The BSTI model is a Bayesian spatio-temporal interaction model, a probabilistic generalized linear model, that predicts aggregated case counts within spatial regions (counties) and time intervals (calendar weeks) using a history of reported cases, temporal features (seasonality and trend) and region-specific as well as demographic information.

The model is implemented in Python and relies on the PyMC3 and Theano packages for computationally efficient sampling.

Key features of the model:

• a single probabilistic model learns to predict the number of weekly case counts for three different diseases (campylobacteriosis, rotaviralenteritis and Lyme borreliosis) at the county level one week ahead of time
• a Bayesian Monte Carlo regression approach provides an estimate of the full probability distribution over inferred parameters as well as model predictions.
• the model learns an interpretable spatio-temporal kernel that captures typical interactions between infection cases of the tested diseases.

It is recommended to install a local virtual environment for python3, a requirements.txt is provided.

Run virtualenv --no-site-packages --python=python3 .venv followed by pip install requirements.txt to get going.

The experiment is then run by first sampling interactions effects and then the posterior, currently implemented via a SGE scripts ./src/gridjob_sample_ia.sge and ./src/gridjob_sample_posterior.sge . Adapt to different runtimes as needed.

The data is provided by the Robert Koch Institute, and consists of weekly reports of case counts for three diseases, campylobacteriosis, rotavirus infections and Lyme borreliosis. They are aggregated by county and collected over a time period spanning from the 1st of January 2011 (2013 for borreliosis) to the 31st of December 2017 via the SurvNet surveillance system. Aggregated case counts of diseases with mandatory reporting in Germany is available online.

Covid-19 data is provided by the Robert Koch Institute via the publically accessiable this link. The downloadable CSV table can be found in ./data/raw and is preprocessed to fit the BSTI Model implementation via the notebook ./notebooks/preprocess_covid19_data.ipynb.

Information about the shape of counties within Germany is publicly provided by the German federal agency for cartography and geodesy (Bundesamt für Kartographie und Geodäsie) (© GeoBasis-DE / BKG 2018) under the dl-de/by-2-0 license.