Quantifying the mosquito’s sweet tooth: modelling the effectiveness of attractive toxic sugar baits (ATSB) for malaria vector control
© Marshall et al.; licensee BioMed Central Ltd. 2013
Received: 27 May 2013
Accepted: 18 August 2013
Published: 23 August 2013
Current vector control strategies focus largely on indoor measures, such as long-lasting insecticide treated nets (LLINs) and indoor residual spraying (IRS); however mosquitoes frequently feed on sugar sources outdoors, inviting the possibility of novel control strategies. Attractive toxic sugar baits (ATSB), either sprayed on vegetation or provided in outdoor bait stations, have been shown to significantly reduce mosquito densities in these settings.
Simple models of mosquito sugar-feeding behaviour were fitted to data from an ATSB field trial in Mali and used to estimate sugar-feeding rates and the potential of ATSB to control mosquito populations. The model and fitted parameters were then incorporated into a larger integrated vector management (IVM) model to assess the potential contribution of ATSB to future IVM programmes.
In the Mali experimental setting, the model suggests that about half of female mosquitoes fed on ATSB solution per day, dying within several hours of ingesting the toxin. Using a model incorporating the number of gonotrophic cycles completed by female mosquitoes, a higher sugar-feeding rate was estimated for younger mosquitoes than for older mosquitoes. Extending this model to incorporate other vector control interventions suggests that an IVM programme based on both ATSB and LLINs may substantially reduce mosquito density and survival rates in this setting, thereby substantially reducing parasite transmission. This is predicted to exceed the impact of LLINs in combination with IRS provided ATSB feeding rates are 50% or more of Mali experimental levels. In addition, ATSB is predicted to be particularly effective against Anopheles arabiensis, which is relatively exophilic and therefore less affected by IRS and LLINs.
These results suggest that high coverage with a combination of LLINs and ATSB could result in substantial reductions in malaria transmission in this setting. Further field studies of ATSB in other settings are needed to assess the potential of ATSB as a component in future IVM malaria control strategies.
In the last decade, declines in the incidence of Plasmodium falciparum malaria have been reported throughout sub-Saharan Africa, occurring concomitantly with the extensive scale-up of insecticide-based vector control and the switch to artemisinin-based combination therapy (ACT) as first-line treatment[1–3]. Vector control strategies have largely focused on interventions which attack the vector indoors, in particular the use of long-lasting insecticide-treated nets (LLINs) and indoor residual spraying (IRS) with insecticides[4, 5]. These are sometimes accompanied by efforts to control vector breeding sites through either source reduction or the application of larvicides. This has resulted in substantial reductions in transmission and disease in many areas; however, in other areas, the reductions have been more modest. This is partly due to the geographical variation in transmission potential which makes widespread elimination of the parasite difficult; however, there is also evidence that a residual population of outdoor-biting vectors, not targeted by indoor control measures, are able to sustain the parasite[8, 9]. Thus it is clear that new vector control tools will be needed to maintain the recent gains made. Furthermore, these tools are essential in the face of evolving drug-resistance among parasites and insecticide-resistance among vectors.
Toxic sugar baits have been proposed as a novel vector control strategy that complements existing tools such as LLINs and IRS[11, 12]. The strategy works by an “attract and kill” principle whereby mosquitoes are attracted to the fruity or flowery scent of the bait, and are then provided with a combination of sugar and an oral toxin such as boric acid, which is highly toxic to Anopheles gambiae, the primary African malaria vector[13, 14]. The strategy has been extensively tested in Israel to suppress populations of the mosquito species Anopheles sergentii, Anopheles claviger, Aedes caspius and Culex pipiens[15–18] and has recently been tested in Bandiagara, a semi-arid area of Mali, to decimate populations of the malaria vector An. gambiae s.l.. In Mali, ATSB solution sprayed onto vegetation near breeding sites was successful in reducing local vector densities by 90%, with the majority of remaining female mosquitoes being too young to transmit malaria. The strategy is, therefore, highly promising for malaria control in semi-arid areas of Africa, with further testing planned to determine its wider applicability.
A major benefit of ATSB is that, unlike LLINs and IRS, it targets female and male mosquitoes while they are outdoors. Larviciding is another important outdoor intervention, but is of limited use in rural areas where it is difficult to identify and treat all potential breeding sites[6, 19, 20]. Outdoor transmission is of growing importance as evidence suggests that intensive indoor control measures are causing transmission to shift from the mostly indoor-biting An. gambiae to the outdoor-adapted An. arabiensis[5, 8, 21, 22]. Furthermore, An. gambiae appears to be becoming increasingly adapted to outdoor biting in some areas. ATSB is also cheap and environmentally friendly, and oral toxins are not affected by the problem of insecticide-resistance. That said, it is advisable that multiple toxins be used in an operational ATSB formula. Effort will be required to ensure adequate vegetation coverage, particularly in less arid locations; however, ATSB benefits from the fact that sugar-feeding is a frequent behaviour for both male and female mosquitoes, and the sole food source for males[24, 25].
