MGSurvE

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

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What/How/Why?

What is it?

A Python package that optimizes the positioning of traps to minimize the time it takes us to detect transgenes in heterogeneous landscapes.

How does it work?

It optimizes Markov's fundamental matrix to minimize the time it takes for "random walkers" (mosquitos) to fall into an absorbing state (traps).

Why does it exist?

To aid in the design of surveillance campaigns for gene-drives and insecticide resitance in mosquito landscapes.

Demos

Workflow

São Tomé

Yorkeys Knob

Future and Resources

OSM Data and Routing Optimization

Installation and Development

System installation


							pip install MGSurvE

Using our docker image


							docker pull chipdelmal/mgsurve:latest
							docker run -it chipdelmal/mgsurve bash

GitHub repo with links to documentation

<img src="./images/banner.png" style="height:150px; border-radius: 10px;">
							<p style="font-size:12.5px">Materials available at: <a style="font-size:12.5px" href="https://github.com/Chipdelmal/MGSurvE_Webinar2023">https://github.com/Chipdelmal/MGSurvE_Webinar2023</a></p>
							<br>

1. [Intro to Python (for R programmers)](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/fundamentals/python.ipynb)
							2. Fundamentals
								1. [Quickstart](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/fundamentals/quickstart.ipynb)
								2. [Workflow Breakdown](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/fundamentals/breakdown.ipynb)
							3. Landscape
								1. [Basic Landscape Elements](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/landscape/simpleLandscape.ipynb)
								2. [Heterogeneous Point-Types](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/landscape/complexLandscape.ipynb)
								3. [Custom Migration Matrix](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/landscape/customLandscape.ipynb)
							4. Optimization
								1. [Genetic Algorithm Primer](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/optimization/ga.ipynb)
								2. [Discrete Optimization (GA)](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/optimization/discrete.ipynb)
								3. [Continuous Optimization (GA)](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/optimization/continuous.ipynb)
							5. Analysis
								1. [Visualization Tools](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/analysis/visualization.ipynb)
								2. [GA Comparison](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/analysis/comparison.ipynb)
							6. More Information
								1. [Mathematical Formulation](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/more/math.ipynb)
								2. [OpenStreetMap Compatibility](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/more/osm.ipynb)
								3. [Particle-Swarm Optimization (PSO)](https://github.com/Chipdelmal/MGSurvE_Webinar2023/blob/main/demos/more/pso.ipynb)
								4. [Preprint on bioRxiv](https://www.biorxiv.org/content/10.1101/2023.06.26.546301v1)

Team and Questions

Héctor M. Sánchez C. — David L. Smith — John M. Marshall

Special Thanks to: Tomás León, Jared Bennett, Lillian Weng, Xingli Yu, Elijah Bartolome, Ayden Salazar, Topiltzin Hernández Mares, Joanna Yoo