Identifying Data “Cold Spots”
Identifying and Forecasting Zika Hot Spots by Finding the Data Cold Spot
Location: Cambridge, Massachusetts, USA
Problem: To combat Zika and future threats, it is crucial for health care systems and policy makers to identify and forecast geographies that have been impacted or are likely to be impacted by Zika before the disease spreads. By analyzing communities that are data “cold spots” - where little to no data from exists, from health indicators, to demographics, to banking data - it’s often the case that communities in these cold spots have not been checked, mitigated against, and/or treated for Zika. The challenge for health care systems and policy makers is to find these cold spots, and take action before data cold spots become disease hotspots.
Solution: Dimagi, the Arnhold Institute for Global Health, and TulaSalud (supported by the Tula Foundation), will leverage one of the most widely used Frontline Worker systems, CommCare, and cutting edge geospatial and predictive algorithms from ATLAS to identify cold spots and compute their risks for different diseases across Latin America. This information can be acted upon to gather more information to detect and manage disease outbreaks. The program will combine three key elements: real-time geo-coded data from front-line workers who are using CommCare, automated population estimates from satellite image analysis through machine learning, and data-driven Zika risk indices inferred from additional spatial and health data such as density of pregnant women, bodies of standing water, air temperature, and humidity. These inputs will be combined, using open standards such as OpenHIE when appropriate, with a new algorithm to create a risk score for cold spots. This new approach will provide actionable insights for managers and decision makers (e.g. community health worker managers, MOH) into where to prioritize short-term resource allocation.
Michael O’Donnell – Washington University in St. Louis
"Working in mobile health provides me the opportunity to stay professionally involving in new and evolving mobile technology, continually devising and testing new use cases for mobile phones, all while working to promote improved access to healthcare for populations most in need."
James Faghmous – PhD in Computer Science, University of Minnesota
Bruno Silva – MFA in Design for Social Innovation, MFA School of Visual Arts; BFA in Design, BFA School of Visual Arts
"As a social innovator my biggest motivator on the work I do is to ensure that innovations benefit the entire society not only a privileged few."
Matthew Le – MS in Computer Science, Rochester Institute of Technology; B.S. in Computer Science, University of Minnesota - Twin Cities
"Throughout graduate school I saw the impact that computer science can have on a global scale. This was strong motivation for me coming to the Arnhold Institute."