DeepABM: Scalable and Efficient Agent-Based Simulations Via Geometric Learning Frameworks - a Case Study For Covid-19 Spread and Interventions-Reference-Cited by-同舟云学术

DeepABM: Scalable and Efficient Agent-Based Simulations Via Geometric Learning Frameworks - a Case Study For Covid-19 Spread and Interventions

Published:2021-12-12 Issue: Volume: Page:
ISSN:
Container-title:2021 Winter Simulation Conference (WSC)
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Author:

Chopra Ayush¹,Raskar Ramesh¹,Subramanian Jayakumar²,Krishnamurthy Balaji²,Gel Esma S.³,Romero-Brufau Santiago⁴,Pasupathy Kalyan S.⁴,Kingsley Thomas C.⁵

Affiliation:

1. Massachusetts Institute of Technology,Media Lab,MA,USA,02139

2. Adobe,Media and Data Science Research Lab,Noida,Uttar Pradesh,INDIA,201304

3. School of Comp., Inf. and Decision Systems Eng., Arizona State University,AZ,USA,85281

4. Mayo Clinic,Department of Healthcare Systems Engineering,MN,USA,55905

5. Alix School of Science and Medicine, Mayo Clinic,MN,USA,55905

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9715283/9715284/09715507.pdf?arnumber=9715507

Reference19 articles.

1. Epidemiological parameters of coronavirus disease 2019: a pooled analysis of publicly reported individual data of 1155 cases from seven countries;ma;medRxiv,0

2. Mesa: An Agent-Based Modeling Framework

3. PyTorch: An Imperative Style, High-Performance Deep Learning Library;paszke;Advances in Neural IInformation Processing Systems,0

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