding habits of anophelines (Diptera: Culicidae) in 1971–1978, with reference to the human blood index: a review. Bull Entomol Res. 1980, 70: 165-185. 10.1017/S0007485300007422.View ArticleGoogle Scholar
- Kiware SS, Chitnis N, Moore SJ, Devine GJ, Majambere S, Killeen GF: Simplified models of vector control impact upon malaria transmission by zoophagic mosquitoes. PLoS One. 2012, 7: e37661-10.1371/journal.pone.0037661.PubMed CentralView ArticlePubMedGoogle Scholar
- Killeen GF, McKenzie FE, Foy BD, Bogh C, Beier JC: The availability of potential hosts as a determinant of feeding behaviours and malaria transmission by mosquito populations. Trans R Soc Trop Med Hyg. 2001, 95: 469-476. 10.1016/S0035-9203(01)90005-7.PubMed CentralView ArticlePubMedGoogle Scholar
- Killeen GF, Chitnis N, Moore SJ, Okumu FO: Target product profile choices for intra-domiciliary malaria vector control pesticide products: repel or kill?. Malar J. 2011, 10: 207-10.1186/1475-2875-10-207.PubMed CentralView ArticlePubMedGoogle Scholar
- Okumu FO, Moore SJ, Govella NJ, Chitnis N, Killeen GF: Potential benefits, limitations and target product-profiles of odor-baited mosquito traps as a means of malaria control. PLoS One. 2010, 5: e11573-10.1371/journal.pone.0011573.PubMed CentralView ArticlePubMedGoogle Scholar
- Killeen GF, Smith TA, Ferguson HM, Mshinda H, Abdulla S, Lengeler C, Kachur SP: Preventing childhood malaria in Africa by protecting adults from mosquitoes with insecticide-treated nets. PLoS Med. 2007, 4: e229-10.1371/journal.pmed.0040229.PubMed CentralView ArticlePubMedGoogle Scholar
- Killeen GF, Smith TA: Exploring the contributions of bednets, cattle, insecticides and excito-repellency to malaria control: A deterministic model of mosquito host-seeking behaviour and mortality. Trans R Soc Trop Med Hyg. 2007, 101: 867-880. 10.1016/j.trstmh.2007.04.022.PubMed CentralView ArticlePubMedGoogle Scholar
- Killeen GF, Moore SJ: Target product profiles for protecting against outdoor malaria transmission. Malar J. 2012, 11: 17-10.1186/1475-2875-11-17.PubMed CentralView ArticlePubMedGoogle Scholar
- Killeen GF, McKenzie FE, Foy BD, Schieffelin C, Billingsley PF, Beier JC: A simplified model for predicting malaria entomologic inoculation rates based on entomologic and parasitologic parameters relevant to control. Am J Trop Med Hyg. 2000, 62: 535-544.PubMed CentralPubMedGoogle Scholar
- Huho BJ, Briët O, Seyoum A, Sikaala CH, Bayoh N, Gimnig JE, Okumu FO, Diallo D, Abdulla S, Smith TA, Killeen GF: Consistently high estimates for the proportion of human exposure to malaria vector populations occurring indoors in rural Africa. Int J Epidemiol. 2013, 42: 235-247. 10.1093/ije/dys214.PubMed CentralView ArticlePubMedGoogle Scholar
- Elliott R: The influence of vector behaviour upon malaria transmission. Am J Trop Med Hyg. 1972, 21: 755-763.PubMedGoogle Scholar
- Seyoum A, Sikaala CH, Chanda J, Chinula D, Ntamatungiro AJ, Hawela M, Miller JM, Russell TL, Briët OJT, Killeen GF: Most exposure to Anopheles funestus and Anopheles quadriannulatus in Luangwa valley, south-east Zambia occurs indoors, even for users of insecticidal nets. Parasit Vectors. 2012, 5: 101-10.1186/1756-3305-5-101.PubMed CentralView ArticlePubMedGoogle Scholar
- Elliott R: Studies on Man-Vector Contact in Some Malarious Areas in Colombia. 1967, Geneva: World Health Organization, 32-Google Scholar
- Garrett-Jones C: A Method for Estimating the Man-Biting Rate. 1964, Geneva: World Health Organization, 22-Google Scholar
- Bugoro H, Cooper RD, Butafa C, Iro’ofa C, Mackenzie DO, Chen CC, Russell TL: Bionomics of the malaria vector Anopheles farauti in Temotu Province, Solomon Islands: issues for malaria elimination. Malar J. 2011, 10: 133-10.1186/1475-2875-10-133.PubMed CentralView ArticlePubMedGoogle Scholar
