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Description

Biological motivation

• What biological question is being addressed
• Why it matters for biology, ecology, medicine or evolution

Formulation of the model

• Assumptions underlying the model
• Variables and model parameters
• Model flow diagram (system dynamics)
• Equations (ODE, PDE, stochastic models, agent-based models, ...)
• Justification why this formulation captures the biology (or is more realistic in such a context)

Analytical or computational methods

• Analysis (equilibria, stability, ...)
• Numerical simulations (with description of schemes, parameter choices/estimation method, software,...)

Biological interpretation of the mathematics

• What does stability means in the biological context
• What insights does the model produce that biology alone cannot

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Description

Validation/connection to empirical data (when applicable)

• Comparison of the model output to experimental or observational data
• Parameter estimation, sensitivity analysis
• Discussion of realism vs abstraction¹

Discussion of model limitations

• What biological elements were omitted
• Simplifying assumptions (mathematical assumptions/hypotheses)

Conclusion

• Main biological and mathematical insights
• How the model advances understanding
• Possible extension or future research directions

Reproducibility

• Parameter tables, description, source
• Model flowchart
• Source code
• Supplementary material

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Description

Motivation/Problem Definition

• What biological problem is being addressed or solved?
• Why is this problem important or interesting?
• What gap/novelty in current knowledge/literature does this work fill?
• Who is the target audience (biologists, mathematicians, interdisciplinary readers, health decision and policy makers)?
• Is the research question of interest to health policy and decision makers and have they been engaged?

Biological Foundations

• What are the essential biological mechanisms that must be represented?
• Are the biological assumptions accurate and supported by data, references, or experts opinion?
• Are biological complexities included or intentionally ignored clearly identified?

Model Formulation

• What type of mathematical model is most appropriate and why (ODEs, PDEs, stochastic, agent-based, network, etc.)?
• What are the variables, parameters, and assumptions?
• How do biological mechanisms map to mathematical terms? (e.g., growth → logistic, ...)
• Are simplifying assumptions justified?

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Description

Data & Parameterization

• Are real or empirical data available? If not, how are parameter choices justified?
• Which parameters are estimated, measured, or fit?
• Are the datasets (to be) used field or synthetic data?
• How sensitive are results to parameter uncertainty?
• Will the model be calibrated using observed data, or will its predictive performance be assessed through validation?

Mathematical analysis

• What are the central research questions being investigated, particularly concerning the system's dynamics (steady states, long-term persistence, or responses to perturbations, ...)?
• Are results reproducible and transparent?
• Does the manuscript (analytical methods, scientific rigor) meet the quality standards and expectations of the target journal?

Computation/Simulations

• What numerical methods or algorithms will be used?
• Are simulations stable, convergent, realistic, ...?
• Have parameter sweeps² or sensitivity analyses been performed?
• Can results be reproduced easily by others (code availability)?

Biological Interpretation

• What are the main biological conclusions?
• Does the model provide new (biological/mathematical) insight or merely restate known results?
• Are predictions realistic or biologically feasible?

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Description

Comparison to Existing Work

• How does the proposed model differ from previous ones?
• What is the novel contribution? (new mechanism, new math, new prediction, new biology, ...)
• Have relevant biological and mathematical literature been cited?

Paper Structure & Clarity

• Is the biological background accessible to mathematicians?
• Is the mathematics accessible to biologists?
• Do figures, diagrams, and simulations clearly support the arguments?

Ethics, Transparency & Reproducibility

• Are there ethical issues (primary ethical considerations and risks associated with collecting, storing, and using client-identifiable data?
• Are all code, data, and documentation available?
• Have model limitations and caveats³ been clearly stated?

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Description

Title

A concise description of the study

Abstract

A brief summary of the purpose, methods, key results and main conclusion (Background/Objective, Methods, Results, and Conclusion)

Introduction

Explain the problem being studied (and why it matters or motivation), background information, and the research question or hypothesis, and the novelty of the study

Methods

Describes how the study was conducted so that others could replicate it

Results

Presents the data and findings without interpretation

Discussion

Interprets the results, explains their significance, compares them to other work, and notes limitations

Conclusion

Summarizes the main takeaways, implications, and potential future directions of the research

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NB: Some journals merge Results and Discussion or Discussion and Conclusion while others keep them separate

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