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Sánchez C. HM, Smith DL, Marshall JM. MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations. PLoS Comput Biol 2024; 20:e1012046. [PMID: 38709820 PMCID: PMC11098508 DOI: 10.1371/journal.pcbi.1012046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 05/16/2024] [Accepted: 04/02/2024] [Indexed: 05/08/2024] Open
Abstract
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.
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Affiliation(s)
- Héctor M. Sánchez C.
- Divisions of Epidemiology and Biostatistics, University of California Berkeley, Berkeley, California, United States of America
| | - David L. Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, Washington, United States of America
| | - John M. Marshall
- Divisions of Epidemiology and Biostatistics, University of California Berkeley, Berkeley, California, United States of America
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Li M, Kandul NP, Sun R, Yang T, Benetta ED, Brogan DJ, Antoshechkin I, Sánchez C. HM, Zhan Y, DeBeaubien NA, Loh YM, Su MP, Montell C, Marshall JM, Akbari OS. Targeting Sex Determination to Suppress Mosquito Populations. bioRxiv 2023:2023.04.18.537404. [PMID: 37131747 PMCID: PMC10153225 DOI: 10.1101/2023.04.18.537404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Each year, hundreds of millions of people are infected with arboviruses such as dengue, yellow fever, chikungunya, and Zika, which are all primarily spread by the notorious mosquito Aedes aegypti. Traditional control measures have proven insufficient, necessitating innovations. In response, here we generate a next generation CRISPR-based precision-guided sterile insect technique (pgSIT) for Aedes aegypti that disrupts genes essential for sex determination and fertility, producing predominantly sterile males that can be deployed at any life stage. Using mathematical models and empirical testing, we demonstrate that released pgSIT males can effectively compete with, suppress, and eliminate caged mosquito populations. This versatile species-specific platform has the potential for field deployment to effectively control wild populations of disease vectors.
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Affiliation(s)
- Ming Li
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Nikolay P. Kandul
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ruichen Sun
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ting Yang
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Elena D. Benetta
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Daniel J. Brogan
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Igor Antoshechkin
- Division of Biology and Biological Engineering (BBE), California Institute of Technology, Pasadena, CA 91125, USA
| | - Héctor M. Sánchez C.
- Divisions of Epidemiology & Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Yinpeng Zhan
- Department of Molecular, Cellular, and Developmental Biology and the Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Nicolas A. DeBeaubien
- Department of Molecular, Cellular, and Developmental Biology and the Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - YuMin M. Loh
- Graduate School of Science, Nagoya University, Nagoya, Aichi, Japan
| | - Matthew P. Su
- Graduate School of Science, Nagoya University, Nagoya, Aichi, Japan
- Institute for Advanced Research, Nagoya University, Nagoya, Aichi, Japan
| | - Craig Montell
- Department of Molecular, Cellular, and Developmental Biology and the Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - John M. Marshall
- Divisions of Epidemiology & Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA
- Innovative Genomics Institute, Berkeley, CA 94720, USA
| | - Omar S. Akbari
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093, USA
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Mondal A, C. HMS, Marshall JM. MGDrivE 3: A decoupled vector-human framework for epidemiological simulation of mosquito genetic control tools and their surveillance. bioRxiv 2023:2023.09.09.556958. [PMID: 37745458 PMCID: PMC10515759 DOI: 10.1101/2023.09.09.556958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
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.
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Affiliation(s)
- Agastya Mondal
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Héctor M. Sánchez C.
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - John M. Marshall
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA
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Smidler AL, Apte RA, Pai JJ, Chow ML, Chen S, Mondal A, Sánchez C. HM, Antoshechkin I, Marshall JM, Akbari OS. Eliminating Malaria Vectors with Precision Guided Sterile Males. bioRxiv 2023:2023.07.20.549947. [PMID: 37503146 PMCID: PMC10370176 DOI: 10.1101/2023.07.20.549947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
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.
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Affiliation(s)
- Andrea L. Smidler
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093
| | - Reema A. Apte
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093
| | - James J. Pai
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093
| | - Martha L. Chow
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093
| | - Sanle Chen
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093
| | - Agastya Mondal
- Divisions of Epidemiology & Biostatistics, School of Public Health, University of California, Berkeley, CA 94720, USA
| | - Héctor M. Sánchez C.
