FORESIGHT COVID-19

The section is devoted to publishing the FORESIGHT COVID-19 project materials of the World Data Center for Geoinformatics and Sustainable Development.

The coronavirus pandemic development scenarios developed by the scientific team of the COVID-19 FORESIGHT project of the World Data Center for Geoinformatics and Sustainable Development at Igor Sikorsky KPI help answering the questions of how the Ukrainians are going to live under the pandemic conditions during the months to come, how the world and Ukraine will change after the pandemic is over, and when can it happen?

These issues are dealt with in the series of scientific studies aimed at predictive modeling and foresight of the said phenomena on a long-term (several years), mid-term (several months), and short-term (5-7 days) time horizons.

The first study performs a systems analysis of the cyclical nature of pandemic occurrences and their impact on the global economy and society in the long-term time frame. We have analyzed the impact of swine flu, Ebola, and coronavirus pandemics (that have occurred over the past 12 years) on the development of the world economy and the global society. These pandemics have been shown to be cyclical with a recurrence period of approximately five years. They have a significant impact on the global economy, leading to the breaking of economic chains and the braking of economic and social development.

By juxtaposing the time axes of the active periods of these pandemics with Mykola Kondratiev’s 40-50-year economic cycles based on changes in the technological paradigm of the society; Clément Juglar’s 7-11-year cycles related to the direction of investments in a business, and the Dow Jones Industrial Index reflecting the total capitalization of 30 largest US companies, we made the following conclusions:

  • in 2020-2021, the lowering wave of the 5th Kondratiev’s cycle ends; which, with the transition to the next technological paradigm, switches to the increasing wave of the 6th K-cycle. This indicates the objective conditions for the further long-term recovery of the world economy;
  • at the same time, the start of this recovery in 2020-2021 is significantly weakened by the disruption of the traditional economic chains as a result of the coronavirus pandemic, a significant “dispersion” (defocusing) of investments in various businesses (both obsolete and promising), which leads to reaching the next bottom of Juglar’s cycle and 30–40% drop in the Dow Jones Index;
  • this decline by Juglar’s should continue for about a year, during which the investments will be redirected to the technologies of the 6th paradigm. Once the contribution of the 6th paradigm technologies into the world GDP exceeds 5-7%, the global economy should begin to rise both according to Kondratiev and Juglar;
  • according to the predictive modeling results, the said positive changes may be seen both globally and in Ukraine already in end-2021.

The first research was carried out based on Intelligent Data Mining, the Principle of Similarity in Mathematical Modeling, the Correlation Analysis, and the Regression Analysis.

 

The second study carried out by the project team is dedicated to the analysis of particularities of development of the coronavirus pandemic in Ukraine in the mid-term time frame (several months) with consideration of its territorial heterogeneity in different regions of the country, in adherence to the quarantine regime, in the percentage of infected doctors compared to other European countries, in a number of performed medical tests, and with an account of other factors.

Based on the correlation and regression analysis and the principle of similarity in mathematical modeling, we determined that on the mid-term time horizon (until end-August 2020), the coronavirus spread process in Ukraine may have the following phases:

  • up to the third ten-day interval of May 2020, the pandemic growth with fluctuating nature is the most probable (linear growth may temporarily shift to exponential and vice versa), which is explained by the worst adherence to the quarantine regime among the European countries, the Europe lowest percentage of performed coronavirus tests, the Europe highest percentage of infected doctors, and certain other adverse factors;
  • the pandemic peak is the most probable during the third ten-day interval of May;
  • slow decline of the coronavirus pandemic may be seen during the warmest season in Ukraine, from end-May to end-August 2020, due to gradual acquisition by the population of collective immunity, improvement of the healthcare system operation, an increase of social responsibility and consciousness of the population;
  • in the autumn-winter period of 2020-2021, the second pandemic wave is possible.

Within the framework of mid-term predictive modeling, the project team studied the regional particularities of the coronavirus pandemic spread, with an account of a considerable territorial heterogeneity of this process in the country’s territory, differences in the population communication, different religious traditions, irregularity of migration flows, regional peculiarities of counteraction to and mitigation of the epidemic, etc.

The team had the task to identify and analyze the trends in numbers of newly infected persons in the regions of Ukraine, the city of Kyiv, and the country as a whole, with consideration of substantial heterogeneity and transiency of the coronavirus’s spread processes, their stochastic nature, and high volatility. We used the methods of technical analysis of time series based on “zigzag” and “supertrend” basic indicators that are used to track the main trends on stock markets that are similar, in their volatility nature, to the coronavirus spread trends. We also applied the geospatial data analysis methods based on the creation of cartographic models through the calculation of spatial density and spread concentration of the number of infected persons, and spatial classification of the Ukrainian regions.

The predictive modeling results allowed assigning all the regions of Ukraine into four groups:

  • with the steady (linear) nature of the pandemic process development (Zhytomyr, Ivano-Frankivsk, Odesa, Rivne, Kharkiv, Chernivtsi, and Mykolaiv regions);
  • with the increasing trend in the number of new daily infection cases (м the city of Kyiv, Vinnytsia, Volyn, Dnipropetrovsk, and Lviv regions);
  • with the decreasing trend in the number of new daily infection cases (Kirovohrad, Zaporizhia, Poltava, Sumy, Kherson, Cherkasy, and Ternopil regions);
  • with a non-determined trend (Donetsk, Luhansk, Mykolaiv, Khmelnytskyi, and Chernihiv regions) due to data insufficient for analysis as of the research date (less than 200 reported infection cases).

