- Abstract:
-
Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adap...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's version
- Files:
-
-
(pdf, 1.8MB)
-
- Publisher copy:
- 10.1016/j.epidem.2018.05.009
-
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- Publisher:
- Elsevier Publisher's website
- Journal:
- Epidemics Journal website
- Volume:
- 25
- Pages:
- 80-88
- Publication date:
- 2018-05-26
- Acceptance date:
- 2018-05-24
- DOI:
- EISSN:
-
1878-0067
- ISSN:
-
1755-4365
- Pubs id:
-
pubs:854084
- URN:
-
uri:2fa6a606-0694-4f3a-a368-12f82e06cff7
- UUID:
-
uuid:2fa6a606-0694-4f3a-a368-12f82e06cff7
- Local pid:
- pubs:854084
- Language:
- English
- Keywords:
- Copyright holder:
- Irvine and Hollingsworth
- Copyright date:
- 2018
- Notes:
- © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Journal article
Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python
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Bill and Melinda Gates Foundation
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