# Causal Loop Diagramming of Socioeconomic Impacts of COVID-19: State-of-the-Art, Gaps and Good Practices

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## Abstract

**:**

## 1. Introduction

## 2. Methods and Scope

## 3. Results

#### 3.1. Research Focus

#### 3.2. Common and Rare Components

_{2}concentration”. Social challenges such as (a lack of) “Trust within communities”, “Crime and violence”, and “Racism” [12], as well as the “Conflicts of interest” [13] appeared in only one paper, correspondingly. The role of vaccines was also highlighted only in two papers (“Development of vaccines”, “Production with promising but not yet certified vaccine”, and “Availability of vaccines” [4] and “Vaccination” [15]), while [4] is the only study which accounts for the role of research institutions (“Research institutes mobilisation”). Some issues that are generally considered important factors for the spread of COVID-19 and its impact, for example, social and economic inequality [35,36], are absent in all reviewed CLDs.

#### 3.3. Basic Network Properties of COVID-19 CLDs

#### 3.4. Major Drivers and Most Impacted Components

#### 3.5. Structural Complexity: Motifs

#### 3.6. Good Practices of Creation and Visualization of CLDs

## 4. Discussion and Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Notes

1 | Here, and in what follows, the names of CLD components in quotation marks are those originally used by their authors in the reviewed publications. |

2 | |

3 | These can be computed as sums of the absolute values of rows and columns of the respective adjacency matrices. |

4 | The 10% threshold is our choice to delimit a group of the most impacting/impacted components from the others. We show this in the distribution plots of the in- and out-degrees for each reviewed CLD (Supplementary Materials Figure S1). |

5 | If several components with the same degree were divided by the top decile, all of them were considered. |

6 | Vester originally classified all components with a high product of in- and out-degrees as critical, thus often including active and passive components. In this review, we emphasize the role of components, which are both systems drivers and indicators, but formally could not be classified as either active or passive. Formally we included components which either (i) have different in- and out-degrees less than top deciles or (ii) have equal in- and out-degrees in the top deciles, and, at the same time, have the product of in-degree and out-degree in the top deciles of the corresponding distributions of in- and out-degrees for each CLD. Vester also considers buffer components which have a low product of in- and out-degrees. These are beyond of scope of our analysis. |

7 | The CLD by [14] does not have any active components fulfilling our criteria. |

8 | Their CLD contained eight standard deviations more of the bidirectional structures than the random networks’ mean. |

9 | A linear transformation of raw data that provides that the mean and the variance of the distribution are 0 and 1, correspondingly. The standard score thus gives the number of standard deviations by which the actual data point is above or below the mean value. |

10 | Table entries marked with “N/A” indicate that the corresponding aspect has been neither explicitly articulated by the authors or the reviewed studies nor it could be identified straightforward by the review authors. |

11 | We assume that the authors of the reviewed CLDs have defined such subsystems a priori classifying components substantially, e.g., economic, social, healthcare, etc. However, it is also possible to recognize subsystems after a CLD has been developed, for example, using graph clustering methods. |

12 | Usually four generic problem archetypes are specified [45]: (i) the underachievement, (ii) relative achievement, (iii) relative control, and (iv) out-of-control. While also being “building blocks” of CLDs containing few components, these are different to motifs discussed in Section 3.5. |

13 | Three studies mention this explicitly, while the CLDs of three more studies have a typical visual appearance, which allowed us to attribute them to this software. |

14 | Analysis of multiple causes and multiple effects (along with detection of feedback loops) for each component of a CLD can be performed using Vensim software (which was used to develop the majority of the reviewed CLDs and is commonly used for this purpose). |

15 | Although six out of eight reviewed CLDs account for time delays for some of the links helping to qualitatively understand the speed of impact propagation, this still does not enable a formal analysis of the modeled systems’ dynamics. |

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**Figure 1.**Average node degree across the reviewed CLDs as a function of their size, i.e., the number of nodes. The blue line represents the estimated linear trend excluding the outlier [12]. The slope is −0.004 with p-value 0.379, and hence the hypothesis that the average degree is independent on the network size cannot be rejected at the significance level at least 99.9%. The mean average degree value is 3.46 ± 0.21.

