Papers

Sites with data and publications

 

  1. Al-qaness, Mohammed AA, et al. “Optimization Method for Forecasting Confirmed Cases of COVID-19 in China.” Journal of Clinical Medicine 9.3 (2020): 674.
  2.  Andersen, Kristian G., et al. “The proximal origin of SARS-CoV-2.” Nature Medicine (2020): 1-3
  3. Batista, Milan. “Estimation of the final size of the COVID-19 epidemic.” medRxiv, doi 10.2020.02 (2020): 16-20023606.
  4. Chen, Jun, et al. “Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study.” medRxiv (2020).
  5. Chinazzi, Matteo, et al. “The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak.” Science (2020).
  6. Fang, Zhiming, et al. “How many infections of COVID-19 there will be in the” Diamond Princess”-Predicted by a virus transmission model based on the simulation of crowd flow.” arXiv preprint arXiv:2002.10616 (2020).
  7. Ferguson, Neil M., et al. “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand.” London: Imperial College COVID-19 Response Team, March 16 (2020).
  8. Gozes, Ophir, et al. “Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis.” arXiv preprint arXiv:2003.05037 (2020).
  9. Han, Henry. “Estimate the incubation period of coronavirus 2019 (COVID-19).” medRxiv (2020).
  10. Hu, Fan, Jiaxin Jiang, and Peng Yin. “Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model.” arXiv preprint arXiv:2003.00728 (2020).
  11. Joshi, Aditya, et al. “Harnessing tweets for early detection of an acute disease event.” Epidemiology 31.1 (2020): 90-97.
  12. Li, Yi, et al. “COVID-19 Epidemic Outside China: 34 Founders and Exponential Growth.” medRxiv (2020). .
  13. Liu, Zhihua, et al. “Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data.” arXiv preprint arXiv:2002.12298 (2020).
  14. Manning, Jessica E., et al. “Rapid metagenomic characterization of a case of imported COVID-19 in Cambodia.” bioRxiv (2020).
  15. Metsky, Hayden C., et al. “CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design.” bioRxiv (2020).
  16. Peng, Liangrong, et al. “Epidemic analysis of COVID-19 in China by dynamical modeling.” arXiv preprint arXiv:2002.06563 (2020).
  17. Qi, Xiaolong, et al. “Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study.” medRxiv (2020).
  18. Randhawa, Gurjit S., et al. “Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.” bioRxiv (2020).
  19. Shan+, Fei, et al. “Lung Infection Quantification of COVID-19 in CT Images with Deep Learning.” arXiv preprint arXiv:2003.04655 (2020).
  20. Smith, Micholas, and Jeremy C. Smith. “Repurposing Therapeutics for COVID-19: Supercomputer-Based Docking to the SARS-CoV-2 Viral Spike Protein and Viral Spike Protein-Human ACE2 Interface.” (2020).
  21. Song, Ying, et al. “Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.” medRxiv (2020).
  22. Stebbing, Justin, et al. “COVID-19: combining antiviral and anti-inflammatory treatments.” The Lancet Infectious Diseases (2020).
  23. Wang, Shuai, et al. “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19).” medRxiv (2020).
  24. Wang, Yunlu, et al. “Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner.” arXiv preprint arXiv:2002.05534 (2020).
  25. Wood, Frank, et. al. “Planning as Inference in Epidemiological Models” (2020).
  26. Xu, Xiaowei, et al. “Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia.” arXiv preprint arXiv:2002.09334 (2020).
  27. Yan, Li, et al. “Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan.” medRxiv (2020).
  28. Yu, Hui, et al. “Data-driven discovery of clinical routes for severity detection in COVID-19 pediatric cases.” medRxiv (2020).
  29. Zhavoronkov, Alex, et al. “Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches.” Insilico Medicine Hong Kong Ltd A 307 (2020): E1.