A Bayesian Monte Carlo approach for predicting the spread of infectious diseases.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2019
2019
Historique:
received:
29
04
2019
accepted:
13
11
2019
entrez:
19
12
2019
pubmed:
19
12
2019
medline:
2
4
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31851680
doi: 10.1371/journal.pone.0225838
pii: PONE-D-19-12253
pmc: PMC6919583
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0225838Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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