Abedi Jafari, H., Abui Ardakan, M., Aghazadeh, F., Delbari Ragheb, F. (2012). Science mapping methodology: A case study of public management science mapping. Seminary and University of Humanities Methodology, 17 (66), 53-69. [in Persian]
Akhtar, M., Kraemer, M. U., & Gardner, L. M. (2019). A dynamic neural network model for predicting risk of Zika in real time.
BMC medicine,
17(1), 171.
https://doi.org/10.1186/s12916-019-1389-3
Ali Ahmadi Jashfaqani, H. (2020). Strategic Disease Management Covid-19. Shahid Sadoughi University of Medical Sciences, 28 (10): 3010-3103. [in Persian]
https://doi.org/10.18502/ssu.v28i10.4919
AliBabaCloud. (2020, February).from Alibaba WebSite:https://www.alibabacloud.com/campaign /fight-coronavirus-covid-19 (Access Date:: 18.04.2020).
Baji, F., Parsai Mohammadi, P., Sabbaghinejad, Z. (2012). A study of the scientific products of the medical field of the Middle Eastern countries in the Scopus citation database in the years 2001-2010. In the Proceedings of the Third National Conference on Research and Production of Science in the Field of Medicine, by the efforts of Musa Yamin Firooz, Afshin Mousavi Chelek and Aram Tirgar. Tehran: Librarian, 2012. [in Persian] https://search.ricest.ac.ir/dl/search/defaultta.aspx?DTC=36&DC=8570
Berryhill, J., Heang, K. K., Clogher, R., & McBride, K. (2019). Hello, World: Artificial Intelligence and its use in the Public Sector. Observatory of Public Sector Innovation (OPSI), 36, 1–148.
Brueckner, R. (2020, March 19). Inside HPC.
https://insidehpc.com/2020/03/alibaba-cloudoffers- ai-cloud-services-to-help-battle-covid-19- globally / (Access Date: 18.04.2020).
Chae, S., Kwon, S., & Lee, D. (2018). Predicting infectious disease using deep learning and big data.
International journal of environmental research and public health,
15(8), 1596.
https://doi.org/10.3390/ijerph15081596
Dai S. (2020), Chinese Government Launches New Tech Database to Help Communities Fight the Coronavirus South China Morning Post, https://www.scmp.com/tech/appssocial/article/3075579/chinese-governmentlaunches- new-tech-database-help-communities (Access Date:19 April 2020).
Danesh, F., Ghavidel, S. (2020). Coronavirus: The scientometrics of fifty years of global science production. Iranian Medical Microbiology, 14 (1): 1-16. [in Persian]
https://doi.org/10.30699/ijmm.14.1.1
Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time.
The Lancet infectious diseases,
20(5), 533-534.
https://doi.org/10.1016/S1473-3099(20)30120-1
Erkuş S. (2020). How China did it: 4 early principles" Hurriyet.com
Farzin Yazdi, M., Rezaei Sharifabadi, S. (2018). A Study of Scientific Productions in the Subject Field of Artificial Intelligence in the Middle East Countries from 1996 to 2014. Scientometrics Research Journal, (6) 3, 97-114. [in Persian] doi: 10.22070 / rsci.2017.512
Gambhir, M., Bozio, C., O'Hagan, J. J., Uzicanin, A., Johnson, L. E., Biggerstaff, M., & Swerdlow, D. L. (2015). Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential. Clinical Infectious Diseases, 60(suppl_1), S11-S19.
Haghani, M., & Bliemer, M. C. (2020). Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCov literature. arXiv preprint arXiv:2006.00674
Haghani, Milad, Bliemer, Michiel C.J., Goerlandt, Floris,Li, Jie (2020) The scientific literature on Coronaviruses, COVID-19 and its associated safety-related research dimensions: A scientometric analysis and scoping review. Safety Science,129, p104806, doi = "https://doi.org/10.1016/j.ssci.2020.104806",
Hamzah, F. B., Lau, C., Nazri, H., Ligot, D. V., Lee, G., & Tan, C. L. (2020). CoronaTracker: worldwide COVID-19 outbreak data analysis and prediction. Bull World Health Organ, 1(32).
Hayati, M., Biller, P., & Colijn, C. (2020). Predicting the short-term success of human influenza virus variants with machine learning. Proceedings of the Royal Society B, 287(1924), 20200319.
https://www.hurriyet.com.tr/gundem/cin-nasilyapti- early-principle-41481192 (Access Date: 13 April 2020).
Hu, Z., Ge, Q., Li, S., Jin, L., & Xiong, M. (2020). Artificial Intelligence Forecasting of Covid-19 in China.
arXiv preprint arXiv. 1–20.
http://arxiv.org/abs/2002.07112
Jafari, S., Farshid, R., Jabbari, L. (2021). Thematic analysis of Covid 19 studies on five major continents. Journal of Scientometrics, 6 (11), 277-297. [in Persian] doi: 10.22070 / rsci.2020.5494.1385
Jiang, D., Hao, M., Ding, F., Fu, J., & Li, M. (2018). Mapping the transmission risk of Zika virus using machine learning models. Acta tropica, 185, 391-399.
Karako, K., Song, P., Chen, Y., & Tang, W. (2020). Analysis of COVID-19 infection spread in Japan based on stochastic transition model. Bioscience trends.
