April, 2020 –

Analysis of risks from quality data is an invaluable resource for swift decision making in situations, whose result hinges on many difficult-to-foresee factors. Read about how they help with managing during a pandemic.

Regulating emergency teams on different levels, including all vested stakeholders, requires not only operational information about the number of infected people, number of people in quarantine, number of free beds in hospitals, condition of medical personnel, securing protective equipment, but also risk models, and their components. It is risk models that help guide resources to minimise the negative impacts of unexpected circumstances in time and in space. The main benefits of risk analysis are probable variable impacts, against which we can react even before they occur.

The hierarchy of preparing risk models
An important factor in risk analysis is to be aware of the hierarchical levels of crisis management. In other words precisely defining, for which level of management a specific analysis is directed at. Statistically calculating the increase in cases in the Slovak Republic, expressed by a graph with presumed number of infected over time, is important for the central crisis staff but difficult to use for managing the safety measures in a district or municipality. Of course it also works the other way around, a detailed risk analysis on the level of a municipality, or of city districts isn’t directly usable during the decision making process of the central crisis staff.
Risk-modelling is a complex process. It is necessary to adapt its purpose to what risks are the subject of analysis. In the context of COVID-19 it can be an analysis of the risk to the population based on their health (status) and age (category). Or it can be an analysis on the impact of the entire socio-economic system of a region, district. In that case it can examine the diversity of incomes in a household, the number of legal entities, the number of self-employed persons, the number of entities working in services, tourism and the like.
In our demonstration of the possibilities in using GIS analysis we used publicly accessible data, therefore the results aren’t intended to support decision-making. If the models were to be extended by other indices calculated from available state data, the results would have value and could be used for a variety of levels of crisis management. We performed the risk analysis using procedures from crisis management and risk management. The analysis is based on accessible data, mainly from data published by the Statistical office of the SR about the age composition in populations of individual municipalities of SR, from accessible geospatial data about transport, including calculating the traffic density created within the EU Open Transport Network project and OpenStreetMap data.
Risk analysis is based on combining the vulnerability and exposure of a population in combination with the susceptibility of an area to the spread of disease on a square network of 250 x 250 meters, so the results are combined even for modelling the spread of disease by IZP.
Analysis of the health vulnerability of a population
Due to the lack of information the main factor in the evaluation was the age structure of a population from the data of the Statistical Office of SR. The data comes from the year 2018, but due to their nature they are sufficient and they do not need to be updated. For the most vulnerable body of the population we chose the age group of 60 and above. We calculated the index of vulnerability as a classified percentage of a given age category relative to the rest of the population within the municipality. The index can be recalculated at any time according to the requirements and guidelines of the crisis staff.
If we had data on the health status of the population, we would recalculate the vulnerability taking this factor into account. Younger people with indicated serious diseases, which in combination with  COVID-19 are particularly threatening, have a higher vulnerability index than a healthy 60-year-old living in a mountain village. If data is available, the calculation of vulnerability can be extended to economic vulnerability, which will be important for directing assistance to restore the socio-economic system after the epidemic subsides. Data from the statistical almanac of the health status of the population is available only for self-governing units, which is the reason they are not usable to the analysis in question. More detailed available census information are out of date, and were therefore not included in the analysis.
Picture Text:  Population at the risk age of 60 years and above
Analysis of the economical vulnerability of the population
The analysis of data, which characterise municipalities from the point of view of economic vulnerability, allows for systematic targeting of measures to mitigate negative trends after an emergency. Above all it will be the migration of populations into cities, the liquidation of small farms, possibly local companies. Among the parameters, that need to be taken into account, is not only unemployment before an emergency, but above all data about the income of a population, the number of employers, types of businesses. An important indicator is also the diversity of incomes in a single household, unfortunately this data isn’t accessible. We recommend that the future census also focuses on obtaining data useful in determining the socio economic stability of a system, including the level of flexibility (resilience) – to what extent they will have the strength to return to their pre-emergency situation.
