A compiled computer programme that allow classic homing gene drive and driving Y chromosomes to be explored separately and together (see Additional file 1: Note 19). (CDF 144 kb)
This chapter discusses the modeling priorities as gene drive mosquito projects advance from the lab to the field. Mathematical modeling has a central role to play in determining the impact that gene drive systems could have, alongside other interventions, toward the goal of malaria elimination. The chapter focuses on CRISPR-based homing gene drive systems, as these currently have the most promise to contribute to the malaria eradication agenda. Good models should satisfy the principle of parsimony and be tailored to the questions they are designed for. In the words of physicist Albert Einstein, "Everything should be made as simple as possible, but no simpler," and in the words of statistician George Box, "All models are wrong, but some are useful." The step to building a mosquito population model is to decompose the mosquito life cycle into distinct life stages. While most models incorporate an aquatic juvenile and terrestrial adult stage, the inclusion of further life-history detail is more variable.
Abstract CRISPR-based gene drives are self-sustaining genetic elements that have been recently generated in the laboratory with the aim to develop potent genetic vector control measures targeting disease vectors including Anopheles gambiae. We have shown that a gene drive directed against the gene doublesex (dsx) effectively suppressed the reproductive capability of mosquito populations reared in small laboratory cages. These experiments, though informative, do not recapitulate the complexity of mosquito behaviour in natural environments. Additional information is needed to bridge the gap between laboratory and the field to validate the vector control potential of the technology. We have investigated the suppressing activity of dsx gene drive strain Ag(QFS)1 on age-structured populations of Anopheles gambiae in large indoor cages that provide a more challenging ecology by more closely mimicking natural conditions and stimulating complex mosquito behaviours. Under these conditions, the Ag(QFS)1 drive spreads rapidly from a single release to the indoor large-cage populations at low initial frequency, leading to full population suppression within one year and without inducing resistance to the gene drive. Initial stochastic simulations of the expected population dynamics, as based on life history parameters estimated in small cages, did not fully capture the observed dynamics in the large cages. Thus, we used the method of approximate Bayesian computation to better estimate population dynamics in the more realistic ecological setting in large cages, allowing the mosquitoes to show a complex feeding and reproductive behaviour. Together, these results establish a new paradigm for generating data to bridge laboratory and field studies, and form an essential component in the stepwise and sound development of gene drive based vector control tools.
We focus on strategies for controlling the epidemiology of mosquito-borne diseases that target the mosquito vector. In order to assess a particular strategy of mosquito population control, two broad issues require attention: the dynamics of the population, and the impact of the intervention on those dynamics. We describe two modelling approaches that are important tools in these respective tasks. Firstly, backwards modelling, the method of using models to interpret complex data, is a valuable means to understanding mosquito ecology. We exemplify backwards modelling with a brief review of research into how larval competition impacts on mosquito population dynamics, an important question to answer given that this density dependent process may significantly interact with some intervention strategies. Secondly, forwards modelling, whereby models are used to forecast the outcome of interacting ecological processes, allows ecological knowledge to be utilised so that the impacts of a particular strategy can be predicted. In particular, forward models allow investigation of how the performance of strategies may vary across different ecological settings. We exemplify forward modelling with an illustrative model of how climatic temperature may influence the effectiveness of a fungal biopesticide intervention. We end the chapter with a brief discussion on important future directions.
The establishment and spread of a disease within a metapopulation is influenced both by dynamics within each population and by the host and pathogen spatial processes through which they are connected. We develop a spatially explicit metapopulation model to investigate how the form of host and disease dispersal jointly influence the probability of disease establishment and invasion. We show that diseases are more likely to establish if both the host and the disease tend to disperse locally, since the former leads to the spatial aggregation of host populations in the environment while the latter facilitates the pathogen's exploitation of this spatial pattern. In contrast, local pathogen dispersal is likely to reduce the probability of subsequent disease spread because it increases the spatial segregation of infected and uninfected populations. The effects of local dispersal on disease dynamics are less pronounced when the pathogen spreads through the movement of infected hosts and more pronounced when pathogen dispersal is independent (for example through airborne viruses) though the details of host and pathogen biology can be important. These spatial effects tend to be more pronounced if the sites available for host occupation are themselves spatially aggregated.
