Mathematical modeling and forecasting of COVID-19 in Moscow and Novosibirsk region
O. I. Krivorotko, S. I. Kabanikhin, N. Yu. Zyatkov, A. Yu. Prikhodko, N. M. Prokhoshin, M. A. Shishlenin

References
1.	Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6, March 21, 2020
2.	M. V. Tamm, “Koronavirusnaya infektsiya v Moskve: prognozy i stsenarii”, FARMAKOEKONOMIKA. Sovremennaya Farmakoekonomika i Farmakoepidemiologiya, 13:1 (2020), 43–51
3.	E. M. Koltsova, E. S. Kurkina, A. M. Vasetsky, Mathematical modeling of the spread of COVID-19 in Moscow and Russian regions, 2004.10118 [q-bio.PE], 2020
4.	A. Zlojutro, D. Rey, L. Gardner, “Optimizing border control policies for global out-break mitigation”, Scientific Reports, 9 (2019), 2216 https://rdcu.be/bniOs
5.	Y. Chen, J. Cheng, Y. Jiang, K. Liu, “A time delay dynamical model for outbreak of 2019-nCoV and the parameter identification”, J. of Inverse and Ill-posed Problems, 28:2 (2020), 243–250
6.	B. Tang, X. Wang, Q. Li, N. L. Bragazzi, S. Tang, Y. Xiao, J. Wu, Estimation of the transmission risk of 2019-nCoV and its implication for public health interventions, SSRN, https://ssrn.com/abstract=3525558
7.	Shi Pengpeng, Cao Shengli, Feng Peihua SEIR Transmission dynamics model of 2019 nCoV coronavirus with considering the weak infectious ability and changes in latency duration, medRxiv, 2020
8.	Kiesha Prem, Yang Liu, Timothy W Russell, Adam J Kucharski, Rosalind M Eggo, Nicholas Davies, Mark Jit, Petra Klepac, “The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study”, The Lancet Public Health, 5:5 (2020), E261–E270
9.	Fan Ru-guo, Wang Yi-bo, Luo Ming, Zhang Ying-qing, Zhu Chao-ping, “SEIR-Based COVID-19 Transmission Model and Inflection Point Prediction Analysis”, J. of University of Electronic Science and Technology of China, 49:3 (2020), 369–374
10.	S. I. Kabanikhin, O. I. Krivorot'ko, “Matematicheskoe modelirovanie epidemii Uhan'skogo koronavirusa COVID-2019 i obratnye zadachi”, Zhurn. vychisl. matematiki i mat. fiziki, 2020, no. 11
11.	Ying Liu, Albert A Gayle, Annelies Wilder-Smith, Joacim Rocklöv, “The reproductive number of COVID-19 is higher compared to SARS coronavirus”, J. of Travel Medicine, 27:2 (2020), taaa021
12.	Liu Xiuli, J. D. Hewings Geoffrey, Wang Shouyang, Qin Minghui, Xiang Xin, Zheng Shan, Li Xuefeng, Modelling the situation of COVID-19 and effects of different containment strategies in China with dynamic differential equations and parameters estimation, medRxiv, 2020
13.	Lijun Pei, “Prediction of numbers of the accumulative confirmed patients (NACP) and the plateau phase of 2019-nCoV in China”, Cognitive Neurodynamics, 14 (2020), 411–424
14.	C. Anastassopoulou, L. Russo, A. Tsakris, C. I. Siettos, “Data-based analysis, modelling and forecasting of the novel Coronavirus (2019-nCoV) outbreak”, Plos One, 2020
15.	Z. Yang, Z. Zeng, K. Wang, S. S. Wong, W. Liang, M. Zanin, P. Liu, X. Cao, Z. Gao, Z. Mai, J. Liang, X. Liu, S. Li, Y. Li, F. Ye, W. Guan, Y. Yang, F. Li, S. Luo, Y. Xie, B. Liu, Z. Wang, S. Zhang, Y. Wang, N. Zhong, J. He, “Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions”, J. Thorac Dis., 12:3 (2020), 165–174
16.	B. Ivorra, M. R. Ferrández, M. Vela-Pérez, A. M. Ramos, “Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China”, Commun Nonlinear Sci Numer Simul., 88 (2020), 105303 (published online ahead of print, 2020 Apr 30)
