Toward analytics-based clinical and policy decision support to respond to the COVID-19 pandemic

The COVID-19 pandemic creates unprecedented challenges for healthcare providers and policy makers. How to triage patients when healthcare resources are limited? Whom to test? And how to design social distancing policies to contain the disease and its socioeconomic impact? Analytics can provide data-driven answers. We have collected comprehensive data from hundreds of clinical studies, case counts, and hospital collaborations. We have developed a new epidemiological model of the disease's dynamics, a machine-learning model of mortality risk, and a resource allocation model-published on www.covidanalytics.io. In this project, we develop automated, interpretable, and scalable decision-making systems based on machine learning and artificial intelligence (ML/AI) to support clinical practices and public policies as they respond to the COVID-19 pandemic. We tackle four research questions: 1) How can we predict admissions in intensive care units (ICU) using machine learning? 2) How does COVID-19 impact different demographic and socioeconomic populations? 3) How does mobility impact the disease's spread, and how to optimize social distancing policies? 4) How to augment COVID-19 tests with data-driven warnings that identify high-risk subjects? This project leverages large-scale datasets (from C3 AI and our own collection efforts), high-performance computing (using the C3 AI suite), and advanced ML/AI. Specifically, this project develops end-to-end ML/AI methods, spanning epidemiological modeling (to model the disease's spread), machine learning (to predict ICU admissions and test results), causal inference (to investigate disparities across populations) and optimal control (to support social distancing guidelines). We will disseminate results to healthcare providers, policy makers, researchers, and the public.