This paper provides a quantitative basis for understanding the potential utility of ATSB as part of an integrated vector management (IVM) programme in Africa. Using results from the Mali field trial described earlier, mathematical models of sugar-feeding behaviour are fitted to the data to estimate parameters underlying the effectiveness of ATSB as a vector control strategy, including the rate of feeding on ATSB-sprayed plants and the expected lifetime of mosquitoes in the field following ingestion of the toxin. These parameters and an ecological model of An. gambiae and An. arabiensis dynamics are then used to investigate the impact of ATSB, as part of an IVM programme, on vector abundance and malaria transmission. The impact of a variety of vector control strategies on malaria transmission has been widely studied using mathematical models[26–30]; however, this study represents the first mathematical evaluation of the performance of ATSB, a highly promising, novel vector control strategy.
Data were analysed from the above-mentioned ATSB field trial conducted near Bandiagara, Mali. Two sites were monitored in this trial – an experimental site where ATSB was administered, and a control site where attractive (non-toxic) sugar bait (ASB) was used. Male and female catch numbers were recorded for six light traps at each site over a one-week pretreatment period and for 30 days post-treatment. The proportion of marked mosquitoes was also recorded, as in these experiments a coloured food dye that can be detected for several days after feeding was added to both ATSB and ASB solutions. To estimate age distribution among female mosquitoes, the number of gonotrophic cycles completed was recorded for a sample of 200 mosquitoes before and after the intervention, for both the experimental and control sites.
Basic model selection
For a given set of parameter values, an expression for the model likelihood can be derived by assuming the observed mosquito catch numbers are sampled from a negative binomial distribution with mean equal to the model-predicted mosquito density and variance to be estimated. A normal prior was used for daily mosquito mortality μ, with a mean of 0.1 per day and a standard deviation of 0.01 per day. Uninformative uniform priors were used for all other model parameters. Posterior parameter distributions were estimated using an MCMC sampling procedure (Additional file1).
Model incorporating gonotrophic cycles
Here, δ represents the reciprocal of the gonotrophic cycle length. The schematic for this model is shown in Additional file4: Figure S1. Analogous equations apply in the control setting, replacing the subscript E with the subscript C. Analytic solutions to these equations are not feasible and so the differential equations must be solved numerically in order to compare the model predictions to the data.
Once again, an MCMC sampling procedure was used to estimate the posterior distributions of each of the model parameters. The likelihood function used was the same as for the basic models, multiplied by a term accounting for the comparison between the model-predicted and observed distribution of gonotrophic cycle number (Additional file1). A normal prior was used for the parameter δ, with a mean of 0.33 per day and a standard deviation of 0.03 per day, and uninformative uniform priors were used for all other parameters.
Model of integrated vector management
The IVM model divides the mosquito life cycle into larval, pupal and adult stages, thus allowing stage-specific interventions to be modelled. Density-dependence is modelled at the larval stage, based on a study in Tanzania suggesting a linear relationship between larval density and mortality. Parameters were estimated from the entomological literature and the Garki Project, undertaken in the 1970s in the Garki District of Nigeria (Additional file5: Table S3). With this framework in place, a variety of interventions were simulated in isolation and synchrony to calculate their expected effects on An. gambiae and An. arabiensis densities.
Here, κ represents the probability that a vector becomes infectious per human bite, assuming it survives long enough, and Q0 represents the proportion of blood-meals taken on humans in the absence of LLINs and IRS. Three transmission settings were considered with preintervention EIRs of 100 (very high transmission), 50 (high transmission) and 10 (moderate transmission). The human biting rate was varied according to the setting, and was consistent with estimates from Nigeria and Tanzania for the very high transmission setting[37–39]. Parameter estimates and their sources are included in Additional file5: Table S3 and Additional file6: Table S4.
