# The Assessment of COVID-19 Vulnerability Risk for Crisis Management
## 3 Results and Discussion
### COVID-19 Vulnerability Index in Time  
Based on the Vulnerability Score summaries for selected days, the CVI was calculated. Figure 12 presents the sequence of CVI maps in time. Based on Figure 12a it can be noticed that the federal states: Bremen, Saarland, and Hamburg were classified as high or very high vulnerability risk from the very beginning of the pandemic. This suggests that preventive actions like increasing the number of hospitals beds, preparing field hospitals or restrictions should be considered to ensure public safety in those federal states.  
Figure 12: Sequence of CVI estimated in selected days: (**a**) 4 March 2020 (**b**) 12 May 2020 (**c**) 12 August 2020 (**d**) 12 November 2020 remaining maps available in Figure A3.  
These recommendations, despite the low number of new cases and hospitalisations, were a result of high vulnerability values assigned to static criteria of the listed federal states. The static and dynamic criteria vulnerabilities of selected countries in time are shown in Figure 13.  
The high level of static vulnerability increases the overall level of COVID-19 vulnerability as shown in Figure 13a,c. On the other hand, the low level of static vulnerability decreases the final level of COVID-19 vulnerability. In real life scenario, this will correspond to the situation, where the number of hospitals and hospital beds exceeds the number of potential patients.  
The analysis in the area of Germany, allows us to estimate the number of people endangered at a certain level of COVID-19 vulnerability in time. Results were presented in Table 5. Pursuant to the above it may be concluded that 22,102,833 population of Germany were at risk of very high COVID-19 vulnerability risk and the number of population endangered changes over time.  
Figure 13: The static (**a**) dynamic (**b**) static and (**c**) summary vulnerability risk for each federal state on selected days.  
In this regard, Figure 14c shows the increase in vulnerability caused by post-holiday returns and the re-opening of schools. Figure A4b presents the general decrease in the risk caused by a significant increase in the number of fully vaccinated people. This was followed by another increase in vulnerability Figure A4c.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **4 March** & **12 May** & **12 August** & **12 November** & **12 February** & **12 May** & **12 August** & **22 October** \\  & **2020** & **2020** & **2020** & **2021** & **2021** & **2021** & **2021** & **2021** & **2021** \\ \hline Very Low & 44,717,994 & 33,610,761 & 44,717,994 & 0 & 13,142,063 & 15,671,946 & 44,717,994 & 33,610,761 \\ Low & 15,391,927 & 24,374,711 & 15,391,927 & 44,717,994 & 31,575,931 & 29,046,048 & 9,097,291 & 14,017,604 \\ Medium & 15,273,831 & 17,398,280 & 14,288,686 & 9,097,292 & 17,366,538 & 17,366,538 & 20,583,322 & 26,770,242 \\ High & 7,810,535 & 7,129,840 & 8,114,985 & 22,194,140 & 13,299,220 & 18,577,079 & 6,263,004 & 8,114,985 \\ Very High & 0 & 680,695 & 680,695 & 680,695 & 7,184,861 & 7,810,535 & 2,532,676 & 2,532,676 & 680,695 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of the German population endangered with a certain level of vulnerability over time.  
Figure 14: Vulnerability risk changes over time. (**a**) 4 March 2020–12 May 2020 (**b**) 12 May 2020–12 August 2020 (**c**) 12 August 2020–12 November 2020 (**d**) 12 November 2020–12 February 2021 remaining maps are available in Figure A4.