Hi all, This is just a quick blog note that the PyFriday Python Tutorial will be delayed by a week (alongside the ThuRsday R Article) as I am writing up

The Susceptible-Exposed-Infected-Recovered (SEIR) model is a natural extension of the SIR Model, accounting for a fourth category of disease state, Exposure. For this PyFriday Tutorial, we’ll cover how to not

The Susceptible-Exposed-Infected-Recovered (SEIR) model is a natural extension of the SIR Model, accounting for a fourth category of disease state, Exposure. For this ThuRsday Tutorial, we’ll cover how to not

The Susceptible-Infected-Recovered (SIR) model is a fundamental concept in epidemiology, offering insights into how diseases spread and recede in populations over time through a relatively simply set of functions. For

The Susceptible-Infected-Recovered (SIR) model is a fundamental concept in epidemiology, offering insights into how diseases spread and recede in populations over time through a relatively simply set of functions. For

Introduction to Relative Risk Relative Risk (RR) is one of the most fundamental measures in public health, offering insights into the strength of association between an exposure (like smoking) and

Introduction to Relative Risk Relative Risk (RR) is one of the most fundamental measures in public health, offering insights into the strength of association between an exposure (like smoking) and

T-tests are a fundamental statistical tool used in various fields, including public health, to compare the means of two groups. Essentially, a T-test helps determine whether the observed differences in

T-tests are a fundamental statistical tool used in various fields, including public health, to compare the means of two groups. Essentially, a T-test helps determine whether the observed differences in

Introduction Odds Ratio (OR) calculations are a cornerstone in public health research, providing insights into the strength of association between an exposure and an outcome. In this tutorial, we’ll explore