Biology subjects

Hancock, P. A.

Publications and source records attributed to Hancock, P. A..

6 recordsLinked to original sources

bioRxiv2024

The potential of gene drive releases in malaria vector species to reduce the malaria burden in different African environments

Gene drive technologies are a promising means of malaria control with the potential to cause widespread and sustained reductions in transmission. In real environments, however, gene drive impacts will depend on local ecological and epidemiological factors. We develop a data-driven model to investigate the impacts of a gene drive that causes vector population suppression. We apply the model to sixteen [~]12,000km2 areas of west Africa that span variation in vector ecology and malaria prevalence. We simulate gene drive releases targeting different vector species combinations, and estimate reductions in vector abundance, malaria prevalence and clinical cases in children. Average reductions in vector abundance ranged from 77.3%-99.1% across areas when the gene drive releases targeted all major vector species (Anopheles gambiae, An. coluzzii, An. arabiensis, and An. funestus). Impacts on malaria depended strongly on which vector species were targeted. Consistently strong impacts required releases targeting all vector species, which reduced prevalence by over 90% in all areas. When the major vector species An. gambiae and An. coluzzii were targeted, average reductions in prevalence ranged from no reduction to 72% across areas, and reductions in cumulative clinical cases ranged from zero to 57%. When other new interventions including RTS,S vaccination and pyrethroid-PBO bednets were in place, at least 60% more clinical cases were averted when gene drives were added, demonstrating the benefits of integrated interventions. Our results give new insights into the factors determining the impacts of gene drives on malaria across different African settings.

genetics↗

bioRxiv2021

Modelling spatiotemporal trends in the frequency of genetic mutations conferring insecticide target-site resistance in African malaria vector species

Resistance in malaria vectors to pyrethroids, the most widely used class of insecticides for malaria vector control, threatens the continued efficacy of vector control tools. Target-site resistance is an important genetic resistance mechanism caused by mutations in the voltage-gated sodium channel (Vgsc) gene that encodes the pyrethroid target-site. Understanding the geographic distribution of target-site resistance, and temporal trends across different vector species, can inform strategic deployment of vector control tools. Here we develop a Bayesian statistical spatiotemporal model to interpret species-specific trends in the frequency of the most common resistance mutations, Vgsc-995S and Vgsc-995F, in three major malaria vector species Anopheles gambiae, An. coluzzii, and An. arabiensis. For nine selected countries, we develop annual predictive maps which reveal geographically-structured patterns of spread of each mutation at regional and continental scales. The results show associations, as well as stark differences, in spread dynamics of the two mutations across the three vector species. The coverage of ITNs was an influential predictor of Vgsc allele frequencies in our models. Our mapped Vgsc allele frequencies are a significant partial predictor of phenotypic resistance to the pyrethroid deltamethrin in An. gambiae complex populations, highlighting the importance of molecular surveillance of resistance mechanisms.

genetics↗

bioRxiv2021

Increasing role of pyrethroid-resistant Anopheles funestus in malaria transmission in the Lake Zone, Tanzania: implications for the evaluation of novel vector control products

Anopheles funestus is playing an increasing role in malaria transmission in parts of sub-Saharan Africa, where An. gambiae s.s. has been effectively controlled by long-lasting insecticidal nets. We investigated vector population bionomics, insecticide resistance and malaria transmission dynamics in 86 study clusters in North-West Tanzania. An. funestus s.l. represented 94.5% (4740/5016) of all vectors and was responsible for the majority of malaria transmission (96.5%), with a sporozoite rate of 3.4% and average monthly entomological inoculation rate (EIR) of 4.57 per house. Micro-geographical heterogeneity in species composition, abundance and transmission was observed across the study district in relation to key ecological differences between northern and southern clusters, with significantly higher densities, proportions and EIR of An. funestus s.l. collected from the south. An. gambiae s.l. (5.5%) density, principally An. arabiensis (81.1%) and An. gambiae s.s. (18.9%), was much lower and closely correlated with seasonal rainfall. Both An. funestus s.l. and An. gambiae s.l. were similarly resistant to alpha-cypermethrin and permethrin. Overexpression of CYP9K1, CYP6P3, CYP6P4 and CYP6M2 and high L1014F-kdr mutation frequency were detected in An. gambiae s.s. populations. Study findings highlight the urgent need for novel vector control tools to tackle persistent malaria transmission in the Lake Region of Tanzania.

