How can data science help manage global childhood obesity?

The growing global issue of childhood obesity has not responded to traditional disease management techniques. Join this panel to hear the potential for better results with the help of analytics.

Discussion triggers

  1. Why has childhood obesity become such an issue?
  2. What needs to happen for Sweden’s “Zero childhood obesity at school start by 2030” target to be achieved?
  3. Where will the balance between prevention and cure be struck? What are the relative economics of both?
  4. How can analytics help?
  5. What are common contributing factors that lead to behavioral triggers to over-eating?
  6. Thinking about the broader issues, where will the data be coming from?
  7. What are common barriers to both prevention and treatment?
  8. How can technology enabled gamification help? Will data be the ultimate motivator?

Further reading

Nutrition and COVID, what we know so far

Can AI in healthcare help us identify high-risk people across systems?

Vision: Zero childhood obesity at school start by 2030

The Endocannabinoid System

Using machine learning to improve local services for residents