Modeling to Inform Disease Control, Screening, Treatment Policies, and Prevention Interventions
Improving Maternal and
Child Health Outcomes in
Partnership with ARMMAN
This project aims at using AI for improving Maternal and Child Health Outcomes by predicting which beneficiaries are at a risk of dropping out from automated health information delivery programs. View this page for more info.
Photo credit: Jon Betz
In collaboration with the Harvard T.H. Chan School of Public Health, the climate-smart health project aims to develop AI tools to 1) detect and predict climate change-induced health outcomes with diverse sources of data, including health surveys and satellite images, and 2) optimize intervention planning for limited health resources allocations to reduce the impact of climate on public health in vulnerable communities in Madagascar. View this page for more information.
Among Homeless Youth
This project focuses on the development of decision support systems for homeless youth drop-in center staff, who need to find the most influential homeless youth to raise awareness about HIV (and other STDs) among their peers, and to drive the homeless youth community towards safer behaviors. View HIV Prevention among Homeless Youth by Influence Maximization.
The COVID-19 outbreak has caused an unprecedented global reaction with countries taking drastic steps to combat the pandemic. Mathematical modeling and multi-agent based analysis of the pandemic allows better understanding of the disease spread and may help inform policy at the national and regional level. We use tools and modeling techniques from AI to help understand the situation better and design aids that may help policymakers design better solutions in the fight against this pandemic. Click here to read more about Teamcore's efforts to stop the spread of COVID-19.
Using Machine Learning
& Multi-Agent Planning
to Fight Tuberculosis
Tuberculosis is one of the top 10 killers in the world and is especially prevalent in India. AI can help across the entire pipeline of care, from decision support tools for planning active screening routes, to predictive algorithms for resource constrained health workers to deliver targeted interventions to patients.
Research has consistently documented levels of cocaine, heroin, methamphetamine, alcohol, and marijuana use and abuse among these adolescents that far exceed that of housed adolescents. This Social Networks and Substance Abuse Prevention for Homeless Youth project aims to use algorithms to determine the best group formations to prevent regular use of hard drugs among homeless youth.
One of the fundamental questions facing social science is how social networks and the cognitions people have about their networks affect their mental states and mental health. AI techniques present an opportunity to dynamically model social networks and the messages transmitted across those networks to create predictive models of influence unavailable with standard statistical techniques. View Predictive Modeling for Early Identification of Suicidal Thinking.