MGDrivE 2: A simulation framework for gene drive systems incorporating seasonality and epidemiological dynamics

MGDrivE 3: A decoupled vector-human framework for epidemiological simulation of mosquito genetic control tools and their surveillance

Novel mosquito genetic control tools, such as CRISPR-based gene drives, hold great promise in reducing the global burden of vector-borne diseases. As these technologies advance through the research and development pipeline, there is a growing need for modeling frameworks incorporating increasing levels of entomological and epidemiological detail in order to address questions regarding logistics and biosafety. Epidemiological predictions are becoming increasingly relevant to the development of target product profiles and the design of field trials and interventions, while entomological surveillance is becoming increasingly important to regulation and biosafety. We present MGDrivE 3 (Mosquito Gene Drive Explorer 3), a new version of a previously-developed framework, MGDrivE 2, that investigates the spatial population dynamics of mosquito genetic control systems and their epidemiological implications. The new framework incorporates three major developments: i) a decoupled sampling algorithm allowing the vector portion of the MGDrivE framework to be paired with a more detailed epidemiological framework, ii) a version of the Imperial College London malaria transmission model, which incorporates age structure, various forms of immunity, and human and vector interventions, and iii) a surveillance module that tracks mosquitoes captured by traps throughout the simulation. Example MGDrivE 3 simulations are presented demonstrating the application of the framework to a CRISPR-based homing gene drive linked to dual disease-refractory genes and their potential to interrupt local malaria transmission. Simulations are also presented demonstrating surveillance of such a system by a network of mosquito traps. MGDrivE 3 is freely available as an open-source R package on CRAN (https://cran.r-project.org/package=MGDrivE2) (version 2.1.0), and extensive examples and vignettes are provided. We intend the software to aid in understanding of human health impacts and biosafety of mosquito genetic control tools, and continue to iterate per feedback from the genetic control community.

Genetic and geographic population structure in the malaria vector, Anopheles farauti, provides a candidate system for pioneering confinable gene-drive releases

Indoor insecticide applications are the primary tool for reducing malaria transmission in the Solomon Archipelago, a region where Anopheles farauti is the only common malaria vector. Due to the evolution of behavioural resistance in some An. farauti populations, these applications have become less effective. New malaria control interventions are therefore needed in this region, and gene-drives provide a promising new technology. In considering developing a population-specific (local) gene-drive in An. farauti, we detail the species' population genetic structure using microsatellites and whole mitogenomes, finding many spatially confined populations both within and between landmasses. This strong population structure suggests that An. farauti would be a useful system for developing a population-specific, confinable gene-drive for field release, where private alleles can be used as Cas9 targets. Previous work on Anopheles gambiae has used the Cardinal gene for the development of a global population replacement gene-drive. We therefore also analyse the Cardinal gene to assess whether it may be a suitable target to engineer a gene-drive for the modification of local An. farauti populations. Despite the extensive population structure observed in An. farauti for microsatellites, only one remote island population from Vanuatu contained fixed and private alleles at the Cardinal locus. Nonetheless, this study provides an initial framework for further population genomic investigations to discover high-frequency private allele targets in localized An. farauti populations. This would enable the development of gene-drive strains for modifying localised populations with minimal chance of escape and may provide a low-risk route to field trial evaluations.

MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations

Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or non-functional effector genes, within intervention sites. This entails the need to distribute mosquito traps efficiently such that an allele of interest is detected as quickly as possible-ideally when remediation is still viable. Additionally, insecticide-based tools such as bednets are compromised by insecticide-resistance alleles for which there is also a need to detect as quickly as possible. To this end, we present MGSurvE (Mosquito Gene SurveillancE): a computational framework that optimizes trap placement for genetic surveillance of mosquito populations such that the time to detection of an allele of interest is minimized. A key strength of MGSurvE is that it allows important biological features of mosquitoes and the landscapes they inhabit to be accounted for, namely: i) resources required by mosquitoes (e.g., food sources and aquatic breeding sites) can be explicitly distributed through a landscape, ii) movement of mosquitoes may depend on their sex, the current state of their gonotrophic cycle (if female) and resource attractiveness, and iii) traps may differ in their attractiveness profile. Example MGSurvE analyses are presented to demonstrate optimal trap placement for: i) an Aedes aegypti population in a suburban landscape in Queensland, Australia, and ii) an Anopheles gambiae population on the island of São Tomé, São Tomé and Príncipe. Further documentation and use examples are provided in project's documentation. MGSurvE is intended as a resource for both field and computational researchers interested in mosquito gene surveillance.

