Estimating the potential impact of Attractive Targeted Sugar Baits (ATSBs) as a new vector control tool for Plasmodium falciparum malaria

Abstract

Background: Attractive targeted sugar baits (ATSBs) are a promising new tool for malaria control as they can target outdoor-feeding mosquito populations, in contrast to current vector control tools which predominantly target indoor-feeding mosquitoes.

Methods: It was sought to estimate the potential impact of these new tools on Plasmodium falciparum malaria prevalence in African settings by combining data from a recent entomological field trial of ATSBs undertaken in Mali with mathematical models of malaria transmission. The key parameter determining impact on the mosquito population is the excess mortality due to ATSBs, which is estimated from the observed reduction in mosquito catch numbers. A mathematical model capturing the life cycle of P. falciparum malaria in mosquitoes and humans and incorporating the excess mortality was used to estimate the potential epidemiological effect of ATSBs.

Results: The entomological study showed a significant reduction of ~ 57% (95% CI 33-72%) in mosquito catch numbers, and a larger reduction of ~ 89% (95% CI 75-100%) in the entomological inoculation rate due to the fact that, in the presence of ATSBs, most mosquitoes do not live long enough to transmit malaria. The excess mortality due to ATSBs was estimated to be lower (mean 0.09 per mosquito per day, seasonal range 0.07-0.11 per day) than the bait feeding rate obtained from one-day staining tests (mean 0.34 per mosquito per day, seasonal range 0.28-0.38 per day).

Conclusions: From epidemiological modelling, it was predicted that ATSBs could result in large reductions (> 30% annually) in prevalence and clinical incidence of malaria, even in regions with an existing high malaria burden. These results suggest that this new tool could provide a promising addition to existing vector control tools and result in significant reductions in malaria burden across a range of malaria-endemic settings.

Keywords: Malaria; Mosquito; Vector control.

Fig. 1

a, b Number of female mosquitoes caught per village using CDC traps in 2016 (a) and 2017 (b) for each group of 7 villages [16]. ATSBs were introduced in Group 2 in 2017. Error bars show the standard deviation between villages. c, d Estimated EIR in ATSB and control villages calculated from the fraction of mosquitoes infected among those caught using the human landing catch method in 2017, split into indoor (c) and outdoor (d) collection [16]. Shaded regions show 95% bootstrap percentile interval based on 5000 bootstrap samples

Fig. 2

a Estimated bait feeding rate in control villages, calculated as the fraction of female mosquitoes stained in 1-day tests using non-toxic but food dye-stained bait between April and December 2017. Shaded region represents binomial confidence interval. b Estimated excess mortality rate µ_ATSB in intervention villages, calculated as the additional death rate (see Eq. 2) required to reproduce the observed difference in mosquito numbers between the intervention and control villages (using functions fitted to data as described in Methods). Shaded region represents 95% confidence interval where base mortality = natural mortality rate 0.096/day

Fig. 3

Sine functions fitted to 2017 mosquito collection data for Groups 1–2, compared with values calculated using Eqs. 3a-b, with equilibrium values M_EQ calculated using Eq. 5b. Calculated values for Group 2 shown for a excess mortality given by 1-day staining tests (Fig. 2a) b mean excess mortality extrapolated from fitting functions (Fig. 2b)

Fig. 4

The lines show the equilibrium year-round average model-estimated a all-age parasite prevalence and b all-age clinical incidence (cases per person per year), plotted against annual entomological inoculation rate (EIR). The coloured points show annual EIR values calculated from field data. The shaded regions correspond to the 95% posterior credible intervals for the modelled relationship between EIR, prevalence and incidence (see Methods)

Fig. 5

a, b Model-predicted all-ages parasite prevalence (a) and clinical incidence (b) over the course of 3 years after introduction of ATSBs (red line) and without ATSBs during the same period (black line). The green dotted line shows the assumed rainfall pattern (in arbitrary units). For these runs the excess mortality µ_ATSB is set to the average value estimated from field trial results (0.09/day). Shaded areas represent range of values obtained using model parameters in 95% credible interval. c, d Model-predicted reduction in all-age year-round parasite prevalence (c) and clinical incidence (d) in first year of ATSB use as a function of prevalence/incidence under non-ATSB conditions and ATSB excess mortality µ_ATSB. All simulations use the seasonal Mali rainfall profile shown in a and b

Fig. 6

a, b Model-predicted all-age parasite prevalence (a) and clinical incidence (b) over the course of 3 years after introduction of ATSBs (red line) and without ATSBs during the same period (black line) under the assumption of constant rainfall (green dotted line) For these runs the excess mortality µ_ATSB is set to the average value estimated from field trial results (0.09/day). Shaded areas represent range of values obtained using model parameters in 95% credible interval. c, d Model-predicted reduction in all-ages year-round prevalence (c) and clinical incidence (d) as a function of prevalence/incidence under non-ATSB conditions and ATSB excess mortality µ_ATSB, assuming constant rainfall

Fig. 7

Predicted time progression of all-age parasite prevalence (a) and clinical incidence (b) under Mali rainfall conditions, over 120-day period representing the time period used to estimate the ATSB excess mortality µ_ATSB. Values are shown for µ_ATSB values of zero (control), for the average value estimated from field trial results (0.09/day), and for the variable values shown in Fig. 2b. Credible intervals are not shown here as the red and green curves overlap