Assessing resilience of healthcare infrastructure exposed to COVID-19: emerging risks, resilience indicators, interdependencies and international standards

Abstract

In the moment of preparation of this paper, the world is still globally in grip of the Corona (COVID-19) crisis, and the need to understand the broader overall framework of the crisis increases. As in similar cases in the past, also with this one, the main interest is on the "first response". Fully appreciating the efforts of those risking their lives facing pandemics, this paper tries to identify the main elements of the larger, possibly global, framework, supported by international standards, needed to deal with new (emerging) risks resulting from threats like Corona and assess the resilience of systems affected. The paper proposes that future solutions should include a number of new elements, related to both risk and resilience. That should include broadening the scope of attention, currently focused onto preparation and response phases, to the phases of "understanding risks", including emerging risks, and transformation and adaptation. The paper suggests to use resilience indicators in this process. The proposed approach has been applied in different cases involving critical infrastructures in Europe (energy supply, water supply, transportation, etc., exposed to various threats), including the health system in Austria. The detailed, indicator-based, resilience analysis included mapping resilience, resilience stress-testing, visualization, etc., showing, already before the COVID-19, the resilience (stress-testing) limits of the infrastructures. A simpler (57 indicator based) analysis has, then been done for 11 countries (including Austria). The paper links these results with the options available in the area of policies, standards, guidelines and tools (such as the RiskRadar), with focus on interdependencies and global standards-especially the new ISO 31,050, linking emerging risks and resilience.

Keywords: COVID-19; Corona; Indicators; Resilience; Risk.

Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interest.

© Springer Science+Business Media, LLC, part of Springer Nature 2020.

Figures

Fig. 1

The world on April 2, 2020: firmly in the grip of COVID-19 (https://www.google.com/maps/d/viewer?mid=1yCPR-ukAgE55sROnmBUFmtLN6riVLTu3&ll=18.683350789325377%2C52.46303051550274&z=2, accessed on 2 Apr 2020)

Fig. 2

The resilience matrix based on five phase and five dimensions as adopted in SmartResilience project (2019)

Fig. 3

Possible outcomes in the case of an infrastructure exposed to an adverse event: between improvement and complete failure, e.g. in the case of COVID-19

Fig. 4

Example of a resilience matrix showing the resilience level in each cell of the matrix (NOT related to any particular case and not to COVID-19; examples of issues and indicators defining the resilience level are given in 4.2)

Fig. 5

Issues measured by indicators, allow to make the bridge between a given, e.g. measured value of an indicator, and the overall, final Resilience Index & Resilience Cube

Fig. 6

Indirect and direct measurement of resilience

Fig. 7

The hierarchical model in method 1

Fig. 8

Flattening of the curve to not overwhelm the healthcare system

Fig. 9

Map of Austria with healthcare providers shown as circles (inset: General Hospital Vienna)

Fig. 10

Test scenario for outpatient care providers: Schematic representation of patient displacement dynamics. a Doctors are represented as nodes (size represents the number of patients treated per year). They are linked if they share patients in the patient sharing network, a (black arrows). The colour represents their current capacity; green means that they have capacity, and red means that they can no longer accept new patients. b Doctor “a” retires at time step 1; his/her patients are distributed to other doctors according to the weights of the links from “a” to “b” and from “a” to “c” (yellow arrows). This, in turn, changes the capacity of the other doctors. c As c has reached its capacity limit (red), he/she must send patients to other doctors (blue arrows from “c” to “b” and “d”). This creates a cascade of patient displacements of size 2

Fig. 11

Snapshot of the hospital assessment tool. Hospitals are shown as circles with colours that indicate their free numbers of beds. The shaded areas give regions of equal time to travel to one specific hospital. The tool allows one to simulate an event in a specific place (specified by the time of day, traffic conditions, and the number of injured people) and computes how long it takes to bring each of those people to a free hospital bed. Inset: example of how a scenario can be implemented. By clicking on a specific location and specifying the number of injured people there, the tool estimates the resulting changes in indicator levels for all hospitals

Fig. 12

Snapshot of the hospital assessment tool. Simulates an event of three concentrations of an adverse event, roughly simultaneous and their impact on possible patient routing to healthcare providers, easily overcoming the normal capacity of the assigned providers

Fig. 13

Main result of the first exercise. For each district of Austria (the “tree” on the right side), the resulting score of the resilience assessment exercise is shown on the scale from 0 (red) to 5 (green)—“routine emergencies” (Table 1)

Fig. 14

The 122 evaluated districts in the healthcare system of Austria

Fig. 15

One of the results of the stress-test (Sardo et al. 2019): Map of Austria showing the upper bound of the resilience indicator for all districts for a very challenging stress-test scenario. Districts coloured in green (red) have a particularly high (low) resilience: that is, critical removal fractions (Fig. 10)—the map shows significant change compared to the scenario in Fig. 13; note that the stress-test scenario was not of the scale of the COVID-19 one

Fig. 16

Countries analysed by the ResilienceTool

Fig. 17

Countries covered by this resilience analysis (“level 0”)

Fig. 18

The resilience phase results (“level 1) per country

Fig. 19

An example of sites putting together full-scale sources (e.g. full reports—“level4”) related to COVID-19 (here: the EU The Health System Response Monitor (HSRM) https://www.c19hsrm.org/searchandcompare.aspx)

Fig. 20

Risk Radar tool applied to the Corona/COVID-19 issue: Web-semantics based analysis identifying the issues of interest and potential new risks, including main sources (on the right); the numbers indicate risks; the vicinity to the centre of the radar (Steinbeis 2020) corresponds to their importance; here the risks resulting from analysis of expert opinions extracted from the course on the risk

Fig. 21

Risk Radar tool applied to the Corona/COVID-19 issue: comparison between press and media (left) and public www sources (right)

Fig. 22

Risk Radar tool applied to the Corona/COVID-19 issue: Web-semantics based analysis identifying the issues of interest and potential new risks, including their sources in www-snapshot made 7 days before Boris Johnson was hospitalized (Steinbeis 2020)

Fig. 23

Visualization of response curves. a An impulse shock of unit size is applied in year t = 2014 to every sector, i, in the USA. In response, the output of each sector is driven from its equilibrium value. b Every line corresponds to one of the 30 largest sectors, ordered according to their susceptibility to the shock (i.e. the area between the response curve and the dotted line that represents the equilibrium value). The sectors with the largest impact are public administration, real estate activities, human health, and wholesale trade. On the other end of the scale one finds the construction sector, that after the initial shock profits from the disruptive event. Depending on the sector, full economic recovery might take up to 6–10 years (Klimek et al. 2019)

Fig. 24

Functionality graphs for three representative aspects of Societal Resilience: Healthcare sector, Financial sector and Society-as-an-Infrastructure. The scenario estimates a time horizon of just over a year, similar to previous outbreaks in 2002 and 2009, and projects one reemergence of COVID-19, as a worst case, given the lack of current data on possible treatments. The GDP-data forecast is from German sources (Der Spiegel 15/2020). The Acceptance level indicates the estimated loss of functionality which is still considered tolerable by society, as defined by the state. Each functionality curve represents its segment as a whole, i.e. Financial systems, including stability indicators as well as debt, recovery programs, etc. Special note is given to Social Infrastructure as it relies often on perceptions and expectations that are not necessarily founded on facts

Fig. 25

The “Resilience landscape” corresponding to the scenario in Fig. 24

Fig. 26

Managing risk and resilience under the umbrella of ISO