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

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...

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Bibliographic Details
Published in:PloS one 2019-12, Vol.14 (12), p.e0225838-e0225838
Main Authors: Stojanović, Olivera, Leugering, Johannes, Pipa, Gordon, Ghozzi, Stéphane, Ullrich, Alexander
Format: Article
Language:English
Subjects:
Analysis
Bayes Theorem
Bayesian analysis
Borrelia infections
Borreliosis
Campylobacter Infections - epidemiology
Campylobacteriosis
Communicable diseases
Computer simulation
Disease transmission
Diseases
Epidemics
Epidemiology
Expected values
Generalized linear models
Germany
Health aspects
Humans
Infection
Infections
Infectious diseases
Lyme Disease - epidemiology
Markov Chains
Medical research
Medical tests
Medicine and Health Sciences
Model testing
Models, Statistical
Monte Carlo Method
Neighborhoods
People and places
Physical Sciences
Predictions
Probabilistic models
Random variables
Rotavirus
Rotavirus infections
Rotavirus Infections - epidemiology
Standard deviation
Statistical analysis
Surveillance
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Summary: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:1932-6203
1932-6203
DOI:10.1371/journal.pone.0225838