Date of publication 09.04.2020

World Data Center «Geoinformatics and Sustainable Development» provides the results of an express study of the spread of a coronavirus pandemic in Kyiv, as one of the most affected regions of Ukraine.

The studies are based on the use of open geocoding data of the addresses of residents of COVID-19 patients and the open data of the Ministry of Health of Ukraine:

Based on the number of addresses of citizens infected with coronavirus (161), researchers have suggested that an average of 2 infected people can live at the same address. Thus, on April 8, 2020, in Kyiv according to unofficial data (web portal, about 320 citizens could be infected, which is slightly different from official figures - 294 (the error does not exceed 8%).

Based on the use of the above data (1), the following surveys have been performed by analysts of the World Data Center «Geoinformatics and Sustainable Development»:

1.    The concentration of patients in the territory of Kyiv was determined  and classification was carried out according to the level of the disease transmission risk, based on the calculation of the density matrix of patients per square km. The buffer zones of 1 km (15 min walking distance) and the zone of risk for others (Fig. 1) were built.


Figure 1. Distribution of patients on the territory of Kyiv (link)

2.    The locations of COVID-19 outbreaks in Kyiv were identified

In this study, the distribution density of infectious points into 10 density classes was used (Fig. 2). The highest density is 6 people per square km on Pechersk. High-risk areas on the right bank were also identified, namely Solomianska Square, Shulyavka, Obolonska Quay, and sector near the Zhuliany Airport. According to researchers, there is an increasing trend in the incidence rate in areas with higher incomes and a significant number of modern buildings, which causes a high concentration of residents in these areas. In these locations, the prevalence rate for COVID-19 is also related to the intensity of people's mobility and the frequency of their business trips and travel. A higher concentration of infected people is observed near transport hubs: airports and train stations.


Figure 2. Concentration of COVID-19 outbreaks in Kyiv (link)

3.    Areas of potential contact with sick people were determined based on the analysis of the total area of the perimeter buffer strip of 1 km wide.

This study made it possible to determine the total territory of potential risk for a population living on a perimeter buffer strip (relative to infected areas) of 1 km wide. The calculations showed that today the total area of these buffer areas, where the contacts of the population with infected people are likely, reaches 23% of the total area of Kyiv and covers about 35% of the city population.

4.    Predictive modeling of the further spread of the pandemic in Kyiv until the end of April 2020 was carried out.

According to open data (), a mathematical model was constructed to calculate the number of people infected with a coronavirus in Kyiv.:


Calculations by the model (2) show that the process of distribution of coronavirus in Kyiv until April 22-23, 2020 (26 days from the date of registration of the first infected person in Kyiv - March 16, 2020) is exponential (there is a daily increase in the number of infected people), and then (38 days from the date of registration of the first infected person in Kyiv - March 16, 2020) it changes to linear (there is no change in the daily increase in the number of infected). Also, after 22-23 April 2020, the balance between the number of lethal cases from the coronavirus disease and the number of recovering persons should change from negative to positive (as it already happened in Italy, Spain, France, and most European countries). The results of computer modeling are shown in Fig. 3 and Table 1.


Figure 3. Forecast of the number of COVID-19 cases in Kyiv

Table 1. Predictive modeling of the number of COVID-19 cases in Kyiv

Date Number of days since the epidemic started The number of COVID-19 cases Predictive modeling on exponential and linear dependencies
16.03.2020 1 2  
17.03.2020 2 2  
18.03.2020 3 2  
19.03.2020 4 2  
20.03.2020 5 3  
21.03.2020 6 3  
22.03.2020 7 9  
23.03.2020 8 29  
24.03.2020 9 29  
25.03.2020 10 31  
26.03.2020 11 34  
27.03.2020 12 47  
28.03.2020 13 76  
29.03.2020 14 82  
30.03.2020 15 102  
31.03.2020 16 107  
01.04.2020 17 134  
02.04.2020 18 160  
03.04.2020 19 180  
04.04.2020 20 195  
05.04.2020 21 225  
06.04.2020 22 234  
07.04.2020 23 253  
08.04.2020 24 294  
09.04.2020 25 318  
10.04.2020 26   394
11.04.2020 27   448
12.04.2020 28   511
13.04.2020 29   582
14.04.2020 30   662
15.04.2020 31   754
16.04.2020 32   859
17.04.2020 33   978
18.04.2020 34   1113
19.04.2020 35   1268
20.04.2020 36   1444
21.04.2020 37   1644
22.04.2020 38
23.04.2020 39
24.04.2020 40   2427
25.04.2020 41   2723
26.04.2020 42   3019
27.04.2020 43   3315
28.04.2020 44   3611
29.04.2020 45   3907
30.04.2020 46   4203
01.05.2020 47   4499
02.05.2020 48   4795

A map of the emergence of new cell zones of coronavirus infection was constructed on the basis of already recorded cases using the probabilistic model of prediction (2). For each cell of the model, the probability of newly infected people is calculated based on an analysis of cases that have fallen within its boundaries and neighborhoods with cells with high infection rates. The bright red color corresponds to the 95% confidence interval. For cells in other classes, the probability is less than 95%. Gray color on the map identifies low-probability areas where the emergence of newly infected people is random, difficult to predict. The distribution of the estimated number of affected persons in the territory of Kyiv is shown in Fig. 4.


Figure 4. Spatial prediction of COVID-19 distribution in Kyiv (link)

A team of researchers at the World Data Center «Geoinformatics and Sustainable Development» cites the results of this study to help Kyiv residents and government officials take more focused and informed action to overcome the coronavirus pandemic as quickly as possible.


© World Data Center
    for Geoinformatics and Sustainable Development
    April 09, 2020