Skip to content

Latest commit

 

History

History
60 lines (54 loc) · 6.4 KB

resources.md

File metadata and controls

60 lines (54 loc) · 6.4 KB
Raw

Resources

XPRIZE

  1. Pandemic response challenge
  2. Guidelines
  3. Resources
  4. Participants page

Cognizant

  1. Demo: How AI Makes Intervention Recommendations
  2. Augmenting Human Decision Making - Optimizing COVID-19 Interventions
  3. Risto Miikkulainen, Olivier Francon, Elliot Meyerson, Xin Qiu, Elisa Canzani, Babak Hodjat
    From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic
    IEEE Transactions on Evolutionary Computatation

Blavatnik School of Government / University of Oxford

  1. Main page: Coronavirus Government Response Tracker
  2. Intervention plans: Codebook for the Oxford Covid-19 Government Response Tracker
  3. Intervention plans data: OxCGRT_latest.csv
  4. Anna Petherick, Beatriz Kira, Thomas Hale, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Saptarshi Majumdar, Helen Tatlow
    Variation in government responses to covid-19
    BSG-WP-2020/032

Papers

  1. Neil M Ferguson, Daniel Laydon, Gemma Nedjati-Gilani et al.
    Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imperial College London (16-03-2020)
    https://doi.org/10.25561/77482.
  2. Flaxman, S., Mishra, S., Gandy, A. et al.
    Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe.
    Nature 584, 257–261 (2020).
    https://doi.org/10.1038/s41586-020-2405-7
  3. Cédric Colas, Boris Hejblum, Sébastien Rouillon, Rodolphe Thiébaut, Pierre-Yves Oudeyer, Clément Moulin-Frier, Mélanie Prague
    EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
    arXiv:2010.04452
  4. Mauricio Arango, Lyudmil Pelov. COVID-19 Pandemic Cyclic Lockdown Optimization Using Reinforcement Learning. https://arxiv.org/abs/2009.04647
  5. IHME COVID-19 Forecasting Team., Reiner, R.C., Barber, R.M. et al. Modeling COVID-19 scenarios for the United States. Nat Med (2020). https://doi.org/10.1038/s41591-020-1132-9 https://rdcu.be/ccUrI
  6. Spencer Woody, Mauricio Tec, Maytal Dahan, Kelly Gaither, Michael Lachmann, Spencer J. Fox, Lauren Ancel Meyers, and James Scott. Projections for first-wave COVID-19 deaths across the U􏰀S􏰀 using social-distancing measures derived from mobile phones. https://covid-19.tacc.utexas.edu/media/filer_public/87/63/87635a46-b060-4b5b-a3a5-1b31ab8e0bc6/ut_covid-19_mortality_forecasting_model_latest.pdf
  7. Mohammad-H. Tayarani N., Applications of artificial intelligence in battling against covid-19: A literature review, Chaos, Solitons & Fractals, 2020, 110338, https://doi.org/10.1016/j.chaos.2020.110338.(http://www.sciencedirect.com/science/article/pii/S0960077920307335)
  8. Shinde, G.R., Kalamkar, A.B., Mahalle, P.N. et al. Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art. SN COMPUT. SCI. 1, 197 (2020). https://doi.org/10.1007/s42979-020-00209-9
  9. Chowdhury, R., Heng, K., Shawon, M.S.R. et al. Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries. Eur J Epidemiol 35, 389–399 (2020). https://doi.org/10.1007/s10654-020-00649-w https://rdcu.be/ccUsw
  10. C. K. Sruthi, Malay Ranjan Biswal, Brijesh Saraswat, Himanshu Joshi, and Meher K Prakash. Predicting and interpreting COVID-19 transmission rates from the ensemble of government policies. medRxiv 2020.08.27.20179853; doi: https://doi.org/10.1101/2020.08.27.20179853.
  11. Nils Haug, Lukas Geyrhofer, Alessandro Londei, Elma Dervic, Amélie Desvars-Larrive, Vittorio Loreto, Beate Pinior, Stefan Thurner & Peter Klimek. Ranking the effectiveness of worldwide COVID-19 government interventions, Nat Hum Behav 4, 1303–1312 (2020). https://doi.org/10.1038/s41562-020-01009-0
  12. Daniel Duque, David P. Morton, Bismark Singh, Zhanwei Du, Remy Pasco, and Lauren Ancel Meyers. Timing social distancing to avert unmanageable COVID-19 hospital surges. Proceedings of the National Academy of Sciences Aug 2020, 117 (33) 19873-19878; DOI: https://doi.org/10.1073/pnas.2009033117.
  13. Haoxiang Yang, Özge Sürer, Daniel Duque, David P. Morton, Bismark Singh, Spencer J. Fox, Remy Pasco, Kelly Pierce, Paul Rathouz, Zhanwei Du, Michael Pignone, Mark E. Escott, Stephen I. Adler, S. Claiborne Johnston, Lauren Ancel Meyers. Design of COVID-19 Staged Alert Systems to Ensure Healthcare Capacity with Minimal Closures. medRxiv 2020.11.26.20152520; doi: https://doi.org/10.1101/2020.11.26.20152520

Data

  1. Desvars-Larrive, A., Dervic, E., Haug, N. et al. A structured open dataset of government interventions in response to COVID-19. Sci Data 7, 285 (2020). https://doi.org/10.1038/s41597-020-00609-9 https://rdcu.be/ccUrD
  2. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/
  3. Coronavirus Resource Center, Johns Hopkins University https://coronavirus.jhu.edu/data
  4. City and County Non-Pharmaceutial Intervention Rollout Dates, Keystone Strategy. https://www.keystonestrategy.com/coronavirus-covid19-intervention-dataset-model/
  5. The COVID Tracking Project at the Atlantic. https://covidtracking.com
  6. SafeGraph COVID-19 Data Consortium. https://www.safegraph.com/covid-19-data-consortium

Research Centers and Consortia

  1. COVID-19 Modeling Consortium, the University of Texas at Austin. https://covid-19.tacc.utexas.edu
  2. The Institute for Health Metrics and Evaluation (IHME), University of Washington. http://www.healthdata.org
  3. MRC Centre for Global Infectious Disease Analysis, Imperial College London. https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/
  4. Coronavirus Resource Center, Johns Hopkins University. https://coronavirus.jhu.edu
  5. MIDAS, https://midasnetwork.us/covid-19/