- Russell TL, Govella NJ, Azizi S, Drakeley CJ, Kachur SP, Killeen GF: Increased proportions of outdoor feeding among residual malaria vector populations following increased use of insecticide-treated nets in rural Tanzania. Malar J. 2011, 10: 80-10.1186/1475-2875-10-80.PubMed CentralView ArticlePubMedGoogle Scholar
- Kiswewski AE, Mellinger A, Spielman A, Malaney P, Sachs SE, Sachs J: A global index representing the stability of malaria transmission. Am J Trop Med Hyg. 2004, 70: 486-498.Google Scholar
- Saul A: Zooprophylaxis or zoopotentiation: the outcome of introducing animals on vector transmission is highly dependent on the mosquito mortality while searching. Malar J. 2003, 2: 32-10.1186/1475-2875-2-32.PubMed CentralView ArticlePubMedGoogle Scholar
- Saul AJ, Graves PM, Kay BH: A cyclical feeding model for pathogen transmission and its application to determine vectorial capacity from vector infection rates. J Appl Ecol. 1990, 27: 123-133. 10.2307/2403572.View ArticleGoogle Scholar
- Killeen GF, Seyoum A, Knols BGJ: Rationalizing historical successes of malaria control in Africa in terms of mosquito resource availability management. Am J Trop Med Hyg. 2004, 71 (Supplement 2): 87-93.PubMedGoogle Scholar
- Lengeler C: Insecticide-treated bed nets and curtains for preventing malaria. Cochrane Database Syst Rev. 2004, 2: CD000363Google Scholar
- Clements AN: Development, Nutrition and Reproduction. The Biology of Mosquitoes. Volume 1. 1992, London: Chapman & HallGoogle Scholar
- Beier JC: Frequent blood-feeding and restrictive sugar-feeding behavior enhance the malaria vector potential of Anopheles gambiae s.l. and An. funestus (Diptera:Culicidae) in western Kenya. J Med Entomol. 1996, 33: 613-618.View ArticlePubMedGoogle Scholar
- Achee NL, Bangs MJ, Farlow R, Killeen GF, Lindsay S, Logan JG, Moore SJ, Rowland M, Sweeney K, Torr SJ, Zwiebel LJ, Grieco JP: Spatial repellents: from discovery and development to evidence-based validation. Malar J. 2012, 11: 164-10.1186/1475-2875-11-164.PubMed CentralView ArticlePubMedGoogle Scholar
- Kambris Z, Blagborough AM, Pinto SB, Blagrove MSC, Godfray HCJ, Sinden RE, Sinkins SP: Wolbachia stimulates immune gene expression and inhibits Plasmodium development in Anopheles gambiae. PLoS Pathog. 2010, 6: e1001143-10.1371/journal.ppat.1001143.PubMed CentralView ArticlePubMedGoogle Scholar
- Thomas MB, Read AF: Can fungal biopesticides control malaria?. Nat Rev Microbiol. 2007, 5: 377-383. 10.1038/nrmicro1638.View ArticlePubMedGoogle Scholar
- Devine GJ, Perea EZ, Killeen GF, Stancil JD, Clark SJ, Morrison AC: Autodissemination of an insecticide by adult mosquitoes drammatically amplifies lethal coverage of their aquatic habitats. Proc Natl Acad Sci U S A. 2009, 106: 11530-11534. 10.1073/pnas.0901369106.PubMed CentralView ArticlePubMedGoogle Scholar
- Harris AF, McKemey AR, Nimmo D, Curtis Z, Black I, Morgan SA, Oviedo MN, Lacroix R, Naish N, Morrison NI, Collado A, Stevenson J, Scaife S, Dafa’alla T, Fu G, Phillips C, Miles A, Raduan N, Kelly N, Beech C, Donnelly CA, Petrie WD, Alphey L: Successful suppression of a field mosquito population by sustained release of engineered male mosquitoes. Nat Biotechnol. 2012, 30: 828-830. 10.1038/nbt.2350.View ArticlePubMedGoogle Scholar
- Killeen GF, Okumu FO, N’Guessan R, Coosemans M, Adeogun A, Awolola S, Etang J, Dabiré RK, Corbel V: The importance of considering community-level effects when selecting insecticidal malaria vector products. Parasit Vectors. 2011, 4: 160-10.1186/1756-3305-4-160.PubMed CentralView ArticlePubMedGoogle Scholar
