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_02_infectious_disease_models.qmd
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_02_infectious_disease_models.qmd
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## What are infectious disease models?
- [*Models*]{style="color:tomato;"} generally refer to conceptual representations of an object or system.
- [*Mathematical models*]{style="color:tomato;"} use mathematics to describe the system. For example, the famous $E = mc^2$ is a mathematical model that describes the relationship between mass and energy.
- [*Infectious disease models*]{style="color:tomato;"} use mathematics/statistics to represent dynamics/spread of infectious diseases.
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- Mathematical models can be used to link the biological process of disease transmission and the emergent dynamics of infection at the population level.
- Models require making some assumptions and abstractions.
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::: columns
::: {.column width="60%"}
- By definition, ["all models are wrong, but some are useful"]{style="color:tomato"} [@Box1979].
- Good enough models are those that capture the [essential features]{style="color:tomato"} of the system being studied.
:::
::: {.column width="40%"}
:::
:::
------------------------------------------------------------------------
- What makes models "wrong" by definition?:
- Simplifications of reality; not capturing all the complexities of the system being studied.
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## Factors that influence model formulation/choice
- [Accuracy]{style="color:tomato"}: how well does the model to reproduce observed data and predict future outcomes?
- [Transparency]{style="color:tomato"}: is it easy to understand and interpret the model and its outputs? (This is affected by the model's complexity)
- [Flexibility]{style="color:tomato"}: the ability of the model to be adapted to different scenarios.
::: notes
These will be touched on in the scenario modelling lectures.
:::
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## What are models used for?
- Generally, models can be used to predict and understand/explain the dynamics of infectious diseases.
::: {.callout-caution collapse="true" icon="false"}
### Discussion
How are these two uses impacted by accuracy, transparency, and flexibility?
:::
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### Prediction of the future course
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- Must be [accurate]{style="color:tomato;"}.
- "But the estimate proved to be off. Way, way off. Like, 65 times worse than what ended up happening."
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{fig-cap="[CDC’s top modeler courts controversy with disease estimate](https://apnews.com/domestic-news-domestic-news-fbb4fc8921d54201a1c5ca91e5b601f5)"}
:::
:::
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<!-- \< Insert examples of models that have made accurate predictions of the future course of an outbreak \> -->
<!-- - See <Justin Lessler, Derek A. T. Cummings, Mechanistic Models of Infectious Disease and Their Impact on Public Health, American Journal of Epidemiology, Volume 183, Issue 5, 1 March 2016, Pages 415–422, https://doi.org/10.1093/aje/kww021> -->
<!-- ------------------------------------------------------------------------ -->
### Understanding or explaining disease dynamics
- Models can be used to understand how a disease spreads and how its spread can be controlled.
- The insights gained from models can be used to:
- inform public health policy and interventions.
- design interventions to control the spread of the disease, for example, randomised controlled trials.
- collect new data.
- build predictive models.
<!-- \< Insert examples of models that explain the spread and dynamics of infectious diseases \> -->
<!-- - See <Justin Lessler, Derek A. T. Cummings, Mechanistic Models of Infectious Disease and Their Impact on Public Health, American Journal of Epidemiology, Volume 183, Issue 5, 1 March 2016, Pages 415–422, https://doi.org/10.1093/aje/kww021> -->
<!-- ------------------------------------------------------------------------ -->
<!-- \< Insert examples of models that evaluate the impact of interventions and determine the next course of action \> -->
<!-- ------------------------------------------------------------------------ -->
---
## Limitations of infectious disease models
- Host behaviour is often difficult to predict.
- The pathogen often has unknown characteristics or known characteristics that are difficult to model.
- Data is often not available or is of poor quality.
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## Summary
- Models:
- [Simplifications of reality]{style="color:tomato"} and do not capture all the [complexities]{style="color:tomato"} of the system being studied.
- Only as good as the [data]{style="color:tomato"} used to parameterize them.
- Sensitive to the [assumptions]{style="color:tomato"} made during their formulation.
- [Computationally expensive]{style="color:tomato"} and require a lot of data to run.
- [Difficult to interpret]{style="color:tomato"} and communicate to non-experts.