Journal article
Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python
- Abstract:
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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...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Version of record, pdf, 1.8MB)
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- Publisher copy:
- 10.1016/j.epidem.2018.05.009
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Authors
Funding
Bill and Melinda Gates Foundation
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Bibliographic Details
- 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:
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1878-0067
- ISSN:
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1755-4365
- Pmid:
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29884470
- Source identifiers:
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854084
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
pubs:854084
- UUID:
-
uuid:2fa6a606-0694-4f3a-a368-12f82e06cff7
- Local pid:
- pubs:854084
- Deposit date:
- 2018-06-19
Terms of use
- 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/).
- Licence:
- CC Attribution (CC BY)
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