论文标题
基于模拟的全球健康决策推论
Simulation-Based Inference for Global Health Decisions
论文作者
论文摘要
COVID-19大流行强调了在预测传染病的动力学方面，在卫生政策和决策者有关适当的预防和遏制策略方面的重要性。在这种情况下的工作涉及解决挑战性的推理和控制问题的基于复杂性的基于个体的模型。在这里，我们讨论了机器学习的最新突破，特别是在基于模拟的推理中，并探讨了其作为模型校准的新型场所，以支持公共卫生干预措施的设计和评估。 To further stimulate research, we are developing software interfaces that turn two cornerstone COVID-19 and malaria epidemiology models COVID-sim, (https://github.com/mrc-ide/covid-sim/) and OpenMalaria (https://github.com/SwissTPH/openmalaria) into probabilistic programs, enabling efficient interpretable Bayesian inference within those模拟器。
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 COVID-sim, (https://github.com/mrc-ide/covid-sim/) and OpenMalaria (https://github.com/SwissTPH/openmalaria) into probabilistic programs, enabling efficient interpretable Bayesian inference within those simulators.