Gene drives for the extinction of wild metapopulations

Population-suppressing gene drives may be capable of extinguishing wild populations, with proposed applications in conservation, agriculture, and public health. However, unintended and potentially disastrous consequences of release of drive-engineered individuals are extremely difficult to predict. We propose a model for the dynamics of a sex ratio-biasing drive, and using simulations, we show that failure of the suppression drive is often a natural outcome due to stochastic and spatial effects. We further demonstrate rock-paper-scissors dynamics among wild-type, drive-infected, and extinct populations that can persist for arbitrarily long times. Gene drive-mediated extinction of wild populations entails critical complications that lurk far beyond the reach of laboratory-based studies. Our findings help in addressing these challenges.

Expansions to the MGDrivE suite for simulating the efficacy of novel gene-drive constructs in the control of mosquito-borne diseases

OBJECTIVE

The MGDrivE (MGDrivE 1 and MGDrivE 2) modeling framework provides a flexible and expansive environment for testing the efficacy of novel gene-drive constructs for the control of mosquito-borne diseases. However, the existing model framework did not previously support several features necessary to simulate some types of intervention strategies. Namely, current MGDrivE versions do not permit modeling of small molecule inducible systems for controlling gene expression in gene drive designs or the inheritance patterns of self-eliminating gene drive mechanisms.

RESULTS

Here, we demonstrate a new MGDrivE 2 module that permits the simulation of gene drive strategies incorporating small molecule-inducible systems and self-eliminating gene drive mechanisms. Additionally, we also implemented novel sparsity-aware sampling algorithms for improved computational efficiency in MGDrivE 2 and supplied an analysis and plotting function applicable to the outputs of MGDrivE 1 and MGDrivE 2.

Making waves: Comparative analysis of gene drive spread characteristics in a continuous space model

With their ability to rapidly increase in frequency, gene drives can be used to modify or suppress target populations after an initial release of drive individuals. Recent advances have revealed many possibilities for different types of drives, and several of these have been realized in experiments. These drives have advantages and disadvantages related to their ease of construction, confinement and capacity to be used for modification or suppression. Though characteristics of these drives have been explored in modelling studies, assessment in continuous space environments has been limited, often focusing on outcomes rather than fundamental properties. Here, we conduct a comparative analysis of many different gene drive types that have the capacity to form a wave of advance in continuous space using individual-based simulations in continuous space. We evaluate the drive wave speed as a function of drive performance and ecological parameters, which reveals substantial differences between drive performance in panmictic versus spatial environments. In particular, we find that suppression drive waves are uniquely vulnerable to fitness costs and undesired CRISPR cleavage activity in embryos by maternal deposition. Some drives, however, retain robust performance even with widely varying efficiency parameters. To gain a better understanding of drive waves, we compare their panmictic performance and find that the rate of wild-type allele removal is correlated with drive wave speed, though this is also affected by other factors. Overall, our results provide a useful resource for understanding the performance of drives in spatially continuous environments, which may be most representative of potential drive deployment in many relevant scenarios.