**Figure 2.**Summary of active and passive components of the systems. Concepts in red circles denote active components aggregated across the reviewed studies, concepts in green circles denote aggregated passive components, and concepts in yellow circles denote aggregated critical hubs.

**Figure 3.**Grey dots represent the standard score9 (z-score) of the number of motifs across 1000 realizations of the randomly generated graphs. The red mark depicts the actually observed indicator standardized in the same manner, so the red mark denotes the number of standard deviations by which the actually observed number of motifs differs from the mean of the distribution.

Authors/CLD ID | Title | Date Published | Type | Reference |
---|---|---|---|---|

(Wicher, 2020) | The COVID-19 case as an example of Systems Thinking usage | 15 March 2020 | Blog | [26] |

(Bradley et al., 2020) | A systems approach to preventing and responding to COVID-19 | 28 March 2020 | Paper in a peer-reviewed journal | [16] |

(Sahin et al., 2020) | Developing a Preliminary Causal Loop Diagram for Understanding the Wicked Complexity of the COVID-19 Pandemic | 18 June 2020 | Paper in a peer-reviewed journal | [12] |

(Bahri, 2020) | The Nexus Impacts of the COVID-19: A Qualitative Perspective | 8 August 2020 | Preprint | [14] |

(Tonnang et al., 2020) | COVID-19 Emergency public health and economic measures causal loops: A computable framework. In COVID-19 | 10 September 2020 | Preprint | [15] |

(Klement, 2020) | Systems Thinking About SARS-CoV-2 | 28 October 2020 | Paper in a peer-reviewed journal | [13] |

(Kontogiannis, 2021) | A qualitative model of patterns of resilience and vulnerability in responding to a pandemic outbreak with system dynamics | 10 November 2020 | Paper in a peer-reviewed journal | [4] |

(Zięba, 2021) | How can systems thinking help us in the COVID-19 crisis? | 8 June 2021 | Paper in a peer-reviewed journal | [2] |

**Table 2.**Network motifs used for analysis. Nodes highlighted with red depict impacting components, nodes highlighted with green depict impacted components. In the cases of bidirectionality and feedback loops, it is assumed that there is no dominant impact in any direction.

# | Motif Name | Motif Description (Following [33]) | Motif Schematic View |
---|---|---|---|

(i) | Bidirectionality | A node impacts and is impacted by another adjacent node | |

(ii) | Multiple causes | Two non-adjacent nodes impact another node, adjacent to both of them | |

(iii) | Multiple effects | A node impacts two adjacent nodes which are non-adjacent between each other | |

(iv) | Indirect effect | A node impacts a non-adjacent node through a third node | |

(v) | Moderated effect | A node impacts an adjacent node both directly and through a third node | |

(vi) | Feedback loop (3 components) | Three adjacent nodes impact each other in one direction, i.e., clockwise, or counterclockwise |

CLD ID | Research Question/Focus |
---|---|

(Wicher, 2020) | “I focused on the media and my role, as an individual, in the COVID-19.” |

(Bradley et al., 2020) | “<…> provide a framework to look beyond the chain of infection and better understand the multiple implications of decisions and (in)actions in face of such a complex situation involving many interconnected factors.” |

(Sahin et al., 2020) | “<…> visualise the complexity in managing the COVID-19 pandemic through a systems lens by identifying the interconnectivity between health, economic, social and environmental aspects.” |

(Bahri, 2020) | “<…> provide readers a qualitative analysis how the COVID-19 may affect our susceptible population, healthcare facilities and economy.” |

(Tonnang et al., 2020) | “<…> envision linkages between the elements of the contagion, healthcare, and the economy, and visualize key components that characterize the whole system.” |

(Klement, 2020) | “<…> try to identify and study system structures and causal loops of the problem at hand, integrating all relevant disciplines within an inter- and transdisciplinary approach.” |

(Kontogiannis, 2021) | “<…> unravel the nexus of social and institutional forces that affect the parameters of ‘system dynamics’ models <…>”; “<…> explore how CLDs, their modular blocks (i.e., system archetypes) and leverage points could be used to model <…> principles of resilience.” |

(Zięba, 2021) | “How do businesses respond to the prolonged exposure to the COVID-19 crisis? What kind of actions are they prone to undertake and what are the drivers of those actions?” |

**Table 4.**Comparative statistics of graph representations of the reviewed CLDs. The CLD highlighted in italics is an outlier in terms of average degree.

