Abstract
Infectious disease transmissionmodels require assumptions about how the pathogen spreads between individuals. These assumptions may be somewhat arbitrary, particularly when it comes to describing how transmission varies between individuals of different types or in different locations, and may in turn lead to incorrect conclusions or policy decisions. We develop a general Bayesian nonparametric framework for transmission modeling that removes the need to make such specific assumptions with regard to the infection process. We use multioutput Gaussian process prior distributions to model different infection rates in populations containing multiple types of individuals. Further challenges arise because the transmission process itself is unobserved, and large outbreaks can be computationally demanding to analyze. We address these issues by data augmentation and a suitable efficient approximationmethod. Simulation studies using synthetic data demonstrate that our framework gives accurate results. We analyze an outbreak of foot and mouth disease in the United Kingdom, quantifying the spatial transmission mechanism between farms with different combinations of livestock.
Original language | English |
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Article number | e2118425119 |
Number of pages | 6 |
Journal | Proceedings of the National Academy of Sciences of the United States of America |
Volume | 119 |
Issue number | 10 |
DOIs | |
Publication status | Published - 1 Mar 2022 |
Bibliographical note
Funding Information:ACKNOWLEDGMENTS. We are grateful for access to the University of Nottingham High Performance Computing Service. We thank the UK Department for Environment Food and Rural Affairs for data on the 2001 Foot and Mouth outbreak. This work was supported by the UK Engineering and Physical Sciences Research Council Grant EP/N50970X/1.
Publisher Copyright:
© 2022 National Academy of Sciences. All rights reserved.
Keywords
- Disease transmission models
- Foot and mouth disease
- Multioutput Gaussian processes
- Spatial epidemic models
ASJC Scopus subject areas
- General