Outbreaks are mostly unpredictable and therefore harder to prevent.
The problems have been fleshed out into a briefing designed to describe the root causes of the problem, current approaches in different countries, and lessons learned from other domains.
Read the Problem DescriptionImprove Zika response by building a predictive analytics platform
Resource Offset: Analytics platforms are designed to reduce medium to long term costs, as the data they generate can help optimize resource deployment (e.g., preemptively sending health workers to where an outbreak is likely to occur can avoid the costs associated with an outbreak)
Lakshminarayanan Subramanian, Professor, Courant Institute, NYU
Use prize-backed challenges to rapidly develop predictive models and leverage outside expertise
Resource Offset: Prizes can be donated, and the challenge should be formulated to produce results that decrease costs. Challenges can also work with small prizes (or no prizes), provided the work is compelling enough and there is sufficient non-financial recognition of participants’ contributions
Michael Johansson, Biologist, CDC
Increase data analytics literacy among public health officials by training them in data science (through partnerships with research institutions, universities, and other training providers)
Resource Offset: More data science training can enable these officials to analyze their own agencies’ work and identify cost savings
Jesse Bell, North Carolina Institute for Climate Studies, Gianluca Fontana, Centre for Health Policy, Institute of Global Health Innovation, Imperial College, Daniel Ray, Chief Data Scientist, UK National Health Service, Michael Johansson, Biologist, CDC
Collaborate on creation of a Zika-related data portal that compiles national and other open datasets
Resource Offset: Money can be saved by using existing open source platforms and prioritizing datasets designed to generate insights that reduce costs
Anita McGahan, University of Toronto
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