Modelling the impact of vector control interventions on Anopheles gambiae population dynamics

Abstract

Background: Intensive anti-malaria campaigns targeting the Anopheles population have demonstrated substantial reductions in adult mosquito density. Understanding the population dynamics of Anopheles mosquitoes throughout their whole lifecycle is important to assess the likely impact of vector control interventions alone and in combination as well as to aid the design of novel interventions.

Methods: An ecological model of Anopheles gambiae sensu lato populations incorporating a rainfall-dependent carrying capacity and density-dependent regulation of mosquito larvae in breeding sites is developed. The model is fitted to adult mosquito catch and rainfall data from 8 villages in the Garki District of Nigeria (the 'Garki Project') using Bayesian Markov Chain Monte Carlo methods and prior estimates of parameters derived from the literature. The model is used to compare the impact of vector control interventions directed against adult mosquito stages--long-lasting insecticide treated nets (LLIN), indoor residual spraying (IRS)-- and directed against aquatic mosquito stages, alone and in combination on adult mosquito density.

Results: A model in which density-dependent regulation occurs in the larval stages via a linear association between larval density and larval death rates provided a good fit to seasonal adult mosquito catches. The effective mosquito reproduction number in the presence of density-dependent regulation is dependent on seasonal rainfall patterns and peaks at the start of the rainy season. In addition to killing adult mosquitoes during the extrinsic incubation period, LLINs and IRS also result in less eggs being oviposited in breeding sites leading to further reductions in adult mosquito density. Combining interventions such as the application of larvicidal or pupacidal agents that target the aquatic stages of the mosquito lifecycle with LLINs or IRS can lead to substantial reductions in adult mosquito density.

Conclusions: Density-dependent regulation of anopheline larvae in breeding sites ensures robust, stable mosquito populations that can persist in the face of intensive vector control interventions. Selecting combinations of interventions that target different stages in the vector's lifecycle will result in maximum reductions in mosquito density.

Figure 1

Relationship between larval mortality rate and first instar larval density. A: When batches of first instar larvae of equal age are placed in breeding sites, the daily mortality rate increases at higher larval densities. The data can be described by a linear (solid) or logistic (dashed) function. B: Placing batches of first instar larvae into breeding sites at staggered times results in a larval population with a mixed age structure. The data are well described by a linear (solid) or a quadratic (dashed) curve. See the Supplementary Information for further details on fitting. Error bars represent 95% confidence intervals for the data. Data are from Njunwa (1993) [12].

Figure 2

Model fits to data from the Garki Project. The number of adult mosquitoes trapped per night (pyrethrum spray catches of indoor resting mosquitoes) aggregated over the village is shown as red markers and compared with the model prediction. The black solid line represents the model prediction with the median posterior estimates and the envelopes depict the inter-quartile range (dark grey) and 95% credible intervals (light grey). The measured rainfall is shown in blue.

Figure 3

Estimates of mosquito reproduction number. Estimates of the effective mosquito reproduction number with density-dependent regulation at the aquatic stages for 8 villages in the Garki Project (black). The measured rainfall is shown in blue. The dashed line indicates where R_eff = 1 and each female mosquito replaces herself.

Figure 4

Mosquito density as a percentage of pre-intervention density. The density of adult mosquitoes as a percentage of total density resulting from no intervention, LLINs (at 50% and 80% coverage) and IRS at 80% coverage, predicted by a model with constant mosquito emergence (no density-dependent feedback; grey bars) in comparison with the prediction by the model including the full mosquito lifecycle presented in this paper (white bars). Error bars show 95% credible intervals arising from the uncertainty in the model fitting only. There will be additional uncertainty due to variation in the effectiveness of the interventions.

Figure 5

Percentage mosquito density due to combinations of vector control interventions. The model-predicted density of adult mosquitoes as a percentage of total density resulting from deploying IRS at 80% (red bars), larvicide at 20% (light green bars), larvicide at 50% (dark green bars), or pupacide at 50% coverage (blue bars) in combination with LLINs (orange bars) at A: 0% LLIN coverage, B: 20% LLIN coverage, C: 50% LLIN coverage, D: 80% LLIN coverage. The model corresponds to the full mosquito lifecycle. Error bars show 95% credible intervals arising from the uncertainty in the model fitting only. There will be additional uncertainty due to variation in the effectiveness of the interventions.