FORESIGHT COVID-19: IMPACT ON ECONOMY AND SOCIETY
Date of publication 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.
ФОРСАЙТ COVID-19: ВПЛИВ НА ЕКОНОМІКУ І СУСПІЛЬСТВО. В досліджені проаналізовано вплив пандемій свинячого грипу, Ебола і коронавірусу (які відбувалися протягом останніх 12 років) на розвиток світової економіки та глобального суспільства. Показано, що ці пандемії носять циклічний характер з періодом повторення, який приблизно дорівнює п’яти рокам. Вони суттєво впливають на світову економіку, приводячи до розриву економічних ланцюгів і гальмування на кілька місяців або навіть до одного року розвитку економіки і суспільства. Для вивчення впливу пандемії коронавірусу на економіку України розроблено прогнозну математична модель і проведено комп’ютерне моделювання цього явища. Розрахунки за песимістичним і оптимістичним сценаріями дозволили оцінити масштаби людських втрат та часові горизонти наростання і згасання пандемії коронавірусу в Україні. Результати цього дослідження можуть використовуватися владою України для розробки плану дій, спрямованих на упередження пандемії і подолання її наслідків.
1. Impact of mass infectious diseases (pandemics) on the development of economy and society on a global scale (world)
In the last decades, mass infectious diseases (pandemics) have become more frequent, which have begun to affect the health of people, social development, economies of countries and regions of the world. So, in 2008-2009, many countries of the world were affected by swine flu, which developed into the H1N1/09 pandemic. In 2014-2015, West Africa, the United States, and Europe were covered by the Ebola epidemic. The beginning of 2020 was sadly marked by the fastest and most extensive coverage of practically the entire world community by the coronavirus pandemic.
We see that the occurrence of pandemics during the specified time is cyclical with a period of occurrence of approximately equal to 5 years. To analyze the impact of these pandemics on the world economy, we compare them on the time axis with next periodic processes:
- Nikolai Kondratiev’s 40-50 year economic cycles based on changes in the technological structure of society;
- Clement Juglar’s 7-11 year cycles related to the direction of investments in business, and
- the industrial index Dow Jones, reflecting the total capitalization of the 30 largest US companies.
It can be seen from fig. 1 that in 2020-2021, the lowering wave of the 5th cycle of Kondratiev ends, which, with the transition to the next technological structure, switches to the increasing wave of the 6th cycle of Kondratiev. This indicates the objective conditions for a further long-term recovery of the world economy. At the same time, the start of this recovery in the period 2020-2021 is significantly weakened by the breakdown of traditional economic chains as a result of the сoronavirus pandemic, a significant “dispersion” (defocusing) of investments in various businesses (both obsolete and promising), which leads to reaching the next bottom of the Juglar’s cycle and a 30–40% drop in the Dow Jones index. This decline by Juglar should continue for about a year, during which time investments will be redirected to technologies of the 6th way. After exceeding the contribution to the world GDP by more than 5-7% due to the technologies of the 6th way, the global economy should begin to rise both according to Kondratiev and Juglar.
Figure 1. Impact of pandemics on the development of the economy and society
Similar falls in the global economy have been observed before. They arose, inter alia, under the influence of the pandemic influenza and swine flu in 2008-2009 and Ebola in 2014-2015, respectively. But they had a short-term influence on the world economy (from several months to a year), after which its objective development continued with elements of renewal and partial elimination of artificial layers (financial bubbles, pyramids, etc.). By extrapolating the phenomena of 2008–2009 and 2014–2015 to the current situation with the coronavirus pandemic, we can make a predictive judgment that the global economic downturn in 2020 will be deeper than the previous crises, but already in 2021–2022, after overcoming the pandemic and getting rid of obsolete and artificial layers, its renewal and growth will begin in accordance with the objective laws of the economy.
2. Building of a predictive model for analysis of COVID-19 spread in Ukraine
Open data provided by national and international organizations were used to build a predictive model for the spread of COVID-19 in Ukraine:
The main methods underlying the building of the predictive model and the COVID-19 spread calculations in Ukraine were:
- Data Mining;
- Principle of Similarity in Mathematical Modeling;
- Сorrelation Аnalysis;
- Regression Analysis.
Following the principle of similarity in mathematical modeling, to build a predictive model for the spread of COVID-19 in Ukraine, it is necessary to find countries in which the spread of the specified disease is most similar to Ukraine in nature. This was done using the Johns Hopkins University Center for Systems Science and Engineering statistics on COVID-19 for the group of countries with the most characteristic pandemic (Table 1).
To observe the uniformity of the data samples, the first day of observation for each country was set to record the first official case of the disease. The application of the method of correlation analysis shows that the spread of the COVID-19 in the countries listed in Table 1 is similar, but the values of correlation coefficients are highest between Ukraine, Italy and Spain.
