Preview

FARMAKOEKONOMIKA. Modern Pharmacoeconomic and Pharmacoepidemiology

Advanced search

COVID-19 in Moscow: prognoses and scenarios

https://doi.org/10.17749/2070-4909.2020.13.1.43-51

Full Text:

Abstract

Aim: to present a mathematical model of the development of COVID-19 in Moscow along with the analysis of some scenarios of epidemic control and possible epidemic consequences.

Materials and Methods. The modeling of the epidemics was based on the extended SEIR model proposed lately in the group of Prof. R. Neher and realized as a freely available software program. The authors based the choice of the parameters of modeling on published data on the epidemiological properties of the novel coronavirus SARS-CoV-2 and open access data on the registered cases of COVID-19 in Moscow for 8-27 March 2020.

Results. Five potential scenarios of the development of COVID-19 epidemics are studied. The scenarios are differed by the levels of the control measures: Null Scenario corresponded to the lack of protective measures, Scenario A – mild measures of the epidemic control (closing of schools and universities, recommendations for senior citizens to stay inside), Scenario B – medium level of control (closing of all public places, recommendation for the citizens to stay inside), Scenarios C and D – complete lockdown (from the beginning of May 2020 within Scenario C and from the beginning of April 2020 within Scenario D). It was shown that within the Null Scenario, the lethality from the novel coronavirus in Moscow will exceed 100 thousand people, and the number of critically ill patients on the peak of the epidemics will exceed the capacities of the system of healthcare. Scenarios A and B did not provide for a radical decrease in the fatality rate, and the number of critically ill patients at the peak of epidemics will still exceed the capacities of the system of healthcare. Besides, within Scenario B, the epidemics will last for more than a year. Scenarios C and D will allow for the control of epidemics and a significant decrease in the rate of letha lity (by 30 and 400 times, respectively). At the same time, these two scenarios prevent the population from developing herd immunity, which would result in the population susceptibility to repeated epidemics outbreaks.

Conclusion. The scenarios intended for the slow development of herd immunity in the conditions of epidemic control would not bring sufficient results: the lethality would remain unacceptably high, the capacities of the system of healthcare would be overloaded, and the time of limiting measures would be unacceptably long. Such measures as complete lockdown would stop the present epidemics. The earlier they are introduced, the more efficient will be the results. To prevent further repeated outbreaks of the epidemics, it is necessary to establish a system of available, quick, and efficient testing in combination with point isolation of the infected patients and their contacts. 

About the Author

M. V. Tamm
Moscow State University ; HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM NRU HSE)
Russian Federation

Mikhail V. Tamm  – PhD (Physico-Mathematical Sciences)

 Researcher ID: G-6959-2016; Scopus Author ID: 7006098030

1 Leninskie gory, Moscow 119991; Tallinskaya Str., 34, Moscow 123458



References

1. Tough quarantine measures have been introduced in Moscow. This seems to be correct: the mathematical model shows that otherwise more than 100 thousand people could have died. 30.03.2020. [Electronic resource] URL: https://meduza.io/ feature/2020/03/30/v-moskve-vveli-zhestkie-karantinnye-merypohozhe-eto-pravilno-matematicheskaya-model-pokazyvaet-chtoinache-mogli-by-pogibnut-bolshe-100-tysyach-chelovek. Accessed: 30.03.2020.

2. Kermack W.O., McKendrick A.G. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A. Containing papers of a mathematical and physical character. 1927; 115 (772): 700-721.

3. Daley D.J., Gani J. Epidemic Modelling: An Introduction, Cambridge University Press, Cambridge, UK, 1999.

4. Diekmann O., Heesterbeek J.A.P. Mathematical epidemiology of infectious diseases: model building, analysis and interpretation, John Wiley& Sons, Chichester, UK, 2000.