Estimates of exposure to ATSB and its impact on mortality using simple models
Parameter estimates for basic sugar-feeding model
Prior distribution (per day):
Posterior estimate with 95% credible interval (per day):
Female ASB-feeding rate (control): sf,C
0.15 (0.12 - 0.19)
Female ATSB-feeding rate (experiment): sf,E
0.50 (0.27 - 0.97)
Male ASB-feeding rate (control): sm,C
0.15 (0.12 – 0.19)
Male ATSB-feeding rate (experiment): sm,E
0.46 (0.27 – 0.84)
Female death rate, μ f
Male death rate, μ m
Female ATSB death rate: μ f ,ATSB
11.7 (6.3 - 22.6)
Male ATSB death rate: μ m ,ATSB
11.0 (6.1 - 20.3)
The feeding rate of most relevance is that of females in the experimental setting, since only female mosquitoes bite and transmit malaria parasites. An ATSB feeding rate of 0.5 per female per day (95% CrI: 0.27-0.97) was estimated for the Mali experiment. Estimates of feeding rates differ significantly between the experimental and control settings (0.50 per day for the experimental setting versus 0.15 per day for the control setting) which could be due to differences in the relative abundance of sugar bait in the two settings (either in terms of application level or the availability of natural sugar sources), or due to dye decay causing the ASB-feeding rates to be underestimated (in the experimental setting, dye decay can be ignored since toxin-induced death occurs at a faster rate). Given that mosquitoes also feed on natural sugar sources, the total sugar-feeding rate will be higher than both of these estimates.
The death rates following ingestion of ATSB are important indicators of the effectiveness of ATSB at reducing mosquito density. For females, an estimated death rate of 11.7 per day corresponds to a mean lifetime of 2.1 hours following ATSB consumption (95% CrI: 1.1-3.8 hours). This estimate is consistent with laboratory experiments showing 100% lethality within 12 hours. It should be noted that, while relevant to mosquito density, this parameter is less relevant to malaria control since mosquitoes tend not to seek blood meals after feeding on ATSB.
Incorporating gonotrophic cycles
Female mosquitoes blood-feed to fuel the production of eggs. The number of blood-feeding and egg-laying (gonotrophic) cycles they have completed provides a measure of their age – each cycle takes approximately three days to complete – and their ability to transmit pathogens. At the earliest, mosquitoes can become infected with malaria on their first gonotrophic cycle, and it takes at least another two cycles for the parasites to incubate within the mosquito. This means that only female mosquitoes that have completed three or more gonotrophic cycles can be infectious to humans. Gonotrophic cycle numbers as high as eight were recorded in the Mali field trial and these provide an opportunity to investigate trends in sugar-feeding with age.
Parameter estimates for sugar-feeding model incorporating gonotrophic cycles
Prior distribution (per day):
Posterior estimate with 95% credible interval (per day):
ASB-feeding rate (0–2 gonotrophic cycles, control): sA,C
0.25 (0.18 - 0.31)
ASB-feeding rate (3 or more gonotrophic cycles, control): sB,C
0.035 (0.009 – 0.076)
ATSB-feeding rate (0–2 gonotrophic cycles, experiment): sA,E
0.84 (0.53 – 1.21)
ATSB-feeding rate (3 or more gonotrophic cycles, experiment): sB,E
0.12 (0.03 – 0.27)
Female death rate, μ f
Female ATSB death rate: μ f ,ATSB
12.2 (7.5 – 23.9)
Reciprocal of gonotrophic cycle length: δ
The potential impact of ATSB as part of integrated vector management (IVM)
To assess the potential contribution of ATSB to IVM strategies, the models and parameters described above were used in conjunction with an existing ecological model of Anopheles population dynamics and an existing model of the effects of LLINs and IRS on mosquito densities, modified slightly as in Griffin et al.. For larviciding, the case of Bacillus thuringiensis var. israelensis (BTI) applied to larval breeding sites was considered. BTI was found to reduce larval density by 88% where applied and to increase larval and pupal death rates by a constant factor. Coverage levels for current vector interventions were assumed to be either 80% or 50% (Additional file1), and ATSB was assumed to be implemented at levels leading to an exposure rate analogous to that in the Mali experimental setting or at levels such that the exposure rate would be half that of the Mali setting. The combined model is described in Additional file1.
Impact of IVM strategies including ATSB on EIR
Reductions in vector density give a clear comparison of the relative impact of IVM strategies; however a more direct measure of human exposure to malaria is the entomological inoculation rate (EIR), defined as the average number of infective bites per person per year. The EIR is more sensitive to the age breakdown of the vector population, since older mosquitoes are more likely to be infectious to humans.
The promise of ATSB described here directly follows from extending the results of a successful field trial in Bandiagara, Mali to a range of different transmission intensities, and modelling its impact in combination with a variety of other vector control strategies. The models suggest that high coverage with a combination of LLINs and ATSB at levels similar to those in the Mali field trial is expected to cause significant reductions in EIR, exceeding the predicted impact of LLINs in combination with other interventions such as IRS or larviciding. Furthermore, ATSB is expected to perform favourably even at half the exposure rates of the Mali field trial.