CLD ID | $\mathbf{Nodes}\left(\mathit{n}\right)$ | $\mathbf{Links}\left(\mathit{l}\right)$ | $\mathbf{Average}\mathbf{Degree}\left(\frac{2\mathit{l}}{\mathit{n}}\right)$ |
---|---|---|---|

(Wicher, 2020) | 21 | 37 | 3.52 |

(Bradley et al., 2020) | 21 | 34 | 3.24 |

(Sahin et al., 2020) | 38 | 88 | 4.63 |

(Bahri, 2020) | 24 | 42 | 3.50 |

(Tonnang et al., 2020) | 50 | 91 | 3.64 |

(Klement, 2020) | 25 | 42 | 3.36 |

(Kontogiannis, 2021) | 78 | 125 | 3.21 |

(Zięba, 2021) | 17 | 32 | 3.77 |

Mean | 34 | 61 | 3.61 |

Active | Passive | Critical Hubs | ||
---|---|---|---|---|

Out-degree | In the top decile | Any | Not in the top decile | In the top decile |

In-degree | Any | In the top decile | Not in the top decile | In the top decile |

Product of in-degree and out-degree | Any | Any | In the top decile | In the top decile |

Active/passive quotient | >1 | <1 | Any | 1 |

**Table 6.**Design and analysis features of the reviewed CLDs10.

CLD ID | Design Procedure | List of Components, Links, and Feedback Loops | Visualization Features | Software Implementation | Analysis Methods |
---|---|---|---|---|---|

(Wicher, 2020) | Based on an analytical article | N/A | Feedback loops marked | N/A | Feedback loops |

(Bradley et al., 2020) | N/A | N/A | The essential feedback loop is highlighted by color | Vensim | Feedback loops |

(Sahin et al., 2020) | Based on expert workshops | Components | Subsystems highlighted by colored areas; feedback loops marked | Vensim | Feedback loops |

(Bahri, 2020) | Based on data analysis and literature review | N/A | Separate CLDs of subsystems and archetypes; feedback loops marked | Vensim | Feedback loops, system archetypes |

(Tonnang et al., 2020) | Formal description of the development process | Feedback loops | Subsystems highlighted by colored links; feedback loops marked | Vensim | Feedback loops |

(Klement, 2020) | Built upon existing CLD | N/A | Subsystems highlighted by colored areas; feedback loops marked | N/A | Feedback loops |

(Kontogiannis, 2021) | Built upon an existing SIR model and expert interviews | Feedback loops | Separate CLDs of archetypes; archetypes highlighted by color on the main CLD; feedback loops marked | Vensim | Feedback loops, system archetypes |

(Zięba, 2021) | Based on “mental database, observation, and intuitive approach” | N/A | Feedback loops marked | Vensim | Feedback loops |

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**MDPI and ACS Style**

Strelkovskii, N.; Rovenskaya, E.
Causal Loop Diagramming of Socioeconomic Impacts of COVID-19: State-of-the-Art, Gaps and Good Practices. *Systems* **2021**, *9*, 65.
https://doi.org/10.3390/systems9030065

**AMA Style**

Strelkovskii N, Rovenskaya E.
Causal Loop Diagramming of Socioeconomic Impacts of COVID-19: State-of-the-Art, Gaps and Good Practices. *Systems*. 2021; 9(3):65.
https://doi.org/10.3390/systems9030065

**Chicago/Turabian Style**

Strelkovskii, Nikita, and Elena Rovenskaya.
2021. "Causal Loop Diagramming of Socioeconomic Impacts of COVID-19: State-of-the-Art, Gaps and Good Practices" *Systems* 9, no. 3: 65.
https://doi.org/10.3390/systems9030065