September problem 5: 18 month Evaluation, Scenario 1, Q1-3

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Scenario 1: Modeling SARS-CoV-2 infections in White-tailed Deer

Estimated % of time: Baseline: 20%; Workbench 15%

To date, COVID-19 modeling efforts have focused almost exclusively within human populations. There is now compelling evidence that SARS-CoV-2, the virus that causes COVID-19, spreads from humans to white-tailed deer (**), as well as between white-tailed deer and possibly from white-tailed deer back into human populations. There is evidence that infected white-tailed deer may serve as a reservoir for nearly extinct variants of concern (e.g., Delta variant), some of which are associated with greater clinical severity in human populations. Additional mutations to these nearly extinct variants of concern within the wildlife reservoir could make them more transmissible in addition to causing increased severity of infection.

Decision makers are interested in (1) better characterizing infection dynamics within the white-tailed deer (WTD) population to understand risks such as re-importation of nearly extinct variants of concern back into human populations; (2) understanding the potential efficacy of interventions targeted towards decreasing the deer population infected with SARS-CoV-2, in order to decrease future likelihood of transmission from deer population back to human population (which could potentially drive new Covid waves in the future, were this to happen at any meaningful level). To do this, they have asked you to find a model of Covid in the WTD population, that already can support, or can be modified to support, these types of interventions.

You have identified three compartmental models of SARS-CoV-2 transmission within the WTD population that are relevant to your task. Two are published as pre-prints, and one is a very recent publication. Given the novelty of these models, you want to better understand how they are similar and different in terms of their assumptions, strengths, limitations, and fit-for-purpose.
• Model A: https://doi.org/10.1155/2024/7589509
• Model B: https://doi.org/10.48550/arXiv.2401.10057
• Model C: https://doi.org/10.1101/2023.08.30.555493

For Q1-5, use only the above 3 publications as source material.

Question 1: Model extraction

Begin by extracting the three models, available at the links above. For each model, note the time to extract the model and get it into an executable state that can run a simple test simulation and get sensible results. You may choose the initial conditions and parameter values for the test simulation; they don’t need to be realistic, but the results do need to make sense given the values you choose. For workbench modelers, model extraction time may include human-in-the-loop curation, and for baseline modelers, this time may include debugging code. For each model, provide simulation results from your test simulation.

For baseline modelers, model extraction is defined as the following:

• Writing out or capturing the equations describing the model, or drawing out the model structure. (You can write them out by hand, but be sure to capture the image for the work product).
• Writing out definitions of all variables and parameters, with units
• Finding default values for parameters, initial values for variables, and whatever else is needed to initiate/run the model
• If the model is not already installed on the VM, find and install code to run it, or produce your own code to run the model. The code should be deposited in your work product SharePoint folder.

For the workbench modelers, model extraction is defined as the following:

• Ingesting the model from source paper or code, into the workbench
• Capturing the set of equations describing the model in the workbench
• Gathering definitions of all variables and parameters, with units
  • Gathering default values for parameters, initial values for variables, and whatever else is needed to initiate/run the model
  • Ensuring the model is executable in the workbench

Question 2: Model Comparison

Do a model comparison based on key differences in assumptions, strengths, limitations, and distinguishing characteristics. Based on this information, rank each model in terms of their relevance and fit-for-purpose in this context.

Model	Distinguishing characteristics	Assumptions	Strengths	Limitations	Rank fit-for-purpose (1 = most suitable; 3 = least suitable), with reasoning

Question 3: Structural Model Comparison

Now perform structural model comparison between each pair of models. By structural comparison, we seek to understand how compartments and transition pathways overlap or diverge between models. Feel free to create diagrams or use equations in your response.