 

The short-term (adjustment) predictive modeling is performed by the scientists every five days using a Back Propagation Multilayer Neural Network based on the “sliding window” mechanism with 12 neural network training data points. This research shows that for Ukraine, over the fifteen days (06.05.20 - 20.05.20), the nature of the pandemic development becomes clearer and more linear, and reaches a plateau of (400-550) new daily infection cases. Such nature of the process development may linger until the last ten days of May 2020, when the pandemic peak is likely to occur. After that, the pandemic should start declining (the number of new daily infection cases will be consistently lower than the number of daily recovery cases).

As for the city of Kyiv, over the fifteen days (06.05.20 - 20.05.20), the pandemic development nature is still more transient; its volatility will probably decrease and its character will become more linear in the following week.

The mean absolute percentage error of the carried out forecast does not exceed MAPE = 1.878 for Ukraine and МАРЕ = 2.3% for the city of Kyiv. According to the researchers, with larger volumes of data (as the overall Ukraine data compared to the city of Kyiv), the Back Propagation Neural Network is less sensitive to short-term spikes and surges and allows for lower forecast error. 

The predictive modeling results also show that the number of daily recovery cases in Ukraine approaches the number of daily infection cases. The project team expects that in the third ten-day interval of May 2020 this ratio may become steadily positive, which will represent the beginning of the pandemic decline.

 

During the third decade of May 2020, a new trend of slow attenuation of the coronavirus pandemic began to take shape, when the number of recovered people per day, in general, began to exceed the number of infected. The fourth foresight research of the World Data Center "Geoinformatics and Sustainable Development" is devoted to the study of the features of this (third) phase of slow attenuation of the disease.

 

More than 350 mass media have published the KPI scientists’ research results. The data obtained by the scientists may be crucial for the further decision-making process regarding efficient mitigation of the pandemic aftermath in the country and transformation of the economy and social institutes when the pandemic is over, based on the new reality of Ukraine and the world.   

 

FORESIGHT COVID-19: IMPACT ON ECONOMY AND SOCIETY

04.04.2020

The study analyzes the impact of the Swine flu, Ebola, and Coronavirus pandemics (that have occurred over the past 12 years) on the development of the world economy and global society. These pandemics have been shown to be cyclical with a recurrence period of approximately 5 years. They have a significant impact on the global economy, leading to the breaking of economic chains and the braking of several months or about one year of economic and social development. To study the impact of the coronavirus pandemic on the Ukrainian economy and society, a predictive mathematical model was developed and computer simulations were made. The calculations for the pessimistic and optimistic scenarios made it possible to estimate the extent of human losses and the time horizons for the growth and extinction of the coronavirus pandemic in Ukraine. The results of this study can be used by the Ukrainian authorities to develop a plan of action aimed at preventing a pandemic and overcoming its effects.

 

FORESIGHT COVID-19: THE MIDDLE PHASE OF DEVELOPMENT

01.05.2020

The study analyzes the phases of the process of coronavirus spread in Ukraine on short-term (up to one week) and medium-term (until the end of August 2020) time horizons.

 

 

 

 

 

FORESIGHT COVID-19: REGIONAL CONTEXT

09.05.2020

This study is devoted to the analysis of the spread of the coronavirus pandemic in the regions of Ukraine, taking into account the significant territorial unevenness of this process in the country, differences in communication, different religious traditions, uneven migration flows, regional features of combating and combating the disease etc.

 

 

 

 

FORESIGHT COVID-19: TRANSITION TO THE PHASE OF PANDEMIC ATTENUATION

09.05.2020

During the third decade of May 2020, a new trend of slow attenuation of the coronavirus pandemic began to take shape, when the number of recovered people per day, in general, began to exceed the number of infected. This foresight of the World Data Center "Geoinformatics and Sustainable Development" is devoted to the study of the features of this (third) phase of slow attenuation of the disease.

 

 

 

 

 

FORESIGHT COVID-19 KYIV

09.04.2020

The results of an express study of the spread of a coronavirus pandemic in Kyiv, as one of the most affected regions of Ukraine.

 

 

 

 

SHORT-TERM COVID-19 FORECAST

 

The results of short-term predictive modeling of the number of patients with COVID-19 in Ukraine and Kyiv are obtained using a multilayer Back Propagation Neural Network based on the mechanism "sliding window".

 

 

 

DASHBOARDS

04.04.2020

The interactive web-based Dashboards to track and forecast COVID-19 cases in Ukraine.

The service provides data for Ukraine and Ukrainian regions for the number of laboratory-confirmed COVID-19 cases, fatal cases and recovering patients. The information is presented in the form of maps, charts, counters.

 

Scientific supervisor of the project: Michael Zgurovsky.

Project team: Oleksandr Voytko, Nataliia Gorban, Iryna Dzhygyrey, Bohdan Dudka, Kostiantyn Yefremov, Yuriy Zaychenko, Pavlo Kasyanov, Maria Perestyuk, Іvan Pyshnograiev, Victor Putrenko, Viktor Sineglasov.

 

© World Data Center
    for Geoinformatics and Sustainable Development