HMS, Marshall JM. MGDrivE 3: A decoupled vector-human framework for epidemiological simulation of mosquito genetic control tools and their surveillance

Novel mosquito genetic control tools, such as CRISPR-based gene drives, hold great promise in reducing the global burden of vector-borne diseases. As these technologies advance through the research and development pipeline, there is a growing need for modeling frameworks incorporating increasing levels of entomological and epidemiological detail in order to address questions regarding logistics and biosafety. Epidemiological predictions are becoming increasingly relevant to the development of target product profiles and the design of field trials and interventions, while entomological surveillance is becoming increasingly important to regulation and biosafety. We present MGDrivE 3 (Mosquito Gene Drive Explorer 3), a new version of a previously-developed framework, MGDrivE 2, that investigates the spatial population dynamics of mosquito genetic control systems and their epidemiological implications. The new framework incorporates three major developments: i) a decoupled sampling algorithm allowing the vector portion of the MGDrivE framework to be paired with a more detailed epidemiological framework, ii) a version of the Imperial College London malaria transmission model, which incorporates age structure, various forms of immunity, and human and vector interventions, and iii) a surveillance module that tracks mosquitoes captured by traps throughout the simulation. Example MGDrivE 3 simulations are presented demonstrating the application of the framework to a CRISPR-based homing gene drive linked to dual disease-refractory genes and their potential to interrupt local malaria transmission. Simulations are also presented demonstrating surveillance of such a system by a network of mosquito traps. MGDrivE 3 is freely available as an open-source R package on CRAN (https://cran.r-project.org/package=MGDrivE2) (version 2.1.0), and extensive examples and vignettes are provided. We intend the software to aid in understanding of human health impacts and biosafety of mosquito genetic control tools, and continue to iterate per feedback from the genetic control community.

HM, Antoshechkin I, Marshall JM, Akbari OS. Eliminating Malaria Vectors with Precision Guided Sterile Males

Controlling the principal African malaria vector, the mosquito Anopheles gambiae, is considered essential to curtail malaria transmission. However existing vector control technologies rely on insecticides, which are becoming increasingly ineffective. Sterile insect technique (SIT) is a powerful suppression approach that has successfully eradicated a number of insect pests, yet the A. gambiae toolkit lacks the requisite technologies for its implementation. SIT relies on iterative mass-releases of non-biting, non-driving, sterile males which seek out and mate with monandrous wild females. Once mated, females are permanently sterilized due to mating-induced refractoriness, which results in population suppression of the subsequent generation. However, sterilization by traditional methods renders males unfit, making the creation of precise genetic sterilization methods imperative. Here we develop precision guided Sterile Insect Technique (pgSIT) in the mosquito A. gambiae for inducible, programmed male-sterilization and female-elimination for wide scale use in SIT campaigns. Using a binary CRISPR strategy, we cross separate engineered Cas9 and gRNA strains to disrupt male-fertility and female-essential genes, yielding >99.5% male-sterility and >99.9% female-lethality in hybrid progeny. We demonstrate that these genetically sterilized males have good longevity, are able to induce population suppression in cage trials, and are predicted to eliminate wild A. gambiae populations using mathematical models, making them ideal candidates for release. This work provides a valuable addition to the malaria genetic biocontrol toolkit, for the first time enabling scalable SIT-like confinable suppression in the species.

HM, Lee HF, Marshall JM, Dimopoulos G, James AA. Dual effector population modification gene-drive strains of the African malaria mosquitoes, Anopheles gambiae and Anopheles coluzzii

Proposed genetic approaches for reducing human malaria include population modification, which introduces genes into vector mosquitoes to reduce or prevent parasite transmission. We demonstrate the potential of Cas9/guide RNA (gRNA)-based gene-drive systems linked to dual antiparasite effector genes to spread rapidly through mosquito populations. Two strains have an autonomous gene-drive system coupled to dual anti-Plasmodium falciparum effector genes comprising single-chain variable fragment monoclonal antibodies targeting parasite ookinetes and sporozoites in the African malaria mosquitoes Anopheles gambiae (AgTP13) and Anopheles coluzzii (AcTP13). The gene-drive systems achieved full introduction within 3 to 6 mo after release in small cage trials. Life-table analyses revealed no fitness loads affecting AcTP13 gene-drive dynamics but AgTP13 males were less competitive than wild types. The effector molecules reduced significantly both parasite prevalence and infection intensities. These data supported transmission modeling of conceptual field releases in an island setting that shows meaningful epidemiological impacts at different sporozoite threshold levels (2.5 to 10 k) for human infection by reducing malaria incidence in optimal simulations by 50 to 90% within as few as 1 to 2 mo after a series of releases, and by ≥90% within 3 mo. Modeling outcomes for low sporozoite thresholds are sensitive to gene-drive system fitness loads, gametocytemia infection intensities during parasite challenges, and the formation of potentially drive-resistant genome target sites, extending the predicted times to achieve reduced incidence. TP13-based strains could be effective for malaria control strategies following validation of sporozoite transmission threshold numbers and testing field-derived parasite strains. These or similar strains are viable candidates for future field trials in a malaria-endemic region.

MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito population

Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or non-functional effector genes, within intervention sites. This entails the need to distribute mosquito traps efficiently such that an allele of interest is detected as quickly as possible - ideally when remediation is still viable. Additionally, insecticide-based tools such as bednets are compromised by insecticide-resistance alleles for which there is also a need to detect as quickly as possible. To this end, we present MGSurvE (Mosquito Gene SurveillancE): a computational framework that optimizes trap placement for genetic surveillance of mosquito populations such that the time to detection of an allele of interest is minimized. A key strength of MGSurvE is that it allows important biological features of mosquitoes and the landscapes they inhabit to be accounted for, namely: i) resources required by mosquitoes (e.g., food sources and aquatic breeding sites) can be explicitly distributed through a landscape, ii) movement of mosquitoes may depend on their sex, the current state of their gonotrophic cycle (if female) and resource attractiveness, and iii) traps may differ in their attractiveness profile. Example MGSurvE analyses are presented to demonstrate optimal trap placement for: i) an Aedes aegypti population in a suburban landscape in Queensland, Australia, and ii) an Anopheles gambiae population on the island of São Tomé, São Tomé and Príncipe. Further documentation and use examples are provided in project's documentation. MGSurvE is freely available as an open-source Python package on pypi (https://pypi.org/project/MGSurvE/). It is intended as a resource for both field and computational researchers interested in mosquito gene surveillance.

Spatial dynamics of malaria transmission

The Ross-Macdonald model has exerted enormous influence over the study of malaria transmission dynamics and control, but it lacked features to describe parasite dispersal, travel, and other important aspects of heterogeneous transmission. Here, we present a patch-based differential equation modeling framework that extends the Ross-Macdonald model with sufficient skill and complexity to support planning, monitoring and evaluation for Plasmodium falciparum malaria control. We designed a generic interface for building structured, spatial models of malaria transmission based on a new algorithm for mosquito blood feeding. We developed new algorithms to simulate adult mosquito demography, dispersal, and egg laying in response to resource availability. The core dynamical components describing mosquito ecology and malaria transmission were decomposed, redesigned and reassembled into a modular framework. Structural elements in the framework-human population strata, patches, and aquatic habitats-interact through a flexible design that facilitates construction of ensembles of models with scalable complexity to support robust analytics for malaria policy and adaptive malaria control. We propose updated definitions for the human biting rate and entomological inoculation rates. We present new formulas to describe parasite dispersal and spatial dynamics under steady state conditions, including the human biting rates, parasite dispersal, the "vectorial capacity matrix," a human transmitting capacity distribution matrix, and threshold conditions. An [Formula: see text] package that implements the framework, solves the differential equations, and computes spatial metrics for models developed in this framework has been developed. Development of the model and metrics have focused on malaria, but since the framework is modular, the same ideas and software can be applied to other mosquito-borne pathogen systems.

The current landscape of software tools for the climate-sensitive infectious disease modelling community

Climate-sensitive infectious disease modelling is crucial for public health planning and is underpinned by a complex network of software tools. We identified only 37 tools that incorporated both climate inputs and epidemiological information to produce an output of disease risk in one package, were transparently described and validated, were named (for future searching and versioning), and were accessible (ie, the code was published during the past 10 years or was available on a repository, web platform, or other user interface). We noted disproportionate representation of developers based at North American and European institutions. Most tools (n=30 [81%]) focused on vector-borne diseases, and more than half (n=16 [53%]) of these tools focused on malaria. Few tools (n=4 [11%]) focused on food-borne, respiratory, or water-borne diseases. The under-representation of tools for estimating outbreaks of directly transmitted diseases represents a major knowledge gap. Just over half (n=20 [54%]) of the tools assessed were described as operationalised, with many freely available online.