- Divisions of Epidemiology & Biostatistics, School of Public Health, University of California, Berkeley, CA 94720, USA
| | - Igor Antoshechkin
- Division of Biology and Biological Engineering (BBE), California Institute of Technology, Pasadena, CA91125, USA
| | - John M. Marshall
- Divisions of Epidemiology & Biostatistics, School of Public Health, University of California, Berkeley, CA 94720, USA
- Innovative Genomics Institute, University of California, Berkeley, CA 94720, USA
| | - Omar S. Akbari
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093
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Carballar-Lejarazú R, Dong Y, Pham TB, Tushar T, Corder RM, Mondal A, Sánchez C. 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. Proc Natl Acad Sci U S A 2023; 120:e2221118120. [PMID: 37428915 PMCID: PMC10629562 DOI: 10.1073/pnas.2221118120] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/05/2023] [Indexed: 07/12/2023] Open
Abstract
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.
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Affiliation(s)
| | - Yuemei Dong
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Malaria Research Institute, Johns Hopkins University, Baltimore, MD21205
| | - Thai Binh Pham
- Department of Microbiology & Molecular Genetics, University of California, Irvine, CA92697-4025
| | - Taylor Tushar
- Department of Microbiology & Molecular Genetics, University of California, Irvine, CA92697-4025
| | - Rodrigo M. Corder
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA94720
| | - Agastya Mondal
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA94720
| | - Héctor M. Sánchez C.
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA94720
| | - Hsu-Feng Lee
- Department of Microbiology & Molecular Genetics, University of California, Irvine, CA92697-4025
| | - John M. Marshall
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA94720
| | - George Dimopoulos
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Malaria Research Institute, Johns Hopkins University, Baltimore, MD21205
| | - Anthony A. James
- Department of Microbiology & Molecular Genetics, University of California, Irvine, CA92697-4025
- Department of Molecular Biology & Biochemistry, University of California, Irvine, CA92697-3900
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Smidler AL, Pai JJ, Apte RA, Sánchez C. HM, Corder RM, Jeffrey Gutiérrez E, Thakre N, Antoshechkin I, Marshall JM, Akbari OS. A confinable female-lethal population suppression system in the malaria vector, Anopheles gambiae. Sci Adv 2023; 9:eade8903. [PMID: 37406109 PMCID: PMC10321730 DOI: 10.1126/sciadv.ade8903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 06/01/2023] [Indexed: 07/07/2023]
Abstract
Malaria is among the world's deadliest diseases, predominantly affecting Sub-Saharan Africa and killing over half a million people annually. Controlling the principal vector, the mosquito Anopheles gambiae, as well as other anophelines, is among the most effective methods to control disease spread. Here, we develop a genetic population suppression system termed Ifegenia (inherited female elimination by genetically encoded nucleases to interrupt alleles) in this deadly vector. In this bicomponent CRISPR-based approach, we disrupt a female-essential gene, femaleless (fle), demonstrating complete genetic sexing via heritable daughter gynecide. Moreover, we demonstrate that Ifegenia males remain reproductively viable and can load both fle mutations and CRISPR machinery to induce fle mutations in subsequent generations, resulting in sustained population suppression. Through modeling, we demonstrate that iterative releases of nonbiting Ifegenia males can act as an effective, confinable, controllable, and safe population suppression and elimination system.
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Affiliation(s)
- Andrea L. Smidler
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - James J. Pai
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Reema A. Apte
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Héctor M. Sánchez C.
