Title : Analysis of the dynamics of COVID 19 cases in Kazakhstan, 2020
Abstract:
The first cases of COVID-19 in Kazakhstan were imported on March 13, 2020 to the cities of Almaty and Nur-Sultan from Germany and Italy. After March 20, cases of COVID-2019 began to be registered in other regions of the country.
To assess the spatial patterns of the spread of COVID-19 in various administrative territories of Kazakhstan, we conducted a descriptive cross-sectional study in 14 regions. GIS analysis of confirmed COVID-19 cases was carried out throughout Kazakhstan at the district level. For each district, the number of cases (in the form of dots) was calculated for spatial analysis. Conceptualization of spatial relations (analysis of the average nearest neighbor) was used to evaluate clustering in districts.
This study is aimed at conducting a spatial analysis of the COVID-19 epidemic in Kazakhstan to better understand the current features of the spread of the virus and study its geographical patterns, especially its spatial clustering.
We included in the study 6165 confirmed cases of COVID-19 of which 45.3% were women and 54.7% were men, the average age of the patients was 36.2 ± 16.6 years; 405 of them were children under 14 years old.
83% COVID-19 cases were hospitalized, 0.52% died.
In the period of 14 days before the onset of the disease, 4.25% of people flew by plane, 0.47% - traveled by train, and most of the COVID-19 patients apparently occurred at the place of residence.
The largest number of cases were registered in the south-eastern part of the country (Almaty and Turkistan regions) and in the capital Akmola region.
During the analysis, we found clustering of COVID-19 cases in the regions, while mostly the points of cases are scattered and distributed not randomly.
Clusters of COVID-19 cases were identified in two regions of Kazakhstan: Almaty (Ili, Karasai, Raiymbek, Talgar districts and Almaty city) and Akmola (Arshali, Yerementau and Shortandy districts).
By calculating population density and using this variable to measure its impact on the spread of COVID-19, we have shown that high population density is a risk factor for the spread of COVID-19.
Thus, GIS methods of analyzing cases of infection helps to determine the occurrence of random and regular cases of infection on the territory, which can help in solving tasks to determine priority areas in surveillance and management decision-making.
What will audience learn from your presentation?
- The audience will use GIS method in surveillance for infectious diseases
- How will this help the audience in their job? Is this research that other faculty could use to expand their research or teaching? Does this provide a practical solution to a problem that could simplify or make a designer’s job more efficient? Will it improve the accuracy of a design, or provide new information to assist in a design problem? List all other benefits.
- The presented materials will help in understanding the mechanism of the spread of the epidemic and the development of effective anti-epidemic measures and can be useful in training