17.	A. Godio, F. Pace, A. Vergnano, “SEIR modeling of the Italian epidemic of SARS-CoV-2 using computational swarm intelligence”, International Journal of Environmental Research and Public Health, 17 (2020), 3535
18.	J. M. Carcione, J. E. Santos, C. Bagaini, J. Ba, “A Simulation of a COVID-19 Epidemic Based on a Deterministic SEIR Model”, Front. Public Health, 8 (2020), 230
19.	Y. Ding, L. Gao, “An evaluation of COVID-19 in Italy: A data-driven modeling analysis”, Infectious Disease Modelling, 5 (2020), 495–501
20.	Askery Canabarro, Elayne Tenorio, Renato Martins, Lais Martins, Samurai Brito, Rafael Chaves Data-driven study of the COVID-19 pandemic via age-structured modelling and prediction of the health system Failure in Brazil amid diverse intervention strategies, medRxiv, 2020
21.	Taarak Rapolu, Brahmani Nutakki, T. Sobha Rani, S. Durga Bhavani, A time-dependent SEIRD model for forecasting the COVID-19 transmission dynamics, medRxiv, 2020
22.	P. Klepac, L. W. Pomeroy, O. N. Bjørnstad, T. Kuiken, A. D.M. E. Osterhaus, J. M. Rijks, “Stage-structured transmission of phocine distemper virus in the Dutch 2002 outbreak”, Proc Biol Sci, 276 (2009), 2469–2476
23.	P. Klepac, H. Caswell, “The stage-structured epidemic: linking disease and demography with a multi-state matrix approach model”, Theor. Ecol., 4 (2011), 301–319
24.	C. I. Siettos, L. Russo, “Mathematical modeling of infectious disease dynamics”, Virulence, 4:4 (2013), 295–306
25.	Samuel M. Jenness, M. Steven, “Goodreau Martina Morris EpiModel: An R package for mathematical modeling of infectious disease over networks”, J. of Statistical Software, 84:8 (2018)
26.	N. B. Noll, I. Aksamentov, V. Druelle, A. Badenhorst, B. Ronzani, G. Jefferies, J. Albert, R. Neher, COVID-19 Scenarios: an interactive tool to explore the spread and associated morbidity and mortality of SARS-CoV-2, medRxiv, 2020
27.	Statistics and forecast at the regional level https://covid19.biouml.org/COVID-19
28.	Projections Using Machine Learning https://covid19-projections.com/COVID-19
29.	Coronavirus disease 2019 (COVID-19), Situation report, https://covid19.who.int/, May 31, 2020
30.	E. Unlu, H. Leger, O. Motornyi, A. Rukubayihunga, T. Ishacian, M. Chouiten, Epidemic analysis of COVID-19 outbreak and counter-measures in France, medRxiv, 2020
31.	R. Sameni, Mathematical modeling of epidemic diseases; A case study of the COVID-19 coronavirus, 2020, arXiv: 2003.11371
32.	S. I. Kabanikhin, M. A. Shishlenin, “Quasi-solution in inverse coefficient problems”, J. of Inverse and Ill-posed Problems, 16:7 (2008), 707–715
33.	G. Bellu, M. P. Saccomani, S. Audoly, L. D'Angi'o, “DAISY: A new software tool to test global identifiability of biological and physiological systems”, Computer Methods and Programs in Biomedicine, 88:1 (2007), 52–61
34.	A. Raue, V. Becker, U. Klingm-uller, J. Timmer, “Identifiability and observability analysis for experimental design in nonlinear dynamical models”, Chaos, 20 (2010), 045105
35.	O. I. Krivorot'ko, D. V. Andornaya, S. I. Kabanikhin, “Analiz chuvstvitel'nosti i prakticheskaya identifitsiruemost' matematicheskih modelei biologii”, Sibirskii zhurnal industrial'noi matematiki, 23:1 (2020), 107–125
36.	A. Raue, J. Karlsson, M. P. Saccomani, M. Jirstrand, J. Timmer, “Comparison of approaches for parameter identifiability analysis of biological systems”, Bioinformatics, 30:10 (2014), 1440–1448