Wang Lab

Weill Cornell Medicine

AI for COVID-19: Battling the Pandemic with Computational Intelligence

AAAI 2021 Tutorial. February 3. Wednesday Afternoon. AH1. 12:00-3:00pm. Pacific Time.

Presenter: Fei Wang

The new coronavirus disease 2019 (COVID-19) has become a global pandemic with over 23 million confirmed cases and 820K deaths until August, 2020 according to the statistics from the World Health Organization. Since the initial outbreak from January 2020 in Wuhan, China, COVID-19 has demonstrated a high transmission rate (with R0 value bigger than 2) and a diverse set of clinical characteristics (e.g., high rate of hospital and intensive care unit admission rates, multi-organ dysfunction for critically ill patients due to hyperinflammation, thrombosis, etc.). To understand the disease mechanism of COVID-19 and develop effective control, treatment and prevention strategies, researchers from related disciplines are making tremendous efforts on different aspects including epidemiology and public health, biology and genomic medicine, as well as clinical care and patient management. This tutorial will summarize the efforts on battling the pandemic with artificial intelligence. In particular, I will focus on how AI methods can derive insights on the prediction, treatment and prevention of COVID-19 on the following 4 aspects: 1) epidemiology; 2) therapeutics; 3) clinical research; 4) social and behaviorial studies. I will also point out the challenges and summarize the implications and envision how AI can advance human healthcare in the post-pandemic era.

The outline of the entire tutorial is listed below.

Introduction (10 min, Slides)
Epidemiology (35 min, Slides)
Compartment Models
Machine Learning for Compartment Models
Estimating the Impact of Non-Pharmaceutical Interventions
Knowledge Transfer
Estimating the Impact of Temperature and Relative Humidity
Therapeutics (25 min, Slides)
De-Novo Design
Computational Drug Repurposing
Knowledge Graph
Clinical Research (75 min, Slides)
Image Based Diagnosis and Prognosis
Prediction of Infection Status with Rountined Blood Tests
Prediction of Clinical Severity with EHR
Deep Phenotyping with Clinical Information at Patient Admission
Progression Subphenotyping of Intubated Patients
Behavioral and Social Sciences (25 min, Slides)
Information Search Behavior Change
The Impact of Misinformation
Psychosocial Impacts
Mobility Network
Contact Tracing
Conclusions (10 min, Slides)

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