A Bayesian Monte Carlo approach for predicting the spread of infectious diseases

Autoři: Olivera Stojanović aff001;  Johannes Leugering aff001;  Gordon Pipa aff001;  Stéphane Ghozzi aff002;  Alexander Ullrich aff002
Působiště autorů: Department of Neuroinformatics, Institute of Cognitive Science, Osnabrück University, Osnabrück, Germany aff001;  Department of Infectious Diseases, Robert Koch Institute, Berlin, Germany aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225838


In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infectious diseases. Testing the model on a one-week-ahead prediction task for campylobacteriosis and rotavirus infections across Germany, as well as Lyme borreliosis across the federal state of Bavaria, shows that the proposed model performs on-par with the state-of-the-art hhh4 model. However, it provides a full posterior distribution over parameters in addition to model predictions, which aides in the assessment of the model. The employed Bayesian Monte Carlo regression framework is easily extensible and allows for incorporating prior domain knowledge, which makes it suitable for use on limited, yet complex datasets as often encountered in epidemiology.

Klíčová slova:

Epidemiology – Germany – Kernel functions – Lyme disease – Probability distribution – Rotavirus infection – Campylobacteriosis – Borrelia infection


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Článek vyšel v časopise


2019 Číslo 12