Simulation-Based Inference for Global Health Decisions

This is fomenting the development of comprehensive modelling The COVID-19 pandemic has highlighted the importance and simulation to support the design of health interventions of in-silico epidemiological modelling in predicting and policies, and to guide decision-making in a variety of the dynamics of infectious diseases to inform health system domains [22, 49]. For example, simulations health policy and decision makers about suitable prevention have provided valuable insight to deal with public health and containment strategies. Work in this setting problems such as tobacco consumption in New Zealand [50], involves solving challenging inference and control and diabetes and obesity in the US [58]. They have been problems in individual-based models of ever increasing used to explore policy options such as those in maternal and complexity. Here we discuss recent breakthroughs antenatal care in Uganda [44], and applied to evaluate health in machine learning, specifically in simulation-based reform scenarios such as predicting changes in access to inference, and explore its potential as a novel venue primary care services in Portugal [21]. Their applicability for model calibration to support the design and evaluation in informing the design of cancer screening programmes of public health interventions. To further stimulate has been also discussed [42, 23]. Recently, simulations have research, we are developing software interfaces that informed the response to the COVID-19 outbreak [19].