4.7 Article

A Practical Introduction to Mechanistic Modeling of Disease Transmission in Veterinary Science

期刊

FRONTIERS IN VETERINARY SCIENCE
卷 7, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fvets.2020.546651

关键词

simulation model; transmission model; disease dynamics; mechanistic model; disease model

资金

  1. University of Sydney
  2. University of Copenhagen

向作者/读者索取更多资源

Computer-based disease spread models are commonly used in veterinary science to predict disease impacts, plan control strategies, and provide insights into disease causation. Individual-based models are a specific type of disease spread models, and can be built using practical methods and code examples. Important steps in building these models include verification, validation, and convergence analysis, as well as sensitivity analysis to assess the impact of parameter uncertainty. Recent developments in disease spread models are also discussed.
Computer-based disease spread models are frequently used in veterinary science to simulate disease spread. They are used to predict the impacts of the disease, plan and assess surveillance, or control strategies, and provide insights about disease causation by comparing model outputs with real life data. There are many types of disease spread models, and here we present and describe the implementation of a particular type: individual-based models. Our aim is to provide a practical introduction to building individual-based disease spread models. We also introduce code examples with the goal to make these techniques more accessible to those who are new to the field. We describe the important steps in building such models before, during and after the programming stage, including model verification (to ensure that the model does what was intended), validation (to investigate whether the model results reflect the modeled system), and convergence analysis (to ensure models of endemic diseases are stable before outputs are collected). We also describe how sensitivity analysis can be used to assess the potential impact of uncertainty about model parameters. Finally, we provide an overview of some interesting recent developments in the field of disease spread models.

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