- Silver JB, Service MW: Mosquito Ecology: Field Sampling Methods. 2008, Dordrecht, the Netherlands: Springer, 3View ArticleGoogle Scholar
- Gillies MT: A modified technique for age grading populations of Anopheles gambiae. Ann Trop Med Parasitol. 1958, 58: 261-273.Google Scholar
- Gillies MT, Wilkes TJ: A study of the age-composition of populations of Anopheles gambiae Giles and A. funestus Giles in North-Eastern Tanzania. Bull Entomol Res. 1965, 56: 237-262. 10.1017/S0007485300056339.View ArticlePubMedGoogle Scholar
- Govella NJ, Chaki P, Mpangile JM, Killeen GF: Monitoring mosquitoes in urban Dar es Salaam: Evaluation of resting boxes, window exit traps, CDC light traps,Ifakara tent traps and human landing catches. Parasit Vectors. 2011, 4: 40-10.1186/1756-3305-4-40.PubMed CentralView ArticlePubMedGoogle Scholar
- Sikaala CH, Killeen GF, Chanda J, Chinula D, Miller J, Russell TL, Seyoum A: Evaluation of alternative mosquito sampling methods for malaria vectors in lowland south-east Zambia. Parasit Vectors. 2013, 6: 91-10.1186/1756-3305-6-91.PubMed CentralView ArticlePubMedGoogle Scholar
- Wong J, Bayoh MN, Olang G, Killeen GF, Hamel MJ, Vulule JM, Gimnig JE: Standardizing operational vector sampling techniques for measuring malaria transmission intensity: Evaluation of six mosquito collection methods in western Kenya. Malar J. 2013, 12: 143-10.1186/1475-2875-12-143.PubMed CentralView ArticlePubMedGoogle Scholar
- Smith DL, McKenzie FE, Snow RW, Hay SI: Revisiting the basic reproductive number for malaria and its implications for malaria control. PLoS Biol. 2007, 5: e42-10.1371/journal.pbio.0050042.PubMed CentralView ArticlePubMedGoogle Scholar
- Smith DL, Dushoff J, Snow RW, Hay SI: The entomological inoculation rate and Plasmodium falciparum infection in African children. Nature. 2005, 438: 492-495. 10.1038/nature04024.PubMed CentralView ArticlePubMedGoogle Scholar
- Smith DL, Hay SI: Endemicity response timelines for Plasmodium falciparum elimination. Malar J. 2009, 8: 87-10.1186/1475-2875-8-87.PubMed CentralView ArticlePubMedGoogle Scholar
- Smith TA, Maire N, Dietz K, Killeen GF, Vounatsou P, Molineaux L, Tanner M: Relationship between entomologic inoculation rate and the force of infection for Plasmodium falciparum malaria. Am J Trop Med Hyg. 2006, 75 (Supplement 2): 11-18.PubMedGoogle Scholar
- Killeen GF, Seyoum A, Gimnig JE, Stevenson JC, Drakeley CJ, Chitnis N: Made-to-measure malaria vector control strategies: rational design based on insecticide properties and coverage of blood resources for mosquitoes. Malar J. 2014, 13: 146-10.1186/1475-2875-13-146.PubMed CentralView ArticlePubMedGoogle Scholar
- Rowland M, Durrani N, Kenward M, Mohammed N, Urahman H, Hewitt S: Control of malaria in Pakistan by applying deltamethrin insecticide to cattle: a community-randomised trial. Lancet. 2001, 357: 1837-1841. 10.1016/S0140-6736(00)04955-2.View ArticlePubMedGoogle Scholar
- Okumu FO, Killeen GF, Ogoma SB, Biswaro L, Smallegange RC, Mbeyela E, Titus E, Munk C, Ngonyani H, Takken W, Mshinda H, Mukabana WR, Moore SJ: Development and field evaluation of a mosquito lure that is more attractive than humans. PLoS One. 2010, 5: e8591-10.1371/journal.pone.0008591.View ArticleGoogle Scholar
- Gu W, Muller G, Schlein Y, Novak RJ, Beier JC: Natural plant sugar sources of Anopheles mosquitoes strongly impact malaria transmission potential. PLoS One. 2011, 6: e15996-10.1371/journal.pone.0015996.PubMed CentralView ArticlePubMedGoogle Scholar
- Huestis DL, Yaro AS, Traore AI, Adamou A, Kassogue Y, Diallo M, Timbine S, Dao A, Lehmann T: Variation in metabolic rate of Anopheles gambiae and A. arabiensis in a Sahelian village. J Exp Biol. 2011, 214: 2345-2353. 10.1242/jeb.054668.PubMed CentralView ArticlePubMedGoogle Scholar
