Simulation-Based Inference for Global Health Decisions

Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, Rob Zinkov, Puneet Dokania, N. Siddharth, Ana Belen Espinosa-Gonzalez, Ara Darzi, Philip Torr, Atilim Gunes Baydin

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

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.
Original languageEnglish
Number of pages6
Publication statusPublished - 18 Jul 2020
EventMachine Learning for Global Health: ICML 2020 Workshop - Virtual workshop
Duration: 18 Jul 202018 Jul 2020


WorkshopMachine Learning for Global Health
CityVirtual workshop
Internet address


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