Leveraging eco-evolutionary models for gene drive risk assessment

Engineered gene drives create potential for both widespread benefits and irreversible harms to ecosystems. CRISPR-based systems of allelic conversion have rapidly accelerated gene drive research across diverse taxa, putting field trials and their necessary risk assessments on the horizon. Dynamic process-based models provide flexible quantitative platforms to predict gene drive outcomes in the context of system-specific ecological and evolutionary features. Here, we synthesize gene drive dynamic modeling studies to highlight research trends, knowledge gaps, and emergent principles, organized around their genetic, demographic, spatial, environmental, and implementation features. We identify the phenomena that most significantly influence model predictions, discuss limitations of biological complexity and uncertainty, and provide insights to promote responsible development and model-assisted risk assessment of gene drives.

Driving down malaria transmission with engineered gene drives

The last century has witnessed the introduction, establishment and expansion of mosquito-borne diseases into diverse new geographic ranges. Malaria is transmitted by female Anopheles mosquitoes. Despite making great strides over the past few decades in reducing the burden of malaria, transmission is now on the rise again, in part owing to the emergence of mosquito resistance to insecticides, antimalarial drug resistance and, more recently, the challenges of the COVID-19 pandemic, which resulted in the reduced implementation efficiency of various control programs. The utility of genetically engineered gene drive mosquitoes as tools to decrease the burden of malaria by controlling the disease-transmitting mosquitoes is being evaluated. To date, there has been remarkable progress in the development of CRISPR/Cas9-based homing endonuclease designs in malaria mosquitoes due to successful proof-of-principle and multigenerational experiments. In this review, we examine the lessons learnt from the development of current CRISPR/Cas9-based homing endonuclease gene drives, providing a framework for the development of gene drive systems for the targeted control of wild malaria-transmitting mosquito populations that overcome challenges such as with evolving drive-resistance. We also discuss the additional substantial works required to progress the development of gene drive systems from scientific discovery to further study and subsequent field application in endemic settings.

An algebraic framework for structured epidemic modelling

Pandemic management requires that scientists rapidly formulate and analyse epidemiological models in order to forecast the spread of disease and the effects of mitigation strategies. Scientists must modify existing models and create novel ones in light of new biological data and policy changes such as social distancing and vaccination. Traditional scientific modelling workflows detach the structure of a model-its submodels and their interactions-from its implementation in software. Consequently, incorporating local changes to model components may require global edits to the code base through a manual, time-intensive and error-prone process. We propose a compositional modelling framework that uses high-level algebraic structures to capture domain-specific scientific knowledge and bridge the gap between how scientists think about models and the code that implements them. These algebraic structures, grounded in applied category theory, simplify and expedite modelling tasks such as model specification, stratification, analysis and calibration. With their structure made explicit, models also become easier to communicate, criticize and refine in light of stakeholder feedback. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

A dynamically structured matrix population model for insect life histories observed under variable environmental conditions

Various environmental drivers influence life processes of insect vectors that transmit human disease. Life histories observed under experimental conditions can reveal such complex links; however, designing informative experiments for insects is challenging. Furthermore, inferences obtained under controlled conditions often extrapolate poorly to field conditions. Here, we introduce a pseudo-stage-structured population dynamics model to describe insect development as a renewal process with variable rates. The model permits representing realistic life stage durations under constant and variable environmental conditions. Using the model, we demonstrate how random environmental variations result in fluctuating development rates and affect stage duration. We apply the model to infer environmental dependencies from the life history observations of two common disease vectors, the southern (Culex quinquefasciatus) and northern (Culex pipiens) house mosquito. We identify photoperiod, in addition to temperature, as pivotal in regulating larva stage duration, and find that carefully timed life history observations under semi-field conditions accurately predict insect development throughout the year. The approach we describe augments existing methods of life table design and analysis, and contributes to the development of large-scale climate- and environment-driven population dynamics models for important disease vectors.