- Marshall JM, White MT, Ghani AC, Schlein Y, Muller GC, Beier JC: Quantifying the mosquito’s sweet tooth: modelling the effectiveness of attractive toxic sugar baits (ATSB) for malaria vector control. Malar J. 2013, 12: 291-10.1186/1475-2875-12-291.PubMed CentralView ArticlePubMedGoogle Scholar
- Nyasembe VO, Teal PE, Sawa P, Tumlinson JH, Borgemeister C, Torto B: Plasmodium falciparum infection increases Anopheles gambiae attraction to nectar sources and sugar uptake. Curr Biol. 2014, 24: 217-221. 10.1016/j.cub.2013.12.022.PubMed CentralView ArticlePubMedGoogle Scholar
- Messenger LA, Matias A, Manana AN, Stiles-Ocran JB, Knowles S, Boakye DA, Coulibaly MB, Larsen M-L, Traoré AS, Diallo B, Konaté M, Guindo A, Traoré SF, Mulder CEG, Le H, Kleinschmidt I, Rowland M: Multicentre studies of insecticide-treated durable wall lining in Africa and South-East Asia: entomological efficacy and household acceptability during one year of field use. Malar J. 2012, 11: 358-10.1186/1475-2875-11-358.PubMed CentralView ArticlePubMedGoogle Scholar
- Maia MF, Robinson A, John AN, Mgando J, Simfukwe E, Moore SJ: Comparison of the CDC Backpack aspirator and the Prokopack aspirator for sampling indoor- and outdoor-resting mosquitoes in southern Tanzania. Parasit Vectors. 2011, 4: 124-10.1186/1756-3305-4-124.PubMed CentralView ArticlePubMedGoogle Scholar
- WHO: World Malaria Report 2013. 2013, Geneva: World Health OrganizationGoogle Scholar
- Graham K, Rehman H, Ahmad M, Kamal M, Khan I, Rowland M: Tents pre-treated with insecticide for malaria control in refugee camps: an entomological evaluation. Malar J. 2004, 3: 25-10.1186/1475-2875-3-25.PubMed CentralView ArticlePubMedGoogle Scholar
- Burns M, Rowland M, N’guessan R, Carneiro I, Beeche A, Ruiz SS, Kamara S, Takken W, Carnevale P, Allan R: Insecticide-treated plastic sheeting for emergency malaria prevention and shelter among displaced populations: an observational cohort study in a refugee setting in Sierra Leone. Am J Trop Med Hyg. 2012, 87: 242-250. 10.4269/ajtmh.2012.11-0744.PubMed CentralView ArticlePubMedGoogle Scholar
- Mittal PK, Sreehari U, Razdan RK, Dash AP: Evaluation of the impact of ZeroFly®, an insecticide incorporated plastic sheeting on malaria incidence in two temporary labour shelters in India. J Vector Borne Dis. 2012, 48: 138-143.Google Scholar
- Diabate A, Yaro AS, Dao A, Diallo M, Huestis DL, Lehmann T: Spatial distribution and male mating success of Anopheles gambiae swarms. BMC Evol Biol. 2011, 11: 184-10.1186/1471-2148-11-184.PubMed CentralView ArticlePubMedGoogle Scholar
- Butail S, Manoukis N, Diallo M, Ribeiro JM, Lehmann T, Paley DA: Reconstructing the flight kinematics of swarming and mating in wild mosquitoes. J R Soc Interface. 2012, 9: 2624-2638. 10.1098/rsif.2012.0150.PubMed CentralView ArticlePubMedGoogle Scholar
- Harris C, Kihonda J, Lwetoijera D, Dongus S, Devine G, Majambere S: A simple and efficient tool for trapping gravid Anopheles at breeding sites. Parasit Vectors. 2011, 4: 125-10.1186/1756-3305-4-125.PubMed CentralView ArticlePubMedGoogle Scholar
- Alcaide M, Rico C, Ruiz S, Soriguer R, Munoz J, Figuerola J: Disentangling vector-borne transmission networks: a universal DNA barcoding method to identify vertebrate hosts from arthropod bloodmeals. PLoS One. 2009, 4: e7092-10.1371/journal.pone.0007092.PubMed CentralView ArticlePubMedGoogle Scholar
- Fornadel CM, Norris DE: Increased endophily by the malaria vector Anopheles arabiensis in southern Zambia and identification of digested blood meals. Am J Trop Med Hyg. 2008, 79: 876-880.PubMed CentralPubMedGoogle Scholar
