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README.Rmd

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[McMasterPandemic](https://github.com/mac-theobio/McMasterPandemic) was developed to provide forecasts and insights to Canadian public health agencies throughout the COVID-19 pandemic. [Much was learned](https://canmod.github.io/macpan-book/index.html#vision-and-direction) about developing general purpose compartmental modelling software during this experience, but the pressure to deliver regular forecasts made it difficult to focus on the software itself. The goal of this `macpan2` project is to re-imagine `McMasterPandemic`, building it from the ground up with architectural and technological decisions that address the many lessons that we learned from COVID-19 about software.
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Impactful applied public health modelling requires many interdisciplinary steps along the path from epidemiological research teams to operational decision makers. Researchers must quickly tailor a model to an emerging public-health concern, validate and calibrate it to data, work with decision makers to define model outputs useful for stakeholders, configure models to generate those outputs, and package up those insights in an appropriate format for stakeholders. Unlike traditional modelling approaches, `macpan2` tackles this challenge from a software-engineering perspective, which allows us to systematically address bottlenecks along this path to impact in ways that will make future solutions easier to achieve. The goal is to enable researchers to focus on their core strengths and fill knowledge gaps efficiently and effectively.
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Although `macpan2` is designed as a compartmental modelling tool that is agnostic about the underlying computational engine, it currently uses [template model builder](https://github.com/kaskr/adcomp) as the sole engine. Template model builder (TMB) is an `R` modelling package based on a `C++` framework incorporating mature [automatic differentiation](https://cppad.readthedocs.io/latest) and [matrix algebra](http://eigen.tuxfamily.org/index.php?title=Main_Page) libraries.
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The [Public Health Risk Sciences Division](https://github.com/phac-nml-phrsd) at the [Public Health Agency of Canada](https://www.canada.ca/en/public-health.html) uses `macpan2` (for example, [here](https://phac-nml-phrsd.github.io/EPACmodel/)).
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## Documentation

README.md

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up with architectural and technological decisions that address the many
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lessons that we learned from COVID-19 about software.
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Impactful applied public health modelling requires many
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interdisciplinary steps along the path from epidemiological research
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teams to operational decision makers. Researchers must quickly tailor a
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model to an emerging public-health concern, validate and calibrate it to
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data, work with decision makers to define model outputs useful for
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stakeholders, configure models to generate those outputs, and package up
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those insights in an appropriate format for stakeholders. Unlike
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traditional modelling approaches, `macpan2` tackles this challenge from
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a software-engineering perspective, which allows us to systematically
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address bottlenecks along this path to impact in ways that will make
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future solutions easier to achieve. The goal is to enable researchers to
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focus on their core strengths and fill knowledge gaps efficiently and
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effectively.
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Although `macpan2` is designed as a compartmental modelling tool that is
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agnostic about the underlying computational engine, it currently uses
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[template model builder](https://github.com/kaskr/adcomp) as the sole
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engine. Template model builder (TMB) is an `R` modelling package based
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on a `C++` framework incorporating mature [automatic
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differentiation](https://cppad.readthedocs.io/latest) and [matrix
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algebra](http://eigen.tuxfamily.org/index.php?title=Main_Page)
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libraries.
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The [Public Health Risk Sciences
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Division](https://github.com/phac-nml-phrsd) at the [Public Health
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Agency of Canada](https://www.canada.ca/en/public-health.html) uses

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