Conference item : Abstract
Simulation-based inference for global health decisions
- Abstract:
-
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies. Work in this setting involves solving challenging inference and control problems in individual-based models of ever increasing complexity. Here we discuss recent breakthroughs in machine learning, specifically in simulation-based inference, and explore its ...
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- Publication status:
- Not published
- Peer review status:
- Peer reviewed
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Authors
Bibliographic Details
- Publication date:
- 2021-07-18
- Acceptance date:
- 2020-06-20
- Event title:
- ML for Global Health: ICML 2020 Workshop
- Event location:
- Online
- Event website:
- https://mlforglobalhealth.org/
- Event start date:
- 2020-07-18
- Event end date:
- 2020-07-18
Item Description
- Language:
- English
- Keywords:
- Subtype:
- Abstract
- Pubs id:
-
1170813
- Local pid:
- pubs:1170813
- Deposit date:
- 2021-04-08
Terms of use
- Copyright holder:
- Schroeder de Witt et al.
- Copyright date:
- 2020
- Notes:
- This abstract was presented at the ML for Global Health: ICML 2020 Workshop, 18th July 2020.
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