Learning machines for computational epidemiology

Resting on our experience of computational epidemiology in practice and of industrial projects on analytics of complex networks, we point to an innovation opportunity for improving the digital services to epidemiologists for monitoring, modeling, and mitigating the effects of communicable disease. Artificial intelligence and intelligent analytics of syndromic surveillance data promise new insights to epidemiologists, but the real value can only be realized if human assessments are paired with assessments made by machines. Neither massive data itself, nor careful analytics will necessarily lead to better informed decisions. The process producing feedback to humans on decision making informed by machines can be reversed to consider feedback to machines on decision making informed by humans, enabling learning machines. We predict and argue for the fact that the sensemaking that such machines can perform in tandem with humans can be of immense value to epidemiologists in the future.