Table 1. The correlation matrix of the COVID-19 cases in selected countries
China | France | Italy | Korea, South | Spain | Sweden | Ukraine | US | |
China | 1,000 | 0,923 | 0,706 | 0,755 | 0,578 | 0,525 | 0,823 | 0,882 |
France | 0,923 | 1,000 | 0,504 | 0,703 | 0,408 | 0,361 | 0,625 | 0,913 |
Italy | 0,706 | 0,504 | 1,000 | 0,767 | 0,957 | 0,934 | 0,975 | 0,458 |
Korea, South | 0,755 | 0,703 | 0,767 | 1,000 | 0,831 | 0,775 | 0,777 | 0,687 |
Spain | 0,578 | 0,408 | 0,957 | 0,831 | 1,000 | 0,980 | 0,891 | 0,375 |
Sweden | 0,525 | 0,361 | 0,934 | 0,775 | 0,980 | 1,000 | 0,854 | 0,328 |
Ukraine | 0,823 | 0,625 | 0,975 | 0,777 | 0,891 | 0,854 | 1,000 | 0,566 |
US | 0,882 | 0,913 | 0,458 | 0,687 | 0,375 | 0,328 | 0,566 | 1,000 |
Italy and Spain were selected as prototype countries for Ukraine based on these calculations. In these prototype countries, the nature of the COVID-19 is the closest to Ukraine but the extent of its spread is much higher.
Analysis of the data shows that in Ukraine the first case of COVID-19 infection was registered 32 days later than in Italy and 31 days later than in Spain. Therefore, based on the data on the spread of the COVID-19 in the selected prototype countries, we forecast the number of COVID-19 cases in Ukraine with a delay of approximately 30 days.
3. A mathematical model for predicting the spread of the COVID-19 in Ukraine
The appropriate graphs are build based on the analysis of the dynamics of the number of COVID-19 cases in Italy and Spain (Fig. 2). The spread of the virus was exponential by the 46th day in Italy and the 51st day in Spain and then, after the imposing of rigid quarantine measures in these countries, it changed to linear with different angles.
Figure 2. Dynamics of COVID-19 cases in Italy and Spain
Let us describe these processes with the following mathematical relations:
where N(t) is the number of cases of the disease, t is the number of days from the first day of the disease.
Forecasts of COVID-19 spread in Ukraine can be performed under pessimistic and optimistic scenarios using the mathematical model (1).
The predictive number of lethal cases in Ukraine is calculated taking into account the lethality from the COVID-19 in Italy and Spain. The basis is the ratio of the number of deaths to the total number of cases for each day of the pandemic in these prototype countries. For Ukraine, this figure was calculated based on the similarity principle in mathematical modeling with a corresponding lag (approximately 30 days).
4. Predictive modeling of the COVID-19 spread in Ukraine
4.1. The pessimistic scenario (that can occur with a probability of up to 30%) is to use a mathematical model (1) to calculate the spread of the COVID-19 in Ukraine, which reflects the nature of the pandemic in Italy and Spain, with a delay of approximately 30 days in the development of these processes, but with a smaller scale than in the selected countries-prototypes. Regression analysis tools are used to calculate the disease spread and the number of possible lethal cases in Ukraine based on the data for Italy and Spain. The modeled results are presented in Table. 2, a graphical interpretation is shown in Fig. 3 and 4.
The modeled results for Ukraine show that the function of the rate of change in the number of COVID-19 cases acquires its maximum by 51-52 days from the first registered patient, namely in the second half of April 2020 (April 22-23). After that, the results of the strict quarantine measures in the country will lead to the “breaking” of the previous trend and the rate of growth of the number of cases of the disease should start to decrease (the function of the number of COVID-19 cases changes from exponential to linear). Fig. 5 shows the results of predictive modeling of the number of possible lethal cases from COVID-19 for Ukraine up to May 2, 2020.
