Rt equation is inverted in the COVID spread notebook

Description

In the Computing $R_t$ section of the COVID spread notebook, the displayed equation has the earlier four-day total in the numerator and the most recent four-day total in the denominator:

R_t = (I_{t-7} + I_{t-6} + I_{t-5} + I_{t-4})
      / (I_{t-3} + I_{t-2} + I_{t-1} + I_t)

This is inverted relative to the interpretation that values above 1 indicate growth.

The code immediately below the equation computes the ratio in the other direction:

df["Rt"] = df["ninfected"].rolling(8).apply(
    lambda x: x[4:].sum() / x[:4].sum()
)

Within the chronological eight-day window, x[:4] is the earlier period and x[4:] is the recent period, so the implementation is consistent with:

R_t = (I_{t-3} + I_{t-2} + I_{t-1} + I_t)
      / (I_{t-7} + I_{t-6} + I_{t-5} + I_{t-4})

For example, if daily infections increase from 10 in each of the earlier four days to 20 in each of the recent four days, the displayed equation gives 0.5, while the code gives 2.

Suggested change

Swap the numerator and denominator in the Markdown equation so that it matches the code and the surrounding explanation.

File: 2-Working-With-Data/07-python/notebook-covidspread.ipynb

Open 2-Working-With-Data/07-python/notebook-covidspread.ipynb and find the Computing $R_t$ section. Compare the Markdown equation with the following rolling Python calculation, then swap the equation's numerator and denominator so it matches the code and the explanation that values above 1 indicate growth.