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## Preface {.unnumbered .unlisted}

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*Last updated:
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❗🛒 Pre-order now: 👉 [Routledge
Website](https://www.routledge.com/Health-Metrics-and-the-Spread-of-Infectious-Diseases-Machine-Learning-Applications-and--Spatial-Modelling-Analysis-with-R/Gazzelloni/p/book/9781032625782/)
👉
[Amazon](https://www.amazon.com/Health-Metrics-Spread-Infectious-Diseases/dp/1032625783/)
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This book will teach you about health metrics such as Disability
Adjusted Life Years (DALYs), Years of Life Lost (YLLs), Years Lived with
Disability (YLDs), and others. It explains how to calculate these
metrics and discusses their components in detail. You will explore the
machine learning framework and learn how to apply the influence of
infectious disease dynamics on these metrics. The book equips you with
all the necessary tools for data collection, analysis, visualisation and
modelling.

Think of it as a toolbox for assessing the health of a country and
comparing it with others. You will also learn how to select the best
tools for predicting health trends using statistics, visualising data,
and working with maps in the R programming language.

Consider this book as your guide to understanding the health status of a
population at both global and country levels, leveraging expertise in
managing various statistical tools.

## Audience and Utility of the Book

This book serves as both a manual and a textbook for introductory
courses in health metrics data analysis. Additionally, it provides
valuable source code for practitioners and data scientists. The book
offers a comprehensive set of tools for analysing various models through
tailored case studies. It focuses on health data, providing an overview
of the burden of diseases to facilitate comparisons between populations'
health status. By combining theoretical insights with practical
applications, the book aims to equip readers with the necessary skills
to conduct health data analyses and make informed decisions.

## Prerequisites

Before delving into the book, it is beneficial for readers to have a
basic understanding of health concepts and terminologies. Familiarity
with fundamental statistical concepts can aid in comprehending the
metrics discussed. Additionally, a grasp of basic epidemiological
principles and awareness of global health challenges will enhance the
reader's engagement. However, it is important to note that if this
knowledge is not already in place, a dedicated section in the book
provides the necessary background.

A basic knowledge of R programming language is required for those
interested in the technical aspects of health data analysis, which cover
a large part of this book. An open mindset and curiosity about the
evolving field of health metrics are key prerequisites, as the book
covers a spectrum from historical perspectives to modern machine
learning applications.

Overall, a multidisciplinary approach, combining aspects of health
sciences, statistics, and technology, will enrich the reader's
experience.

## Acknowledgements

*A big thank you to all my friends and colleagues for their support
throughout this journey. Your encouragement and belief in my work have
been invaluable.*

This book is the result of nearly four years of dedicated work,
research, and continuous learning in the field of data science and
public health. My journey began as a consequence of the COVID-19
outbreak. Witnessing the global impact of this pandemic inspired me to
investigate the spread of infectious diseases and contribute to the
understanding and management of public health crises.

I would also like to acknowledge the invaluable assistance of ChatGPT,
an AI language model developed by OpenAI, which provided essential
inputs, helped refine complex ideas, and offered suggestions that
greatly enhanced the content of this book. The ability to leverage such
advanced technology has significantly contributed to the clarity and
depth of the material presented.

Finally, I would like to express my profound gratitude to the
researchers, data scientists, and public health experts whose work and
insights have been referenced and built upon throughout this book. Your
pioneering efforts and dedication to improving public health have laid
the foundation for the analyses and models discussed herein. Your
contributions have been a source of inspiration and guidance.

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🙌 **Special Acknowledgement**

I also want to extend a heartfelt thank-you to members of the
[DSLC](https://dslc.io/) *Health Metrics Book Club*, whose thoughtful
reflections, questions, and encouragement have contributed to the final
refinement of this book. In particular, I’d like to acknowledge:

-   [Chantel Davies](https://www.linkedin.com/in/chanteldavies)
-   [Robert Willans](https://www.linkedin.com/in/robert-willans-b0b4b3161/)
-   [Retselisitsoe Monyake](https://www.linkedin.com/in/rmonyake/)
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### How to Cite This Book

To cite this book in publications, please use:

> Federica Gazzelloni (2024). *Health Metrics and the Spread of
> Infectious Diseases: Machine Learning Applications and Spatial
> Modelling Analysis with R*. CRC Press, Boca Raton, FL, USA. ISBN: .

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If you're using BibTeX, you can use the following entry:

``` bibtex
@book{gazzelloni2024,
  author = {Federica Gazzelloni},
  title = {Health Metrics and the Spread of Infectious Diseases: Machine Learning Applications and Spatial Modelling Analysis with R},
  year = {2024},
  publisher = {CRC Press},
  address = {Boca Raton, FL, USA},
  isbn = {978-1-032-62578-2}
}
```

Alternatively, [download the citation file
here](https://github.com/Fgazzelloni/hmsidR/blob/main/CITATION).
:::

## About the Author

The author of this book is *Federica Gazzelloni*[^index-1], Actuary and
Statistician by education and training. She is also a
collaborator[^index-2] at the *Institute for Health Metrics and
Evaluation (IHME)*, which inspired this work to serve as a manual
providing formulas and code for working with health metrics. Federica
began focusing on modelling health data, particularly infectious
diseases, following the Covid-19 outbreak, when this deadly disease was
rapidly spreading worldwide. Before that, she briefly worked in
corporate and academic environments, serving as a research practitioner
actuary and teaching mathematics to high school students and computer
science to university students. She is actively involved with the open
source tech community, collaborating with organisations such as Actex
learning, The Carpentries, Bioconductor, and the R Consortium. More
about her ongoing projects here: <https://federicagazzelloni.com/>

[^index-1]: To learn more about the author's ongoing projects, please
    visit here: <https://www.federicagazzelloni.com>

[^index-2]: If you'd like to know more about who are GBD Collaborators
    have a look here:
    <https://www.healthdata.org/research-analysis/gbd/collaborator-network>

## Data Sources

All data used in this book are from the **Institute for Health Metrics
and Evaluation (IHME)**. **GBD Results**. Seattle, WA: IHME, University
of Washington, 2020
([https://vizhub.healthdata.org/gbd-results/](https://urldefense.com/v3/__https:/vizhub.healthdata.org/gbd-results/__;!!K-Hz7m0Vt54!l72IcmA_WDWDWqHLaUzVrt8XbheUn0_vWggtGDbhl4vxiT64nLrmzX32rCVfXnXUO6SO89nOjSnj5g$) -
accessed January 2023), the **World Health Organization (WHO)**,
**Global Health Observatory data repository**
([https://apps.who.int/gho/data/](https://apps.who.int/gho/data/node.main.LIFECOUNTRY?lang=en)),
and the **European Centre for Disease Prevention and Control (ECDC)
(**<https://opendata.ecdc.europa.eu/covid19/>).

In particular, for reproducibility all data used throughout the book is
collected in the `{hmsidwR}` R package, years span from 2000 to 2021,
and both the GBD 2019 and 2021 is used. Careful consideration is to be
done when data is extracted before the next GBD study data is released,
as then all data is updated to consider the estimates of the latest GBD
study. In some parts of the book the data is extracted from the IHME
API, the GHO and Athena WHO API, the code is provided in the book.

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## Code of Conduct

Please note that this book is released with a [Contributor Code of
Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.
:::