# Excess deaths due to COVID-19#

```
import pandas as pd
import causalpy as cp
```

```
%load_ext autoreload
%autoreload 2
%config InlineBackend.figure_format = 'retina'
seed = 42
```

## Load data#

```
df = (
cp.load_data("covid")
.assign(date=lambda x: pd.to_datetime(x["date"]))
.set_index("date")
)
treatment_time = pd.to_datetime("2020-01-01")
df.head()
```

temp | deaths | year | month | t | pre | |
---|---|---|---|---|---|---|

date | ||||||

2006-01-01 | 3.8 | 49124 | 2006 | 1 | 0 | True |

2006-02-01 | 3.4 | 42664 | 2006 | 2 | 1 | True |

2006-03-01 | 3.9 | 49207 | 2006 | 3 | 2 | True |

2006-04-01 | 7.4 | 40645 | 2006 | 4 | 3 | True |

2006-05-01 | 10.7 | 42425 | 2006 | 5 | 4 | True |

The columns are:

`date`

+`year`

: self explanatory`month`

: month, numerically encoded. Needs to be treated as a categorical variable`temp`

: average UK temperature (Celsius)`t`

: time`pre`

: boolean flag indicating pre or post intervention

## Run the analysis#

In this example we are going to standardize the data. So we have to be careful in how we interpret the inferred regression coefficients, and the posterior predictions will be in this standardized space.

Note

The `random_seed`

keyword argument for the PyMC sampler is not necessary. We use it here so that the results are reproducible.

```
result = cp.InterruptedTimeSeries(
df,
treatment_time,
formula="standardize(deaths) ~ 0 + standardize(t) + C(month) + standardize(temp)",
model=cp.pymc_models.LinearRegression(sample_kwargs={"random_seed": seed}),
)
```

```
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [beta, sigma]
```

```
Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.
Sampling: [beta, sigma, y_hat]
Sampling: [y_hat]
Sampling: [y_hat]
Sampling: [y_hat]
Sampling: [y_hat]
```

```
result.summary()
```

```
==================================Pre-Post Fit==================================
Formula: standardize(deaths) ~ 0 + standardize(t) + C(month) + standardize(temp)
Model coefficients:
C(month)[1] 1.6, 94% HDI [1.1, 2]
C(month)[2] -0.2, 94% HDI [-0.64, 0.24]
C(month)[3] 0.27, 94% HDI [-0.11, 0.64]
C(month)[4] -0.039, 94% HDI [-0.34, 0.25]
C(month)[5] -0.15, 94% HDI [-0.45, 0.14]
C(month)[6] -0.21, 94% HDI [-0.62, 0.21]
C(month)[7] -0.023, 94% HDI [-0.53, 0.5]
C(month)[8] -0.41, 94% HDI [-0.88, 0.076]
C(month)[9] -0.44, 94% HDI [-0.82, -0.064]
C(month)[10] -0.059, 94% HDI [-0.34, 0.22]
C(month)[11] -0.36, 94% HDI [-0.69, -0.029]
C(month)[12] 0.074, 94% HDI [-0.37, 0.49]
standardize(t) 0.23, 94% HDI [0.15, 0.31]
standardize(temp) -0.45, 94% HDI [-0.75, -0.16]
sigma 0.55, 94% HDI [0.5, 0.62]
```