Modelling homing suppression gene drive in haplodiploid organisms

Gene drives have shown great promise for suppression of pest populations. These engineered alleles can function by a variety of mechanisms, but the most common is the CRISPR homing drive, which converts wild-type alleles to drive alleles in the germline of heterozygotes. Some potential target species are haplodiploid, in which males develop from unfertilized eggs and thus have only one copy of each chromosome. This prevents drive conversion, a substantial disadvantage compared to diploids where drive conversion can take place in both sexes. Here, we study homing suppression gene drives in haplodiploids and find that a drive targeting a female fertility gene could still be successful. However, such drives are less powerful than in diploids and suffer more from functional resistance alleles. They are substantially more vulnerable to high resistance allele formation in the embryo owing to maternally deposited Cas9 and guide RNA and also to somatic cleavage activity. Examining spatial models where organisms move over a continuous landscape, we find that haplodiploid suppression drives surprisingly perform nearly as well as in diploids, possibly owing to their ability to spread further before inducing strong suppression. Together, these results indicate that gene drive can potentially be used to effectively suppress haplodiploid populations.

Risk management recommendations for environmental releases of gene drive modified insects

The ability to engineer gene drives (genetic elements that bias their own inheritance) has sparked enthusiasm and concerns. Engineered gene drives could potentially be used to address long-standing challenges in the control of insect disease vectors, agricultural pests and invasive species, or help to rescue endangered species. However, risk concerns and uncertainty associated with potential environmental release of gene drive modified insects (GDMIs) have led some stakeholders to call for a global moratorium on such releases or the application of other strict precautionary measures to mitigate perceived risk assessment and risk management challenges. Instead, we provide recommendations that may help to improve the relevance of risk assessment and risk management frameworks for environmental releases of GDMIs. These recommendations include: (1) developing additional and more practical risk assessment guidance to ensure appropriate levels of safety; (2) making policy goals and regulatory decision-making criteria operational for use in risk assessment so that what constitutes harm is clearly defined; (3) ensuring a more dynamic interplay between risk assessment and risk management to manage uncertainty through closely interlinked pre-release modelling and post-release monitoring; (4) considering potential risks against potential benefits, and comparing them with those of alternative actions to account for a wider (management) context; and (5) implementing a modular, phased approach to authorisations for incremental acceptance and management of risks and uncertainty. Along with providing stakeholder engagement opportunities in the risk analysis process, the recommendations proposed may enable risk managers to make choices that are more proportionate and adaptive to potential risks, uncertainty and benefits of GDMI applications, and socially robust.

Potential use of gene drive modified insects against disease vectors, agricultural pests and invasive species poses new challenges for risk assessment

Potential future application of engineered gene drives (GDs), which bias their own inheritance and can spread genetic modifications in wild target populations, has sparked both enthusiasm and concern. Engineered GDs in insects could potentially be used to address long-standing challenges in control of disease vectors, agricultural pests and invasive species, or help to rescue endangered species, and thus provide important public benefits. However, there are concerns that the deliberate environmental release of GD modified insects may pose different or new harms to animal and human health and the wider environment, and raise novel challenges for risk assessment. Risk assessors, risk managers, developers, potential applicants and other stakeholders at many levels are currently discussing whether there is a need to develop new or additional risk assessment guidance for the environmental release of GD modified organisms, including insects. Developing new or additional guidance that is useful and practical is a challenge, especially at an international level, as risk assessors, risk managers and many other stakeholders have different, often contrasting, opinions and perspectives toward the environmental release of GD modified organisms, and on the adequacy of current risk assessment frameworks for such organisms. Here, we offer recommendations to overcome some of the challenges associated with the potential future development of new or additional risk assessment guidance for GD modified insects and provide considerations on areas where further risk assessment guidance may be required.