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

Autor(en): Stojanovic, Olivera
Leugering, Johannes
Pipa, Gordon 
Ghozzi, Stephane
Ullrich, Alexander
Stichwörter: CRITERIA; DISTRIBUTIONS; MODEL; Multidisciplinary Sciences; Science & Technology - Other Topics
Erscheinungsdatum: 2019
Journal: PLOS ONE
Volumen: 14
Ausgabe: 12
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.
ISSN: 19326203
DOI: 10.1371/journal.pone.0225838

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