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7: Probability and Risk Analysis

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    Introduction

    The public health applications of epidemiology, the branch of medicine concerned with identifying patterns and potential causes of disease and health in populations, were every day in the news during the Covid-19 pandemic. From contact tracing to reproductive rate of the SARS-Cov-2 virus to numbers of hospital beds and nurses available in ICU units across the country, to the discussions and debates over how the virus is spread, no doubt you have learned much about the critical role epidemiology continues to play.

    Four-panel cartoon showing a doctor and a patient. The doctor states that it doesn't look good for the patient, who has a lot of measurements and variables, because variables are the #1 risk factor for outcomes. When the patient asks if that is just causality, the doctor replies that causality is the leading cause of death, and the patient having any family history "in general" is bad news.
    Figure \(\PageIndex{1}\): “Health data,” https://xkcd.com/2620/

    This chapter is about probability and will introduce you to risk analysis (Fig. \(\PageIndex{1}\)), used to “… characterize the nature and magnitude of risks to human health for various populations…”, a foundational topic in biostatistics and epidemiology. The epiR package will be introduced and code examples provided for descriptive epidemiology and again for statistical inference (Chapter 9).


    This page titled 7: Probability and Risk Analysis is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Michael R Dohm via source content that was edited to the style and standards of the LibreTexts platform.

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