zoology↗

bioRxiv2019

Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology

Mosquitoes are important vectors for pathogens of humans and other vertebrate animals. Some aspects of adult mosquito behavior and mosquito ecology play an important role in determining the capacity of vector populations to transmit pathogens. Here, we re-examine factors affecting the transmission of pathogens by mosquitoes using a new approach. Unlike most previous models, this framework considers the behavioral states and state transitions of adult mosquitoes through a sequence of activity bouts. We developed a new framework for individual-based simulation models called MBITES (Mosquito Bout-based and Individual-based Transmission Ecology Simulator). In MBITES, it is possible to build models that simulate the behavior and ecology of adult mosquitoes in exquisite detail on complex resource landscapes generated by spatial point processes. We also developed an ordinary differential equation model which is the Kolmogorov forward equations for models developed in MBITES under a specific set of simplifying assumptions. While infection of the mosquito and pathogen development are one possible part of a mosquitos state, that is not the main focus. Using extensive simulation using some models developed in MBITES, we show that vectorial capacity can be understood as an emergent property of simple behavioral algorithms interacting with complex resource landscapes, and that relative density or sparsity of resources and the need to search can have profound consequences for mosquito populations capacity to transmit pathogens.

ecology↗

bioRxiv2019

Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors

The impact of insecticide resistance in malaria vectors is poorly understood and quantified. Here a series of geospatial datasets for insecticide resistance in malaria vectors are provided so that trends in resistance in time and space can be quantified and the impact of resistance found in wild populations on malaria transmission in Africa can be assessed. Data are also provided for common genetic markers of resistance to support analyses of whether these genetic data can improve the ability to monitor resistance in low resource settings. Specifically, data have been collated and geopositioned for the prevalence of insecticide resistance, as measured by standard bioassays, in representative samples of individual species or species complexes. Data are provided for the Anopheles gambiae species complex, the Anopheles funestus subgroup, and for nine individual vector species. In addition, allele frequencies for known resistance associated markers in the Voltage-gated sodium channel (Vgsc) are provided. In total, eight analysis-ready, standardised, geopositioned datasets encompassing over 20,000 African mosquito collections between 1957 and 2017 are provided.

zoology↗

bioRxiv2018

Predicting The Spatial Dynamics Of Wolbachia Infections In Aedes aegypti Arbovirus Vector Populations In Heterogeneous Landscapes

O_LIA promising strategy for reducing the transmission of dengue and other arboviral human diseases by Aedes aegypti mosquito vector populations involves field introductions of the endosymbiotic bacteria Wolbachia. Wolbachia infections inhibit viral transmission by the mosquito, and can spread between mosquito hosts to reach high frequencies in the vector population. Wolbachia spreads by maternal transmission, and spread dynamics can be variable and highly dependent on natural mosquito population dynamics, population structure and fitness components.\nC_LIO_LIWe develop a mathematical model of an Ae. aegypti metapopulation that incorporates empirically validated relationships describing density-dependent mosquito fitness components. We assume that density dependence relationships differ across subpopulations, and construct heterogeneous landscapes for which model-predicted patterns of variation in mosquito abundance and demography approximate those observed in field populations. We then simulate Wolbachia release strategies similar to that used in field trials.\nC_LIO_LIWe show that our model can produce rates of spatial spread of Wolbachia similar to those observed following field releases.\nC_LIO_LIWe then investigate how different types of spatio-temporal variation in mosquito habitat, as well as different fitness costs incurred by Wolbachia on the mosquito host, influence predicted spread rates. We find that fitness costs reduce spread rates more strongly when the habitat landscape varies temporally due to stochastic and seasonal processes.\nC_LIO_LIOur empirically based modelling approach represents effects of environmental heterogeneity on the spatial spread of Wolbachia. The models can assist in interpreting observed spread patterns following field releases and in designing suitable release strategies for targeting spatially heterogeneous vector populations.\nC_LI

ecology↗