The Rockefeller Foundation https://www.rockefellerfoundation.org and Global Health Partners including UNICEF, WHO, The Global Fund, the World Banks’s Global Financing Facility, and Gavi, the Vaccine Alliance, recently have provided $100 Million to develop the “Precision Public Health Initiative”.
The initiative aims to prevent 6 million deaths in 10 countries by 2030. This initiative will begin in India and Uganda and then expand to eight additional countries. The goal is to bring the latest data science innovations and machine learning to Community Health Systems and Frontline workers.
The reality is that data science and new innovations are not reaching the people who need them the most. The “Precision Public Health Initiative” will leverage technologies that will be used to transform health in wealthy countries to dramatically reduce preventable maternal and child deaths.
The data science will include more accurate and precise decision-making tools based on large, integrated datasets, predictive analytics, artificial intelligence, and machine learning. This will enable frontline health workers to have access to simple inexpensive data analytic tools.
The Initiative will build upon similar efforts on a smaller scale that have already shown encouraging results in applying data science to better deploy life-saving health tools. These tools include developing real-time risk maps to direct frontline health workers to areas of greatest need and analyze non-health data like climate patterns or social media trends to predict and better address health emergencies weeks in advance.
The Foundation and the Global Health Partners are looking forward to sharing knowledge and data through the partner alliance so experts working together on technology will be able to share information datasets, expertise, and other resources.
There is also interest in leveraging innovations from the private sector to identify and test new ways to use digital maps, road networks, climate patterns, and social media data from the private sector to better predict public health challenges such as infectious disease outbreaks before they occur.