5. Murray J.D., Mathematical Biology: I. An Introduction, SpringerVerlag, New York, NY, 2002.

6. Keeling M.J., Rohani P. Modelling Infectious Diseases in Humans and Animals, Princeton University Press, Princeton, NJ, 2008.

7. Bjørnstad O. SEIR model. 2005; [Electronic resource] URL: https://ms.mcmaster.ca/~bolker/eeid/sir.pdf. Accessed: 27.03.2020.

8. Rocklow J., Sjodin H., Wilder-Smith A. COVID-19 outbreak on the Diamond Princess cruise ship: estimating the epidemic potential and effectiveness of public health countermeasures. J. Travel Medicine. 2020; DOI: 10.1093/jtm/taaa030.

9. Peng L., Yang W., Zhang D., Zhuge C., Hong L. Epidemic analysis of COVID-19 in China by dynamical modeling. arXiv preprint. 2020; arXiv:2002.06563.

10. COVID-19 reports of the MRC Centre for Global Infectious Disease Analysis, Imperial College London. [Electronic resource] URL: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/ covid-19/. Accessed: 27.03.2020.

11. Maslov S., Goldenfeld N. Window of Opportunity for Mitigation to Prevent Overflow of ICU capacity in Chicago by COVID-19. arXiv preprint. 2020; arXiv:2003.09564.

12. COVID-19 Scenarios. [Electronic resource] URL: https:// neherlab.org/covid19/. Accessed: 27.03.2020.

13. Lauer S.A., Grantz K.H., Bi Q., Jones F.K., Zheng Q. et al., The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine, 2020; DOI: 10.1101/2020.02.02.20020016.

14. Chan J. F.-W., Yuan S., Kok K.-H., To K. K.-W., Chu H. et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. The Lancet. 2020; 395: 514.

15. Wu J.T., Leung K., Leung G.M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet. 2020; 395: 689.

16. Kucharski A.J., Russell T.W., Diamond C., Liu Y., Edmunds J. et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet. Infectious Diseases. 2020; DOI: 10.1016/S1473-3099(20)30144-4.

17. Liu T., Hu J., Xiao J., He G., Kang M. et al. Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China. bioRxiv.org preprint. DOI: 10.1101/2020.01.25.919787.

18. Wu Z., McGoogan J.M. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China. J. Amer. Med. 2020; DOI: 10.1001/jama.2020.2648.

19. Neher R.A., Dyrdack R., Druelle V., Hodcroft E.B., Albert J. Potential impact of seasonal forcing on a SARS-CoV-2 pandemic. Swiss Med. Weekly. 2020; 150: w20224. DOI: 10.4414/ smw.2020.20224.

20. Liu Y., Gayle A.A., Wilder-Smith A., Rocklow J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J. Travel Med. 2020; DOI: 10.1093/jtm/taaa021.

21. Flaxman S., Mishra S., Gangy A., Unwin H.J.T., Coupland et al. Estimating the number of infections and the impact of nonpharmaceutical interventions on COVID-19 in 11 European countries. 13th report of the Imperial College COVID-19 Response Team. 30 March 2020; DOI: 10.25561/77731.

22. Number of resident population – men by age on January 1 [Electronic resource] URL: https://www.fedstat.ru/indicator/31548. Accessed: 27.03.2020.

23. The number of resident population – women by age on January 1. [Electronic resource] URL: https://www.fedstat.ru/indicator/33459. Accessed: 27.03.2020.

24. Wang C., Liu L., Hao X., Guo H., Wang Q. et al. Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China. medRxiv.org preprint. DOI: 10.1101/2020.03.03.20030593


For citation:


Tamm M.V. COVID-19 in Moscow: prognoses and scenarios. FARMAKOEKONOMIKA. Modern Pharmacoeconomic and Pharmacoepidemiology. 2020;13(1):43-51. (In Russ.) https://doi.org/10.17749/2070-4909.2020.13.1.43-51

Views: 451


ISSN 2070-4909 (Print)
ISSN 2070-4933 (Online)