Koo, J. R., Cook, A. R., Park, M., Sun, Y., Sun, H., Lim, J. T., ... & Dickens, B. L. (2020). Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study. The Lancet Infectious Diseases.
Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., ... & Flasche, S. (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The lancet infectious diseases.
Maskarpour Amiri, M., Nasiri, T., Mehdizadeh, P. (2020). Analysis of thematic clusters and drawing a scientific map of research in the field of Covid-19 in the scientific database of Scopus. Military Medicine, 22 (6): 663-669. [in Persian]
Norouzi Chakoli, A.; Hassanzadeh, M. (2000). Indexed scientific productions of Iran and Islamic countries in the Middle East in WOS (2007-2003. Science. (6), 89-103. [in Persian]
Nowruzi Chakli, H., Noor Mohammadi, H., Norouzi Chakoli, A. (2020). Evaluating the research productivity of Iranian public universities and research institutes in the fields related to expert systems. Journal of Scientometrics, 5 (10), 159-176. [in Persian] doi: 10.22070 / rsci.2017.560
Önder, M. & Saygılı, H. (2018). Artificial Intelligence and Its Reflections on Public Administration. Turkish Administration Journal, 90: 487, (629-668).
Pan, A., Liu, L., Wang, C., Guo, H., Hao, X., Wang, Q., ... & Wu, T. (2020). Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. Jama, 323(19), 1915-1923.
Patil, S. B. (2020). A scientometric analysis of global COVID-19 research based on dimensions' database. Available at SSRN 3631795.
Pourhomayoun, M., & Shakibi, M. (2020). Predicting mortality risk in patients with COVID-19 using artificial intelligence to help medical decision-making. medRxiv.
Ramezani, H., Alipour Hafezi, M., Momeni, E. (2019). Mapping the Scientific Cooperation Network of Research Institutions in the Field of Digital Libraries in Iran. Library and Information Science. (1) 21, 55-99. [in Persian]
Rao, A. S. S., & Vazquez, J. A. (2020). Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine. Infection Control & Hospital Epidemiology, 41(7), 826-830.
Rasmussen, S. A., & Redd, S. C. (2015). Using results from infectious disease modeling to improve the response to a potential H7N9 influenza pandemic. Clinical Infectious Diseases, 60(suppl_1), S9-S10.
Riad, M. H., Sekamatte, M., Ocom, F., Makumbi, I., & Scoglio, C. M. (2019). Risk assessment of Ebola virus disease spreading in Uganda using a two-layer temporal network. Scientific reports, 9(1), 1-17.
Sahoo, S., & Pandey, S. (2020). Evaluating research performance of Coronavirus and Covid-19 pandemic using scientometric indicators. Online Information Review.
Shanafelt, T., Ripp, J., & Trockel, M. (2020). Understanding and addressing sources of anxiety among health care professionals during the COVID-19 pandemic. Jama, 323(21), 2133-2134.
Soam, S. S., Bhasker, B., & Mishra, B. N. (2011). Improved prediction of MHC class I binders/non-binders peptides through artificial neural network using variable learning rate: SARS corona virus, a case study. In Software Tools and Algorithms for Biological Systems (pp. 223-229). Springer, New York, NY.
Toninelli D, Toninelli D, Pinter R, Pedraza P de, Pinter R and Pedraza P de(2015).
Mobile research methods. Ubiquity press. Available from:
https://www.ubiquitypress.com/site/chapters/10.5334/bar.c/
Venna, S. R., Tavanaei, A., Gottumukkala, R. N., Raghavan, V. V., Maida, A. S., & Nichols, S. (2018). A novel data-driven model for real-time influenza forecasting. IEEE Access, 7, 7691-7701.
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., ... & Peng, Z. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. Jama, 323(11), 1061-1069.
Wu, J. T., Leung, K., & Leung, G. M. (2020). Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet, 395(10225), 689-697.
Wynants, L., Van Calster, B., Collins, G. S., Riley, R. D., Heinze, G., Schuit, E., ... & van Smeden, M. (2020). Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. bmj, 369.
Yang, Y., Lu, Q., Liu, M., Wang, Y., Zhang, A., Jalali, N., ... & Fang, L. (2020). Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China. MedRxiv.
Yang, Z., Zeng, Z., Wang, K., Wong, S. S., Liang, W., Zanin, M., ... & He, J. (2020). Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease, 12(3), 165-174.
Zhang, G. L., Khan, A. M., Srinivasan, K. N., August, J. T., & Brusic, V. (2005). Neural models for predicting viral vaccine targets. Journal of bioinformatics and computational biology, 3(05), 1207-1225.
Zhang, P., Chen, B., Ma, L., Li, Z., Song, Z., Duan, W., & Qiu, X. (2015). The large scale machine learning in an artificial society: prediction of the Ebola outbreak in Beijing.
Computational intelligence and neuroscience,
2015. Available from:
https://www.hindawi.com/journals/cin/2015/531650/
Zhao, S., Musa, S. S., Lin, Q., Ran, J., Yang, G., Wang, W., ... & Wang, M. H. (2020). Estimating the unreported number of novel coronavirus (2019-nCoV) cases in China in the first half of January 2020: a data-driven modelling analysis of the early outbreak. Journal of clinical medicine, 9(2), 388.
Zlojutro, A., Rey, D., & Gardner, L. (2019). A decision-support framework to optimize border control for global outbreak mitigation. Scientific reports, 9(1), 1-14.