Elderfield et al. 2018 Fungicide Model

What is this?

The package fungicide_model provides an implementation of the models described in Elderfield et al. 2018 (https://apsjournals.apsnet.org/doi/abs/10.1094/PHYTO-08-17-0277-R?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dpubmed). These are ODE models of fungicide resistance evolution in septoria leaf blotch of UK winter wheat and in powdery mildew of French grapevine.

What am I allowed to do with this code?

Feel free to use or modify this code in any way you see fit. If your work leads to a publication of any sort I would ask that you cite the paper introducing this model (linked above).

How do I use it? (A short example)

You can can use the Parameters class to define the parameter values used for the simulation, defaults matching those in the paper are provided for all but the fungicide application strategy (Parameters.strategy) and the doses to use (Parameters.highRiskDose and Parameters.lowRiskDose). For example

from fungicide_model import Parameters, SprayStrategies, Model

# Build a parameters object, specifying which model we intend to use
params = Parameters(Model.WheatSeptoria)

# Choose a fungicide application strategy
params.strategy = SprayStrategies.Mixture

# Choose which doses to apply (NB: should be halved under mixture relative to alternation)
params.highRiskDose = 0.5
params.lowRiskDose = 0.5

Once the parameter values have been chosen, they are fed into a Simulation object which carries out the numerical simulation itself.

from fungicide_model import Simulation

# Provide the chosen parameters to the simulation
sim = Simulation(params)

# Do the calculation
sim.run()

Once the simulation has finished (this may take some time depending on the parameter values chosen) the output can then be accessed directly from the object.

from matplotlib import pyplot as plt

fig, ax = plt.subplots()

# Plot out the amount of healthy tissue over time
ax.plot(
    sim.output.index,
    sim.output["S"],
    color = "#006000"
)

# Print out the summary
print("Selection ratio: {}, effective life: {}, lifetime yield: {}".format(
    sim.selectionRatio,
    sim.seasonsTilCriticalLoss,
    sim.yieldTilCriticalLoss
))

plt.show()

What else can it do?

Investigating the Parameters object will show the range of other parameter values that can be tweaked. The most interesting is the ability to turn on and off different features of the model. This is done by setting the boolean state of the member variables: Parameters.densityDependentInfection, Parameters.seasonality, Parameters.latentPeriod and Parameters.fungicideDecay. Note that changing these members will automatically change the value of the infection rate parameter (Parameters.beta) to match the appropriate model.

Potential improvements in future

This package produced all of the results for the previously mentioned paper and a good portion of my PhD thesis. There are a number of other similar bits of code I've written for related models or extensions of the models included here that will either be added to this package or released separately as and when the publications attached to them are sent for submission.

Given infinite time the following are things I'd like to add or change,

• Update names of members to match those used in the paper (e.g. the seasonality member variable refers to Phenology from the paper)
• Bring in some of the extensions I've been working on apart from the attached paper (e.g. partial resistance)
• Attach the test suite to the distribution