نقش علوم داده در مواجهه با همه گیری کرونا

1. Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challengesInternational journal of antimicrobial agents. 2020;55(3):105924.
2. Bluedot [Available from: https://bluedot.global/].
3. Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. 2020:200642.
4. inferVISION [Available from: https://global.infervision.com/].
5. Terra Drone [Available from: https://www.terra-drone.net/global/].
6. Blue Ocean Robotics [Available from: https://www.blue-ocean-robotics.com/].
7. Robot Vehicles Make Contactless Deliveries Amid Coronavirus Quarantine [Available from: https://spectrum.ieee.org/automaton /transportation/self-driving/robot-vehicles-make-contactless-deliveries-amid-coronavirus-quarantine].
8. Zhang Y. Progress and challenges in protein structure prediction. Current opinion in structural biology. 2008;18(3):342-8.
9. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, et al. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins: Structure, Function, and Bioinformatics. 2019;87(12):1141-8.
10. 1 Yang J, Anishchenko I, Park H, Peng Z, Ovchinnikov S, Baker D. Improved protein structure prediction using predicted interresidue orientations. Proceedings of the National Academy of Sciences of the United States of America. 2020;117(3):1496-503.
11. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020:1-5.
12. DeepMind[Available from: https://deepmind.com/].
13. Qiang X-L, Xu P, Fang G, Liu W-B, Kou Z. Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus. Infectious Diseases of Poverty. 2020;9(1):1-8.
14. Grifoni A, Sidney J, Zhang Y, Scheuermann RH, Peters B, Sette A. A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2. Cell Host Microbe. 2020;27(4):671-80.e2.
15. Srinivasan S, Cui H, Gao Z, Liu M, Lu S, Mkandawire W, et al. Structural Genomics of SARS-CoV-2 Indicates Evolutionary Conserved Functional Regions of Viral Proteins. Viruses. 2020;12(4):360.
16. Gordon DE, Jang GM, Bouhaddou M, Xu J, Obernier K, O’Meara MJ, et al. A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing. bioRxiv. preprint.
17. Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F. Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discovery. 2020;6(1):1-18.
18. Mosaddeghi P, Negahdaripour M, Dehghani Z, Farahmandnejad M, Moghadami M, Nezafat N, et al. Therapeutic approaches for COVID-19 based on the dynamics of interferon-mediated immune responses. 2020, preprint.
19. Tárnok A. Machine Learning, COVID‐19 (2019‐nCoV), and multi‐OMICS. Cytometry Part A. 2020;97(3):215-6.
20. Ibrahim IM, Abdelmalek DH, Elshahat ME, Elfiky AA. COVID-19 Spike-host cell receptor GRP78 binding site prediction. Journal of Infection. 2020, in press.
21. سامانه خود ارزیابی بیماران کرونا [Available from: https://salamat.gov.ir/].
22. Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. nature. 1998;393(6684):440.
23. Karinthy F. Chain-links. Everything is different. 1929
24. Zhao PJ. A Social Network Model of the COVID-19 Pandemic. medRxiv. 2020, preprint.

25.Taubenberger JK, Kash JC, Morens DM. The 1918 influenza pandemic: 100 years of questions answered and unanswered. Science translational medicine. 2019;11(502).

26. Lin Q, Zhao S, Gao D, Lou Y, Yang S, Musa SS, et al. A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. International journal of infectious diseases. 2020;93:211-6.
27. Shao N, Zhong M, Yan Y, Pan H, Cheng J, Chen W. Dynamic models for Coronavirus Disease 2019 and data analysis. Mathematical Methods in the Applied Sciences. 2020:1-7.
28. Fanelli D, Piazza F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals. 2020;134:109761.
29. Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The lancet infectious diseases. 2020, in press.
30. Benvenuto D, Giovanetti M, Vassallo L, Angeletti S, Ciccozzi M. Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in brief. 2020:105340.
31. Roda WC, Varughese MB, Han D, Li MY. Why is it difficult to accurately predict the COVID-19 epidemic? Infectious Disease Modelling. 2020:2468-0427
32. Murray JD. Mathematical biology: I. An introduction. 3rd ed: Springer Science & Business Media; 2007.
33. Oppenheim AV, Schafer WR, Buck JR. Discrete-time Signal Processing. 2nd ed: Prentice Hall Press; 1999.