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

Sean L. Wu, Jared B. Bennett, Héctor M. Sánchez C., Andrew J. Dolgert, Tomás M. León, John M. Marshall
doi: https://doi.org/10.1101/2020.10.16.343376
Sean L. Wu
1Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
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  • For correspondence: slwu89@berkeley.edu john.marshall@berkeley.edu
Jared B. Bennett
2Biophysics Graduate Group, Division of Biological Sciences, College of Letters and Science, University of California, Berkeley, CA, USA
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Héctor M. Sánchez C.
1Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
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Andrew J. Dolgert
3Institute for Health Metrics and Evaluation, Seattle, WA, USA
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Tomás M. León
1Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
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John M. Marshall
1Divisions of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
4Innovative Genomics Institute, Berkeley, CA, USA
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  • For correspondence: slwu89@berkeley.edu john.marshall@berkeley.edu
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Abstract

  1. 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.

  2. We present MGDrivE 2 (Mosquito Gene Drive Explorer 2): an extension of and 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 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.

  3. Example MGDrivE 2 simulations are presented to demonstrate the application of the framework to a CRISPR-based homing gene drive system intended to drive a disease-refractory gene into a population, incorporating time-varying temperature and rainfall data, and predict impact on human disease incidence and prevalence. Further documentation and use examples are provided in vignettes at the project’s CRAN repository.

  4. MGDrivE 2 is an open-source R package freely available on CRAN. 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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/MarshallLab/MGDrivE/tree/master/MGDrivE2

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 17, 2020.
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MGDrivE 2: A simulation framework for gene drive systems incorporating seasonality and epidemiological dynamics
Sean L. Wu, Jared B. Bennett, Héctor M. Sánchez C., Andrew J. Dolgert, Tomás M. León, John M. Marshall
bioRxiv 2020.10.16.343376; doi: https://doi.org/10.1101/2020.10.16.343376
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MGDrivE 2: A simulation framework for gene drive systems incorporating seasonality and epidemiological dynamics
Sean L. Wu, Jared B. Bennett, Héctor M. Sánchez C., Andrew J. Dolgert, Tomás M. León, John M. Marshall
bioRxiv 2020.10.16.343376; doi: https://doi.org/10.1101/2020.10.16.343376

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