Picture Text: Unemployment, Percentage of unemployment (less than 3, 3-5, 5-10, 10-15, 15 and more)
Settlement remoteness analysis
It is based on the analysis of all transport networks throughout Slovakia. All the roads were classified based on the calculated traffic density within the project Open Transport Network. Comparing the results with the road class revealed however, that the data about theoretical traffic density aren’t relevant. More accurate approaches could be updated using data from mobile operators or from navigation system owners (eg Google). We calculated the Euclidean distance from roads and classified it into categories based on accessibility (easy accessibility by foot, medium accessibility, accessibility via individual non-motorised means of transport and accessibility via motorised vehicles). Similarly we added the distance to railway stations into the remoteness analysis. The remoteness index can be interpreted as a basis for calculating the expected rate of spread of disease into the statistical models currently in use.
Picture Text: Distance from road networks, Road network: _____ road, distance from roads in m
Analysis of gathering places of the population
This analysis is notably important especially in municipalities, where the high risk age groups are more active and at the same time less informed. In practice it means that the residents ignore necessary face protections, ignore the ban on visiting churches, cemeteries, go shopping, and meet up. Included were also parks and petrol stations. Data about businesses with numbers of employees are only available at the district level, and were therefore not included. In the case of calculations at regional levels they can be incorporated. All the listed factors were taken into account and we calculated the Euclidean distance from these objects. The population gathering index can be interpreted as places, where the spread of viruses can occur easier between residents. Municipalities can monitor and guide their residents in regard to these places.
Picture Text: Assembly index, Border of self-governing regions, Border of villages, Other gatherings
Building density analysis
The final factor is building density, which represents to what extent the inhabitants of houses/living spaces are in vicinity, or in isolation. Especially in residential structures with a lower building density it is the rate at which diseases spread are not predicted to be high. Density was calculated based on the ratio of the built-up area to the total area on elementary units of 250 × 250 meters.
Picture Text: Building density, Municipality boundaries, Traffic density, 2000 and more, Percentage of built up areas
Index of the degree of threat of spreading COVID-19
Index of the threat of spreading the virus COVID-19 among the population was finally calculated using a combination of the index of remoteness and the index of gathering places of the population. The scales between them were set in the ratio of 60:40 for the index of building density to/vs the other indices of remoteness and gatherings/assemblies. The index can also be interpreted as possible centers of further incidences of COVID-19 between the population. In the designated areas it is necessary to monitor and test the population.
Risk Analysis
The final analysis was performed as a synthesis of the vulnerability index and spread threat index gradients. The result is a qualitative evaluation of individual municipalities. Despite the fact that the threat indices were calculated on a network of 250 x 250 meters, data about age composition was only available at the municipal level. On the other hand it allows for more focused targeting of safety measures to local governments with attention to possible risks and possibly even the tightening of generally applicable regulations.
Picture Text: Risk analysis of COVID-19 impacts, Border of self-governing regions, Risk levels, Low risk (green), Medium risk (yellow), High risk (orange), Extreme risk (red)
The analysis does not aim to directly invoke safety measures. It was created as an example of the possibilities of geospatial analysis that can be made with data with the goal to support decision-making in emergency situations, and to do so for crisis staffs of different hierarchical levels. If more detailed data was available it is possible to refine the analysis, but also to expand it and analyze the necessary safety precautions of medical staff and materials.
By expanding with the current incidents of infection, with databases about tested persons with negative results, or maybe with databases about persons in quarantine, they will allow the calculations of even more targeted outputs, that can help operational management and ultimately reduce the negative impact of the present situations.
All the data, outputs and algorithms and configured in an analytical system, which allows you to modify the calculation model based on the needs and requirements, dynamically refine the outputs by adding more detailed data, and create outputs for different scales, or aggregate them into hierarchically higher units.
YMS, a.s., www.yms.sk. The analysis was carried out in accordance with risk assessment standards. Individual indices and classifications are calculated on the basis of professional experience and estimates of the author’s team. The analysis and its results are an example of the possibilities of geospatial analysis as supporting tools for the operational management of crisis staffs. To calculate the usable outputs with a guaranteed degree of accuracy, it is necessary to provide more accurate data from state institutions, or to extend the model with other indices according to the knowledge of professional bodies.