Learning COVID-19 Mitigation Strategies Using Reinforcement Learning-Reference-Cited by-同舟云学术

Learning COVID-19 Mitigation Strategies Using Reinforcement Learning

Published:2021-09-07 Issue: Volume: Page:251-271
ISSN:1069-5265
Container-title:Mathematics of Public Health
language:
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Author:

Denis Nicholas,El-Hajj Alexander,Drummond Blair,Abiza Yamina,Gopaluni Krishna Chaitanya

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-85053-1_12

Reference31 articles.

1. Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., ... & Xing, X. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England Journal of Medicine

2. Banka, P., & Comiskey, C. M. (2020). The incubation period of COVID-19: A scoping review and meta-analysis to aid modelling and planning. medRxiv

3. Mahsin, M., Lee, S., Vickers, D., Guigue, A., Williamson, T., Quan, H., ... & Ravani, P. (2020). The Coronavirus 2019 pandemic in Canada: the impact of public health interventions on the course of the outbreak in Alberta and other provinces. medRxiv

4. Flaxman, S., Mishra, S., Gandy, A., Unwin, H. J. T., Mellan, T. A., Coupland, H., ... & Monod, M. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257–261

5. COVID, I. Modeling COVID-19 scenarios for the United States. Nature medicine

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