Watch here: https://youtu.be/4ZwH-KliJos

A new agent-based modeling tool evaluates the consequences of social behavior on viral spread during the vaccination phase by simulating the effects of increased social interaction post-vaccination. The new tool compares and analyzes viral spread and the effects of interventions, in real-time, across varying configurations of infection and disease transmissions. It supports several COVID-19 interventions (Quanatine, Fast Test, PCR Test, Vaccination, Digital Exposure Notification) and scales to large agent populations (>100,000). The new modeling tool, a novel tensor calculus based, agent-based model (ABM) framework with an associated COVID-19 simulation tool, helps unite ABMs and scalable deep learning and can be implemented on GPUs.

On January 26, we'll bring together leaders in technology, design, and healthcare to discuss this advancement in ABM frameworks and its potential utility for researchers and policymakers as well as other tools to simplify the vaccination user's journey.

Vaccination coordination is facing daunting challenges. Citizens are expected to navigate an array of websites and health authorities are using disconnected health IT systems. Reporting lags by several days. Following up with vaccinated subjects to monitor side effects is difficult. The systems to monitor ineffective batches of vaccines are yet to become mature. Vaccine verifications documents are prone to fraud.

Join us for a discussion of solutions for the many challenges remaining in the vaccine rollout.

Speakers:

Ramesh Raskar, MIT

Kris Joshi, Change Healthcare

Asif Dhar, Deloitte

Balaji Krishnamurthy, Adobe

Vitor Pamplona, PathCheck Foundation

James Smalls, IDEO

Ayush Chopra, MIT

Learn more about Trusted Pandemic Technologies at the MIT Media Lab here: https://pandemic.mit.edu/