Simulation-based inference for global health decisions
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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
2020
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| _version_ | 1826312884708179968 |
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| author | Schroeder de Witt, C Gram-Hansen, B Nardelli, N Gambardella, A Zinkov, R Dokania, P Siddharth, N Espinosa-Gonzalez, AB Darzi, A Torr, PHS Güneş Baydin, A |
| author_facet | Schroeder de Witt, C Gram-Hansen, B Nardelli, N Gambardella, A Zinkov, R Dokania, P Siddharth, N Espinosa-Gonzalez, AB Darzi, A Torr, PHS Güneş Baydin, A |
| author_sort | Schroeder de Witt, C |
| collection | OXFORD |
| description | 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 potential as a novel venue for model calibration to support the design and evaluation of public health interventions. To further stimulate research, we are developing software interfaces that turn two cornerstone COVID-19 and malaria epidemiology models (CovidSim and OpenMalaria) into probabilistic programs, enabling efficient interpretable Bayesian inference within those simulators. |
| first_indexed | 2024-03-07T02:33:16Z |
| format | Conference item |
| id | oxford-uuid:a7f1c1d8-4f38-4794-8767-632d9f1ccff3 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:02:08Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | oxford-uuid:a7f1c1d8-4f38-4794-8767-632d9f1ccff32024-04-26T11:16:31ZSimulation-based inference for global health decisionsConference itemhttp://purl.org/coar/resource_type/c_6670uuid:a7f1c1d8-4f38-4794-8767-632d9f1ccff3EnglishSymplectic Elements2020Schroeder de Witt, CGram-Hansen, BNardelli, NGambardella, AZinkov, RDokania, PSiddharth, NEspinosa-Gonzalez, ABDarzi, ATorr, PHSGüneş Baydin, AThe 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 potential as a novel venue for model calibration to support the design and evaluation of public health interventions. To further stimulate research, we are developing software interfaces that turn two cornerstone COVID-19 and malaria epidemiology models (CovidSim and OpenMalaria) into probabilistic programs, enabling efficient interpretable Bayesian inference within those simulators. |
| spellingShingle | Schroeder de Witt, C Gram-Hansen, B Nardelli, N Gambardella, A Zinkov, R Dokania, P Siddharth, N Espinosa-Gonzalez, AB Darzi, A Torr, PHS Güneş Baydin, A Simulation-based inference for global health decisions |
| title | Simulation-based inference for global health decisions |
| title_full | Simulation-based inference for global health decisions |
| title_fullStr | Simulation-based inference for global health decisions |
| title_full_unstemmed | Simulation-based inference for global health decisions |
| title_short | Simulation-based inference for global health decisions |
| title_sort | simulation based inference for global health decisions |
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