A mathematical model and forecast for the coronavirus disease COVID-19 in Ukraine (М2 vs М4)

(A.B. Alyokhin, B.V. Burkynskyi, A.N. Grabovoi, V.A, Dilenko, N.I. Khumarova)

Forecast Monitor

18/10/2020. No F-239.

https://www.facebook.com/ab.alyokhin

As we have already reported, our working group is currently applying four qualitatively different approaches to forecasting the COVID-19 epidemic, which allows not only to master and compare different forecasting techniques, but also to better understand the nature of the forecasting object.

Now we are working on the form of presenting alternative forecasts as a tool for reflecting a complex, multi-model vision of the forecast perspective. Today we will present two forecasts (forecasts M2 and M4) for a 7-day and 3-week period from October 19th, developed on the basis of the same statistical data of today. We plan to switch to the mode of short-term (weekly, main) and medium-term (three-week, approximate) forecasting, with the beginning of the forecasting period, which coincides with Monday of each week, with an emphasis on the accuracy of forecasting the dynamics of daily indicators.

One rationale for this approach is that forecasts made using different techniques tend to differ from each other in detail. In the case of the announced forecasts, one of them in a certain respect is clearly more, and the other one is obviously less optimistic. Only the actual course of events, however, will show which forecast more accurately predicts the course of events.

The results of forecast calculations are showing in Fig. 1–13 (forecast M2) and Fig. 1–2 — Fig. 13–2 (forecast M4). Further, in the description of the diagrams, the second figure will be omitting.

Fig. 1–4 displayed:

- actual trajectories of the main cumulative indicators of the COVID-19 epidemic in Ukraine for the entire observation period;

- estimated trajectories of these indicators for this, as well as for the 21-day forecast period.

Diagrams Fig. 5–8 reflect:

- actual trajectories of the main daily indicators of the COVID-19 epidemic in Ukraine for the entire observation period.

- estimated trajectories of these indicators for this, as well as for the 21-day forecast period.

The diagrams in Fig. 9–13 depicts:

- actual trajectories of the main synthetic indicators of the epidemic (statistical reproductive number SR0, the average absolute increase in the number of infected, the case fatality rate I (TC), the case fatality rate I (CC), the IP progress indicator) for the entire observation period;

- calculated trajectories of these indicators for this period, as well as for the 21-day forecast period.

Despite certain differences in the forecast dynamics, both of these forecasts united by the extrapolation of negative trends in the development of the epidemiological situation in Ukraine in a short-term perspective.

Sources of statistical data:

https://www.worldometers.info/coronavirus/#countries

https://covid19.rnbo.gov.ua/

Our materials also:

https://www.facebook.com/MATHMODELCOVID19

https://t.me/mathmodelcovid19

Accuracy of our forecasts:

https://www.facebook.com/ab.alyokhin/posts/154698732839697 (Germany)

https://www.facebook.com/ab.alyokhin/posts/142548897388014 (Spain)

https://www.facebook.com/ab.alyokhin/posts/150095069966730 (Italy)

https://www.facebook.com/ab.alyokhin/posts/148450556797848 (USA)

https://www.facebook.com/ab.alyokhin/posts/154364292873141 (Ukraine)

https://www.facebook.com/ab.alyokhin/posts/144983953811175 (France)

https://www.facebook.com/ab.alyokhin/posts/152284093081161 (South Korea)

Publications on case fatality rates and progress indicator

https://www.facebook.com/ab.alyokhin/posts/105684827741088

https://www.facebook.com/ab.alyokhin/posts/106831140959790

https://www.facebook.com/ab.alyokhin/posts/107444734231764

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IMPEER of the NAS of Ukraine

IMPEER of the NAS of Ukraine

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