Temperature, mosquito feeding status and mosquito density influence the measured bio-efficacy of insecticide-treated nets in cone assays

BACKGROUND

The WHO cone bioassay is routinely used to evaluate the bioefficacy of insecticide-treated nets (ITNs) for product pre-qualification and confirmation of continued ITN performance during operational monitoring. Despite its standardized nature, variability is often observed between tests. We investigated the influence of temperature in the testing environment, mosquito feeding status and mosquito density on cone bioassay results.

METHODS

Cone bioassays were conducted on MAGNet (alphacypermethrin) and Veeralin (alphacypermethrin and piperonyl butoxide (PBO)) ITNs, using laboratory-reared pyrethroid-resistant Anopheles funestus sensu stricto (FUMOZ strain) mosquitoes. Three experiments were conducted using standard cone bioassays following WHO-recommended test parameters, with one variable changed in each bioassay: (i) environmental temperature during exposure: 22-23 °C, 26-27 °C, 29-30 °C and 32-33 °C; (ii) feeding regimen before exposure: sugar starved for 6 h, blood-fed or sugar-fed; and (iii) mosquito density per cone: 5, 10, 15 and 20 mosquitoes. For each test, 15 net samples per treatment arm were tested with four cones per sample (N = 60). Mortality after 24, 48 and 72 h post-exposure to ITNs was recorded.

RESULTS

There was a notable influence of temperature, feeding status and mosquito density on An. funestus mortality for both types of ITNs. Mortality at 24 h post-exposure was significantly higher at 32-33 °C than at 26-27 °C for both the MAGNet [19.33% vs 7%; odds ratio (OR): 3.96, 95% confidence interval (CI): 1.99-7.87, P < 0.001] and Veeralin (91% vs 47.33%; OR: 22.20, 95% CI: 11.45-43.05, P < 0.001) ITNs. Mosquito feeding status influenced the observed mortality. Relative to sugar-fed mosquitoes, The MAGNet ITNs induced higher mortality among blood-fed mosquitoes (7% vs 3%; OR: 2.23, 95% CI: 0.94-5.27, P = 0.068) and significantly higher mortality among starved mosquitoes (8% vs 3%, OR: 2.88, 95% CI: 1.25-6.63, P = 0.013); in comparison, the Veeralin ITNs showed significantly lower mortality among blood-fed mosquitoes (43% vs 57%; OR: 0.56, 95% CI: 0.38-0.81, P = 0.002) and no difference for starved mosquitoes (58% vs 57%; OR: 1.05, 95% CI: 0.72-1.51, P = 0.816). Mortality significantly increased with increasing mosquito density for both the MAGNet (e.g. 5 vs 10 mosquitoes: 7% vs 12%; OR: 1.81, 95% CI: 1.03-3.20, P = 0.040) and Veeralin (e.g. 5 vs 10 mosquitoes: 58% vs 71%; OR 2.06, 95% CI: 1.24-3.42, P = 0.005) ITNs.

CONCLUSIONS

The results of this study highlight that the testing parameters temperature, feeding status and mosquito density significantly influence the mortality measured in cone bioassays. Careful adherence to testing parameters outlined in WHO ITN testing guidelines will likely improve the repeatability of studies within and between product testing facilities.

Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus

BACKGROUND

Accurately determining the age and survival probabilities of adult mosquitoes is crucial for understanding parasite transmission, evaluating the effectiveness of control interventions and assessing disease risk in communities. This study was aimed at demonstrating the rapid identification of epidemiologically relevant age categories of Anopheles funestus, a major Afro-tropical malaria vector, through the innovative combination of infrared spectroscopy and machine learning, instead of the cumbersome practice of dissecting mosquito ovaries to estimate age based on parity status.

METHODS

Anopheles funestus larvae were collected in rural south-eastern Tanzania and reared in an insectary. Emerging adult females were sorted by age (1-16 days old) and preserved using silica gel. Polymerase chain reaction (PCR) confirmation was conducted using DNA extracted from mosquito legs to verify the presence of An. funestus and to eliminate undesired mosquitoes. Mid-infrared spectra were obtained by scanning the heads and thoraces of the mosquitoes using an attenuated total reflection-Fourier transform infrared (ATR-FT-IR) spectrometer. The spectra (N = 2084) were divided into two epidemiologically relevant age groups: 1-9 days (young, non-infectious) and 10-16 days (old, potentially infectious). The dimensionality of the spectra was reduced using principal component analysis, and then a set of machine learning and multi-layer perceptron (MLP) models were trained using the spectra to predict the mosquito age categories.

RESULTS

The best-performing model, XGBoost, achieved overall accuracy of 87%, with classification accuracy of 89% for young and 84% for old An. funestus. When the most important spectral features influencing the model performance were selected to train a new model, the overall accuracy increased slightly to 89%. The MLP model, utilizing the significant spectral features, achieved higher classification accuracy of 95% and 94% for the young and old An. funestus, respectively. After dimensionality reduction, the MLP achieved 93% accuracy for both age categories.

CONCLUSIONS

This study shows how machine learning can quickly classify epidemiologically relevant age groups of An. funestus based on their mid-infrared spectra. Having been previously applied to An. gambiae, An. arabiensis and An. coluzzii, this demonstration on An. funestus underscores the potential of this low-cost, reagent-free technique for widespread use on all the major Afro-tropical malaria vectors. Future research should demonstrate how such machine-derived age classifications in field-collected mosquitoes correlate with malaria in human populations.

Molecular and entomological surveillance of malaria vectors in urban and rural communities of Benguela Province, Angola

BACKGROUND

Malaria is a major public health problem in Angola, with Anopheles gambiae sensu lato (s.l.) and An. funestus s.l. being the primary vectors. This study aimed to clarify the information gaps concerning local Anopheles mosquito populations. Our objectives were to assess their abundance, geographical dispersion, and blood-feeding patterns. We also investigated their insecticide resistance. Molecular methods were used to identify sibling species, determine the origin of blood meals, measure Plasmodium falciparum infection rates, and detect the presence of knockdown resistance (kdr) mutations.

METHODS

Adult mosquitoes were collected indoors using CDC light traps from nine randomly selected households at two sentinel sites with distinct ecological characteristics. The samples were collected from 1 February to 30 June 2022. Anopheles mosquitoes were morphologically identified and subjected to molecular identification. Unfed Anopheles females were tested for the presence of P. falciparum DNA in head and thorax, and engorged females were screened for the source of the blood meals. Additionally, members of An. gambiae complex were genotyped for the presence of the L1014F and L1014S kdr mutations.

RESULTS

In total, 2226 adult mosquitoes were collected, including 733 Anopheles females. Molecular identification revealed the presence of Anopheles coluzzii, An. gambiae senso stricto (s.s.), An. arabiensis, and An. funestus s.s. Notably, there was the first record of An. coluzzii/An. gambiae s.s. hybrid and An. vaneedeni in Benguela Province. Plasmodium falciparum infection rates for An. coluzzii at the urban sentinel site and An. funestus s.s. at the rural site were 23.1% and 5.7%, respectively. The L1014F kdr mutation was discovered in both resistant and susceptible An. coluzzii mosquitoes, while the L1014S mutation was detected in An. gambiae s.s. for the first time in Benguela Province. No kdr mutations were found in An. arabiensis.

CONCLUSIONS

This study provides valuable insights into the molecular characteristics of malaria vectors from the province of Benguela, emphasising the need for continuous surveillance of local Anopheles populations regarding the establishment of both kdr mutations for tailoring vector control interventions.