modeling
for pandemic prevention
[ thrust 03 ]
In MAPPS Thrust 3, we leverage Thrust 1 and Thrust 2 data streams and develop methodologies for joint modeling of biological and social processes. We propose a research program that develops methods and infrastructure to optimize responses to epidemics of transmissible pathogens at the micro, meso, and macro levels.
Our goal for the 18-month Predictive Intelligence for Pandemic Prevention (PIPP) Phase I is to develop a prototype of the modeling framework using the ongoing SARS-CoV-2 pandemic as the primary example, while integrating sufficiently flexible functions enabling efficient model adaptation to new pathogens or new knowledge of existing pathogens.
Our longer term goal is to design and implement a fully functioning modeling framework, fed by available data and targeted data collection activities coordinated under Thrusts 1 and 2, that is flexible, publicly available, and able to predict the effects of pandemics and pandemic prevention policies on a population. We envision assembling a library of model “templates” which will serve as recipes to create specific models for novel epidemic scenarios. Experience from infectious diseases like HIV, tuberculosis, malaria, and influenza, and chronic diseases, like cancer, suggests that such comparative modeling can help inform robustness of approaches and reveal important knowledge gaps, especially when results are qualitatively dissonant.
Meet the Simulation and Modeling Working Group
MAPPS Thrust 3 is managed by Co-PI Thomas Trikalinos (Professor in the Department of Health Services, Policy & Practice and the Department of Biostatistics), and the related Modeling and Simulation Working Group is led by Jason Gantenberg (Research Scientist in Health Services, Policy & Practice and Assistant Professor of Practice in Epidemiology). The group includes Brown faculty with experience in a variety of mathematical modeling and machine learning techniques. Current members include Aditya Khanna (Department of Behavioral and Social Sciences), Alyssa Bilinski (Department Health Services, Policy & Practice), Stavroula Crysanthopoulou (Department of Biostatistics), and Mark Lurie (Department of Epidemiology). Simulation and modeling work is supported by undergraduate students Robayet Hossain, Xilin Wang, Yatharth Sharma, and Kiku Shaw and graduate students Gage Reitzel and Jorge Ledesma.