The benefit of ATSB is that it kills mosquitoes while they are outdoors, thus targeting a different stage of the mosquito gonotrophic cycle than LLINs and IRS. Larviciding targets a different stage of the mosquito life cycle; however ATSB has the advantage that it skews the adult age distribution towards younger mosquitoes, which is beneficial for malaria control because only older mosquitoes have time to acquire, incubate and transmit the parasite. It is also cheap and environmentally friendly and, while not modelled here, it targets both male and female mosquitoes.
An interesting result from the model fits was a significant trend in declining sugar-feeding rate with age among female mosquitoes. Since older mosquitoes are more likely to transmit malaria, a strategy that targets these older mosquitoes is desirable; however, if mosquitoes are targeted when they are young, they will not reach the required age to transmit malaria, so both approaches are effective. This is evidenced by the scarcity of mosquitoes having completed more than two gonotrophic cycles within a few weeks of ATSB application in Mali (Figure 3). That said, it is not clear the extent to which this trend is influenced by the high rate of sugar-feeding following emergence. Regardless, the results from this trial suggest a high death rate among young mosquitoes, which is predicted to reduce the number of adult mosquitoes capable of transmitting malaria similarly to strategies that target adult mosquitoes in an age-independent manner.
Also worthy of note is that the sugar-feeding rates estimated here are for ATSB and ASB-sprayed vegetation at the coverage levels of the Mali experiment. An estimate of the total sugar-feeding rate on all available vegetation would be of interest to understanding the maximum potential of ATSB at reducing mosquito density. One way to measure this would be to spray patches of vegetation with ASB containing different coloured dyes. Coloured and multicoloured mosquitoes could then be used to infer the total sugar-feeding rate in a similar manner to how total population size is inferred in a traditional mark-release-recapture experiment. Also of interest is the relationship between coverage level and ATSB-feeding rate. In the Mali trial, one square metre spots of vegetation were sprayed every three metres around breeding sites leading to the ATSB-feeding rates estimated here. A relationship between these variables would assist in operational and cost-effectiveness analyses.
The performance of ATSB in different geographic and seasonal settings is of great interest. Field trials have thus far been conducted in Mali and Israel[14–18] and provide a proof of principle in semi-arid areas. In Israel, ATSB has been shown to outcompete natural sugar sources and to reduce mosquito populations even in sugar-rich environments. Modelling results presented here predict ATSB to be effective even if exposure rates are half those of the Mali experiment. However, the performance of ATSB remains to be tested in settings with a greater abundance of natural sugar sources. A further complication is that heavy rains can wash ATSB off of vegetation, making reapplication necessary during the rainy season. A complementary approach is the provision of covered bait stations, which have proven successful in Israel[15, 16], and are currently undergoing field testing and product development in other settings. Another product enhancement being considered is combining ATSB with larvicides which mosquitoes may carry to breeding sites after sugar-feeding.
As for any modelling exercise, simplifications have been made and limitations exist that mean that the results are indicative rather than predictive. The sugar-feeding model is parameterized by fitting to the available trial data from one semi-arid location; however, the parameter estimates include a high degree of uncertainty. Furthermore, the true underlying dynamics may be more complicated than suggested by the simple parsimonious models explored here. For instance, sugar-feeding rates are likely to decline more gradually with age than was possible to detect by fitting to the available data and dye decay was not incorporated but is known to occur in the wild. However, the general trends inferred here capture important features of vector control with ATSB. Parameter estimates for other vector control strategies are collated from several different locations and neglect phenomena such as waning of efficiency with time. Whilst the LLIN model captures the effect size observed in randomized trials, both the IRS and larviciding models have not been validated against trial data. Furthermore, An. gambiae and An. arabiensis have been considered as separate entities here, while future studies could investigate potential shifts in species composition under a variety of IVM combinations using a species competition model. Therefore, the IVM model predictions should be interpreted in this light as providing insight into the potential of ATSB to contribute to future integrated vector control programs rather than precise predictions.
In summary, the models presented suggest that ATSB, or modifications of this approach to target outdoor mosquitoes, could be important to consider in future IVM programmes, especially in combination with LLINs and in semi-arid areas. ATSB kills mosquitoes while they are outdoors and skews the adult age distribution towards younger mosquitoes, leading to substantial reductions in both sporozoite rate and EIR. Further field testing is needed to address operational issues (in particular the degree of overall coverage that can be obtained) and to determine its efficacy in a range of other settings. If the predictions of this modelling effort hold true, ATSB could be a useful additional tool for malaria control in permissive settings.
JMM acknowledges support from a Medical Research Council (MRC) Centre extension grant and a fellowship from the MRC/Department for International Development. MTW and ACG acknowledge support from the Bill and Melinda Gates Foundation Vaccine Modeling Initiative.
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