- Junnila A, Muller GC, Schlein Y: Species identification of plant tissues from the gut of Anopheles sergentii by DNA analysis. Acta Trop. 2010, 115: 227-233. 10.1016/j.actatropica.2010.04.002.View ArticlePubMedGoogle Scholar
- Manda H, Gouagna LC, Nyandat E, Kabiru EW, Jackson RR, Foster WA, Githure JI, Beier JC, Hassanali A: Discriminative feeding behaviour of Anopheles gambiae s.s. on endemic plants in western Kenya. Med Vet Entomol. 2007, 21: 103-111. 10.1111/j.1365-2915.2007.00672.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Hagler JR, Jackson CG: Methods for marking insects: current techniques and future prospects. Annu Rev Entomol. 2001, 46: 511-543. 10.1146/annurev.ento.46.1.511.View ArticlePubMedGoogle Scholar
- Vontas J, Moore SJ, Kleinschmidt I, Ranson H, Lindsay SW, Lengeler C, Hamon N, McLean T, Hemingway J: Framework for rapid assessment and adoption of new vector control tools. Trend Parasitol. 2014, 30: 191-204. 10.1016/j.pt.2014.02.005.View ArticleGoogle Scholar
- Dugassa S, Lindh JM, Torr SJ, Oyieke F, Lindsay SW, Fillinger U: Electric nets and sticky materials for analysing oviposition behaviour of gravid malaria vectors. Malar J. 2012, 11: 374-10.1186/1475-2875-11-374.PubMed CentralView ArticlePubMedGoogle Scholar
- Majambere S, Massue DJ, Mlacha Y, Govella NJ, Magesa SM, Killeen GF: Advantages and limitations of commercially available electrocuting grids for studying mosquito behaviour. Parasit Vectors. 2013, 6: 53-10.1186/1756-3305-6-53.PubMed CentralView ArticlePubMedGoogle Scholar
- Torr SJ, Della Torre A, Calzetta M, Costantini C, Vale GA:Towards a fuller understanding of mosquito behaviour: use of electrocuting grids to compare the odour-orientated responses ofAnopheles arabiensisandAn. quadriannulatusin the field.Med Vet Entomol. 2008, 22: 93-108. 10.1111/j.1365-2915.2008.00723.x.View ArticlePubMedGoogle Scholar
- Müller GC, Beier JC, Traore SF, Toure MB, Traore MM, Bah S, Doumbia S, Schlein Y: Field experiments of Anopheles gambiae attraction to local fruits/seedpods and flowering plants in Mali to optimize strategies for malaria vector control in Africa using attractive toxic sugar bait methods. Malar J. 2010, 9: 262-10.1186/1475-2875-9-262.PubMed CentralView ArticlePubMedGoogle Scholar
- Mascari TM, Stout RW, Clark JW, Gordon SW, Bast JD, Foil LD: Insecticide-treated rodent baits for sand fly control. Pestic Biochem Physiol. 2013, 106: 113-117. 10.1016/j.pestbp.2013.04.006.View ArticleGoogle Scholar
- Mascari TM, Hanafi HA, Jackson RE, Ouahabi S, Ameur B, Faraj C, Obenauer PJ, Diclaro JW, Foil LD: Ecological and control techniques for sand flies (Diptera: Psychodidae) associated with rodent reservoirs of leishmaniasis. PLoS Negl Trop Dis. 2013, 7: e2434-10.1371/journal.pntd.0002434.PubMed CentralView ArticlePubMedGoogle Scholar
- Killeen GF, Knols BG, Gu W: Taking malaria transmission out of the bottle: implications of mosquito dispersal for vector-control interventions. Lancet Infect Dis. 2003, 3: 297-303. 10.1016/S1473-3099(03)00611-X.View ArticlePubMedGoogle Scholar
- Service MW: Mosquito (Diptera: Culicidae) dispersal-the long and short of it. J Med Entomol. 1997, 34: 579-588.View ArticlePubMedGoogle Scholar
- Hawley WA, Phillips-Howard PA, Ter Kuile FO, Terlouw DJ, Vulule JM, Ombok M, Nahlen BL, Gimnig JE, Kariuki SK, Kolczak MS, Hightower AW: Community-wide effects of permethrin-treated bednets on child mortality and malaria morbidity in western Kenya. Am J Trop Med Hyg. 2003, 68 (Supplement 4): 121-127.PubMedGoogle Scholar
- WHO: Global Strategic Framework for Integrated Vector Management. 2004, Geneva: World Health OrganizationGoogle Scholar