Sites with data and publications
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- 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.
- Andersen, Kristian G., et al. “The proximal origin of SARS-CoV-2.” Nature Medicine (2020): 1-3
- Batista, Milan. “Estimation of the final size of the COVID-19 epidemic.” medRxiv, doi 10.2020.02 (2020): 16-20023606.
- Chen, Jun, et al. “Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study.” medRxiv (2020).
- Chinazzi, Matteo, et al. “The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak.” Science (2020).
- 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).
- 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).
- 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).
- Han, Henry. “Estimate the incubation period of coronavirus 2019 (COVID-19).” medRxiv (2020).
- 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).
- Joshi, Aditya, et al. “Harnessing tweets for early detection of an acute disease event.” Epidemiology 31.1 (2020): 90-97.
- Li, Yi, et al. “COVID-19 Epidemic Outside China: 34 Founders and Exponential Growth.” medRxiv (2020). .
- 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).
- Manning, Jessica E., et al. “Rapid metagenomic characterization of a case of imported COVID-19 in Cambodia.” bioRxiv (2020).
- Metsky, Hayden C., et al. “CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design.” bioRxiv (2020).
- Peng, Liangrong, et al. “Epidemic analysis of COVID-19 in China by dynamical modeling.” arXiv preprint arXiv:2002.06563 (2020).
- 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).
- Randhawa, Gurjit S., et al. “Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.” bioRxiv (2020).
- Shan+, Fei, et al. “Lung Infection Quantification of COVID-19 in CT Images with Deep Learning.” arXiv preprint arXiv:2003.04655 (2020).
- 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).
- Song, Ying, et al. “Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.” medRxiv (2020).
- Stebbing, Justin, et al. “COVID-19: combining antiviral and anti-inflammatory treatments.” The Lancet Infectious Diseases (2020).
- Wang, Shuai, et al. “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19).” medRxiv (2020).
- 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).
- Wood, Frank, et. al. “Planning as Inference in Epidemiological Models” (2020).
- Xu, Xiaowei, et al. “Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia.” arXiv preprint arXiv:2002.09334 (2020).
- 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).
- Yu, Hui, et al. “Data-driven discovery of clinical routes for severity detection in COVID-19 pediatric cases.” medRxiv (2020).
- 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.