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA 94720, USA
| | - Rodrigo M. Corder
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA 94720, USA
| | - Eileen Jeffrey Gutiérrez
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA 94720, USA
- Oxitec Ltd., Abingdon, OX14 4RQ, UK
| | - Neha Thakre
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Igor Antoshechkin
- Division of Biology and Biological Engineering (BBE), California Institute of Technology, Pasadena, CA 91125, USA
| | - John M. Marshall
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA 94720, USA
- Innovative Genomics Institute, University of California, Berkeley, CA 94720, USA
| | - Omar S. Akbari
- School of Biological Sciences, Department of Cell and Developmental Biology, University of California San Diego, La Jolla, CA 92093, USA
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Wu SL, Bennett JB, Sánchez C. HM, Dolgert AJ, León TM, Marshall JM. MGDrivE 2: A simulation framework for gene drive systems incorporating seasonality and epidemiological dynamics. PLoS Comput Biol 2021; 17:e1009030. [PMID: 34019537 PMCID: PMC8186770 DOI: 10.1371/journal.pcbi.1009030] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 06/08/2021] [Accepted: 05/02/2021] [Indexed: 12/30/2022] Open
Abstract
Interest in gene drive technology has continued to grow as promising new drive systems have been developed in the lab and discussions are moving towards implementing field trials. The prospect of field trials requires models that incorporate a significant degree of ecological detail, including parameters that change over time in response to environmental data such as temperature and rainfall, leading to seasonal patterns in mosquito population density. Epidemiological outcomes are also of growing importance, as: i) the suitability of a gene drive construct for release will depend on its expected impact on disease transmission, and ii) initial field trials are expected to have a measured entomological outcome and a modeled epidemiological outcome. We present MGDrivE 2 (Mosquito Gene Drive Explorer 2): a significant development from the MGDrivE 1 simulation framework that investigates the population dynamics of a variety of gene drive architectures and their spread through spatially-explicit mosquito populations. Key strengths and fundamental improvements of the MGDrivE 2 framework are: i) the ability of parameters to vary with time and induce seasonal population dynamics, ii) an epidemiological module accommodating reciprocal pathogen transmission between humans and mosquitoes, and iii) an implementation framework based on stochastic Petri nets that enables efficient model formulation and flexible implementation. Example MGDrivE 2 simulations are presented to demonstrate the application of the framework to a CRISPR-based split gene drive system intended to drive a disease-refractory gene into a population in a confinable and reversible manner, incorporating time-varying temperature and rainfall data. The simulations also evaluate impact on human disease incidence and prevalence. Further documentation and use examples are provided in vignettes at the project’s CRAN repository. MGDrivE 2 is freely available as an open-source R package on CRAN (https://CRAN.R-project.org/package=MGDrivE2). We intend the package to provide a flexible tool capable of modeling gene drive constructs as they move closer to field application and to infer their expected impact on disease transmission. Malaria, dengue and other mosquito-borne diseases continue to pose a major global health burden through much of the world. Currently available tools, such as insecticides and antimalarial drugs, are not expected to be sufficient to eliminate these diseases from highly-endemic areas, hence there is interest in novel strategies including genetics-based approaches. In recent years, the advent of CRISPR-based gene-editing has greatly expanded the range of genetic control tools available, and MGDrivE 1 (Mosquito Gene Drive Explorer 1) was proposed to simulate the dynamics of these systems through spatially-structured mosquito populations. As the technology has advanced and potential field trials are being discussed, models are now needed that incorporate additional details, such as life history parameters that respond to daily and seasonal environmental fluctuations, and transmission of pathogens between mosquito and vertebrate hosts. Here, we present MGDrivE 2, a gene drive simulation framework that significantly improves upon MGDrivE 1 by addressing these modeling needs. MGDrivE 2 has also been reformulated as a stochastic Petri net, enabling model specification to be decoupled from simulation, making it easier to adapt the model for application to other insect and mammalian species.
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Affiliation(s)
- Sean L. Wu
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America
- * E-mail: (SLW); (JMM)
| | - Jared B. Bennett
- Biophysics Graduate Group, Division of Biological Sciences, College of Letters and Science, University of California, Berkeley, California, United States of America
| | - Héctor M. Sánchez C.
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America
| | - Andrew J. Dolgert
- Institute for Health Metrics and Evaluation, Seattle, Washington, United States of America
| | - Tomás M. León
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America
| | - John M. Marshall
- Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America
- Innovative Genomics Institute, University of California, Berkeley, California, United States of America
- * E-mail: (SLW); (JMM)
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Sánchez C. HM, Wu SL, Bennett JB, Marshall JM. MGD
riv
E: A modular simulation framework for the spread of gene drives through spatially explicit mosquito populations. Methods Ecol Evol 2019. [DOI: 10.1111/2041-210x.13318] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Héctor M. Sánchez C.
- Division of Epidemiology and Biostatistics School of Public Health University of California Berkeley CA USA
| | - Sean L. Wu
- Division of Epidemiology and Biostatistics School of Public Health University of California Berkeley CA USA
| | - Jared B. Bennett
- Biophysics Graduate Group Division of Biological Sciences College of Letters and Science University of California Berkeley CA USA
| | - John M. Marshall
- Division of Epidemiology and Biostatistics School of Public Health University of California Berkeley CA USA
- Innovative Genomics Institute Berkeley CA USA
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