  During 2020, the infection rate of COVID-19 has been investigated by many
scholars from different research fields. In this context, reliable and
interpretable forecasts of disease incidents are a vital tool for policymakers
to manage healthcare resources. Several experts have called for the necessity
to account for human mobility to explain the spread of COVID-19. Existing
approaches are often applying standard models of the respective research field.
This habit, however, often comes along with certain restrictions. For instance,
most statistical or epidemiological models cannot directly incorporate
unstructured data sources, including relational data that may encode human
mobility. In contrast, machine learning approaches may yield better predictions
by exploiting these data structures, yet lack intuitive interpretability as
they are often categorized as black-box models. We propose a trade-off between
both research directions and present a multimodal learning approach that
combines the advantages of statistical regression and machine learning models
for predicting local COVID-19 cases in Germany. This novel approach enables the
use of a richer collection of data types, including mobility flows and
colocation probabilities, and yields the lowest MSE scores throughout our
observational period in our benchmark study. The results corroborate the
necessity of including mobility data and showcase the flexibility and
interpretability of our approach.