Table 2. Results of predictive modeling of the number of COVID-19 cases and possible deaths in Ukraine
Date |
Confirmed cases / |
Increase in number of cases |
Lethal cases / (predictive modeling*) |
Percentage of lethal cases / (predictive modeling*) |
Number of days since first case |
03.03.2020 | 1 | 0 | 1 | ||
04.03.2020 | 1 | 0 | 0 | 2 | |
05.03.2020 | 1 | 0 | 0 | 3 | |
06.03.2020 | 1 | 0 | 0 | 4 | |
07.03.2020 | 1 | 0 | 0 | 5 | |
08.03.2020 | 1 | 0 | 0 | 6 | |
09.03.2020 | 1 | 0 | 0 | 7 | |
10.03.2020 | 1 | 0 | 0 | 8 | |
11.03.2020 | 1 | 0 | 0 | 9 | |
12.03.2020 | 1 | 0 | 0 | 10 | |
13.03.2020 | 3 | 2 | 1 | 0 | 11 |
14.03.2020 | 3 | 0 | 1 | 0 | 12 |
15.03.2020 | 3 | 0 | 1 | 0 | 13 |
16.03.2020 | 7 | 4 | 1 | 0 | 14 |
17.03.2020 | 14 | 7 | 2 | 0 | 15 |
18.03.2020 | 14 | 0 | 2 | 0 | 16 |
19.03.2020 | 16 | 2 | 2 | 0 | 17 |
20.03.2020 | 29 | 13 | 3 | 0 | 18 |
21.03.2020 | 47 | 18 | 3 | 0 | 19 |
22.03.2020 | 73 | 26 | 3 | 0 | 20 |
23.03.2020 | 73 | 0 | 3 | 0 | 21 |
24.03.2020 | 97 | 24 | 3 | 4,5 | 22 |
25.03.2020 | 145 | 48 | 5 | 3,1 | 23 |
26.03.2020 | 196 | 51 | 5 | 1,9 | 24 |
27.03.2020 | 310 | 114 | 5 | 3,0 | 25 |
28.03.2020 | 356 | 46 | 9 | 3,0 | 26 |
29.03.2020 | 475 | 119 | 10 | 2,6 | 27 |
30.03.2020 | 548 | 73 | 13 | 2,5 | 28 |
31.03.2020 | 645 | 97 | 17 | 2,3 | 29 |
01.04.2020 | 794 | 149 | 20 | 2,5 | 30 |
02.04.2020 | 942 | 148 | 23 | 2,4 | 31 |
03.04.2020 | 1096 | 154 | 33 | 3,0 | 32 |
04.04.2020* | 1170* | 74 | 37 | 3,2 | 33 |
05.04.2020 | 1266 | 96 | 44 | 3,5 | 34 |
06.04.2020 | 1389 | 123 | 53 | 3,8 | 35 |
07.04.2020 | 1548 | 159 | 56 | 3,6 | 36 |
08.04.2020 | 1754 | 206 | 79 | 4,5 | 37 |
09.04.2020 | 2020 | 266 | 91 | 4,5 | 38 |
10.04.2020 | 2363 | 343 | 123 | 5,2 | 39 |
11.04.2020 | 2807 | 444 | 154 | 5,5 | 40 |
12.04.2020 | 3381 | 574 | 186 | 5,5 | 41 |
13.04.2020 | 4123 | 742 | 226 | 5,5 | 42 |
14.04.2020 | 5082 | 959 | 264 | 5,2 | 43 |
15.04.2020 | 6322 | 1241 | 354 | 5,6 | 44 |
16.04.2020 | 6921 | 599 | 394 | 5,7 | 45 |
17.04.2020 | 9098 | 2176 | 536 | 5,9 | 46 |
18.04.2020 | 11461 | 2364 | 699 | 6,1 | 47 |
19.04.2020 | 14073 | 2612 | 844 | 6,0 | 48 |
20.04.2020 | 17015 | 2942 | 1038 | 6,1 | 49 |
21.04.2020 | 17481 | 466 | 1119 | 6,4 | 50 |
22.04.2020 | 21915 | 4434 | 1147 | 6,6 | 51 |
23.04.2020 | 26350 | 4434 | 1765 | 6,7 | 52 |
24.04.2020 | 30784 | 4434 | 2124 | 6,9 | 53 |
25.04.2020 | 35218 | 4434 | 2430 | 6,9 | 54 |
26.04.2020 | 39652 | 4434 | 2735 | 6,9 | 55 |
27.04.2020 | 44086 | 4434 | 3087 | 7,0 | 56 |
28.04.2020 | 48520 | 4434 | 3445 | 7,1 | 57 |
29.04.2020 | 52954 | 4434 | 3813 | 7,2 | 58 |
30.04.2020 | 57388 | 4434 | 4189 | 7,3 | 59 |
01.05.2020 | 61822 | 4434 | 4575 | 7,4 | 60 |
02.05.2020 | 66256 | 4434 | 4837 | 7,3 | 61 |
*from 04.04.2020 predicted values
Figure 3. The COVID-19 cases forecast for Ukraine
Figure 4. The forecast of rate of change in the number of COVID-19 cases in Ukraine
Figure 5. The forecast of the number of COVID-19 lethal cases in Ukraine
4.2. According to some experts, the optimistic scenario for Ukraine (with a probability of up to 10%) is the threefold reduction of COVID-19 cases and lethal cases compared to the pessimistic one (Table 2). The arguments of these experts are based on the following:
- higher collective immunity of the population of Ukraine compared to the population of prototype countries, especially based on the hypothesis that older people in Ukraine who at their time received BCG vaccination are more resistant to coronavirus disease;
- the advent of dry and warm weather in Ukraine;
- possible effective disease-control measures of the authorities;
- large-scale consolidation of civil society and national business around the problem of overcoming the pandemic.
Analysts at the World Geoinformatics and Sustainable Development Data Center believe that the most realistic scenario (with a probability of up to 60%) will be the average (by the above parameters) between pessimistic and optimistic scenarios. However, the pessimistic scenario constructed in this study should, in the opinion of the developers, be a baseline for further consideration with a view to planning anti-epidemic measures based on the assumption that it is better to be wrong.
The project team draws attention to the fact that the given results and conclusions are evaluative. The reliability of forecasts is determined by the adequacy of the initial hypotheses and definitions and the reliability of data used. Initiated studies will continue and the results will be made public periodically.
for Geoinformatics and Sustainable Development
April 04, 2020