A compiled computer programme written in Mathematica, that allows further exploration of the CATCHA mechanism (see Additional file 1: Note 16). (CDF 44 kb)
In the context of widespread mosquito resistance to currently available pesticides, novel, precise genetic vector control methods aimed at population suppression or trait replacement are a potentially powerful approach that could complement existing malaria elimination interventions. Such methods require knowledge of vector population composition, dynamics, behaviour and role in transmission. Here were characterized these parameters in three representative villages, Bana, Pala and Souroukoudingan, of the Sudano-Sahelian belt of Burkina Faso, a region where bed net campaigns have recently intensified.From July 2012 to November 2015, adult mosquitoes were collected monthly using pyrethroid spray catches (PSC) and human landing catches (HLC) in each village. Larval habitat prospections assessed breeding sites abundance at each site. Mosquitoes collected by PSC were identified morphologically, and then by molecular technique to species where required, to reveal the seasonal dynamics of local vectors. Monthly entomological inoculation rates (EIR) that reflect malaria transmission dynamics were estimated by combining the HLC data with mosquito sporozoite infection rates (SIR) identified through ELISA-CSP. Finally, population and EIR fluctuations were fit to locally-collected rainfall data to highlight the strong seasonal determinants of mosquito abundance and malaria transmission in this region.The principal malaria vectors found were in the Anopheles gambiae complex. Mosquito abundance peaked during the rainy season, but there was variation in vector species composition between villages. Mean survey HLC and SIR were similar across villages and ranged from 18 to 48 mosquitoes/person/night and from 3.1 to 6.6% prevalence. The resulting monthly EIRs were extremely high during the rainy season (0.91-2.35 infectious bites/person/day) but decreased substantially in the dry season (0.03-0.22). Vector and malaria transmission dynamics generally tracked seasonal rainfall variations, and the highest mosquito abundances and EIRs occurred in the rainy season. However, despite low residual mosquito populations, mosquitoes infected with malaria parasites remained present in the dry season.These results highlight the important vector control challenge facing countries with high EIR despite the recent campaigns of bed net distribution. As demonstrated in these villages, malaria transmission is sustained for large parts of the year by a very high vector abundance and high sporozoite prevalence, resulting in seasonal patterns of hyper and hypo-endemicity. There is, therefore, an urgent need for additional vector control tools that can target endo and exophillic mosquito populations.
It is generally well understood that some ecological factors select for increased and others for decreased dispersal. However, it has remained difficult to assess how the evolutionary dynamics are influenced by the spatio-temporal structure of the environment. We address this question with an individual-based model that enables habitat structure to be controlled through variables such as patch size, patch turnover rate, and patch quality. Increasing patch size at the expense of patch density can select for more or less dispersal, depending on the initial configuration. In landscapes consisting of high-quality and long-lived habitat patches, patch degradation selects for increased dispersal, yet patch loss may select for reduced dispersal. These trends do not depend on the component of life-history that is affected by habitat quality or the component of life-history through which density-dependence operates. Our results are based on a mathematical method that enables derivation of both the evolutionary stable strategy and the stationary genotype distribution that evolves in a polymorphic population. The two approaches generally lead to similar predictions. However, the evolutionary stable strategy assumes that the ecological and evolutionary time scales can be separated, and we find that violation of this assumption can critically alter the evolutionary outcome.
The development of genetically modified (GM) mosquitoes and their subsequent field release offers innovative and cost-effective approaches to reduce mosquito-borne diseases, such as malaria. A sex-distorting autosomal transgene has been developed recently in G3 mosquitoes, a laboratory strain of the malaria vector
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