Safety Science 134 (2021) 105077 Contents lists available at ScienceDirect Safety Science journal homepage: www.elsevier.com/locate/safety A qualitative model of patterns of resilience and vulnerability in responding to a pandemic outbreak with system dynamics Tom Kontogiannis Department of Production Engineering & Management, Technical University of Crete, Chania, Crete GR 73100, Greece ARTICLE INFO ABSTRACT Keywords: Covid-19 has revealed the fragility of healthcare, government, social support and economic systems. The Covid-19 complexity of interactions and the tight coupling of these systems have become evident during the spread of the Emergencies disease and the evolution of policies that produced unintended migrating effects. Striving for efficiency most System dynamics systems have abandoned resilience principles against disturbances. In this article, systemic risk is viewed within Archetypes a system dynamics framework complemented by a resilience perspective in anticipating threats and making Causal loop diagrams Resilience provisions for resources, responding in the face of uncertainty and foreseeing side-effects, monitoring critical Preparedness indicators to assess progress and learning by doing. Earlier system dynamics models focused on the epidemio­ logical micro-level, hence, losing sight of the wider social and the economic landscape that affect the control of the disease. This article expands this model-based approach by looking into the capacity of the healthcare to pace with the surge of patients, the role of governments in mobilizing individuals and organizations, the diffusion of risk information to the general public and so on. The relationships between the healthcare, government, social support and economic systems are presented with the use of system archetypes and leverage points for over­ coming bottlenecks. Finally, the archetypes are assembled into an overall causal loop diagram that has impli­ cations for policies and behavioral patterns. 1. Introduction capabilities into more efficientsectors, relying on ‘just-in-time’ policies that minimize redundancies and slack, increasing dependence on sup­ The Covid-19 pandemic has brought into the foreground the fragility plies that could be produced cheaper abroad, and tightening task se­ of many fundamental man-made systems. At the level of healthcare, we quences that leave little margin for recovery. This ‘faster-cheaper- have witnessed many disruptions in the capacity of facilities (i.e., labs/ tighter’ cycle of operations has produced highly profitable yet fragile testing, hospital beds), significant delays in supply chains (i.e., protec­ supply lines whose disruptions could have far reaching effects (Hynes tive clothing, medical instruments), and staff shortages (i.e., nurses, et al. 2020). Resilience acknowledges that several disruptions can occur clinicians, support staff) among others. At a wider level, a vicious mix of for which production systems should have the capacity to recover and supply and demand fluctuations gave rise to cascading problems in adapt in order to ensure their survival into the future. The ability to production, financial, and transportation systems. Although most maintain slack resources can diminish the impact of events as well as countries applied swift policies to interrupt the spread of the virus and provoke adaptive responses (Bryce et al., 2020). Resilience focuses on mitigate its impact, huge economic damage has already been done in the capability of systems to anticipate, absorb and recover from a wide terms of loss of production, rising unemployment and income loss range of threats. (Collins et al. 2020). Despite their stringent measures and best efforts to What makes covid-19 different from other pandemics is its appear­ ameliorate economic damage, the pandemic has already produced sig­ ance in a complicated web of interconnected production systems where nificant short-term impacts that are likely to worsen in the near future. small disruptions can cascade in unfamiliar ways. In highly coupled and Striving for efficiency and optimization, healthcare and other sys­ complex systems, problems may manifest quickly or unexpectedly and tems have abandoned resilience principles against disturbances whose could migrate over multiple sectors and social boundaries, making it impact may leave businesses, governments and the public in a weakened difficult for crisis management systems to control the situation and state (Gereff, G. 2020). Optimization meant concentrating industrial sustain business continuity. Early government responses to contain the E-mail address: [email protected]. https://doi.org/10.1016/j.ssci.2020.105077 Received 29 June 2020; Received in revised form 11 September 2020; Accepted 23 October 2020 Available online 10 November 2020 0925-7535/© 2020 Elsevier Ltd. All rights reserved. T. Kontogiannis Safety Science 134 (2021) 105077 pandemic with strict lockdown policies had substantial impact on the that anticipating the incidence of these factors is different than esti­ economy in terms of reduced production, minimum consumer spending, mating their potential impact on infection rates assessed by quantitative growing unemployment and with multiple households on the brink of models. Many studies have argued that when these models are inte­ bankruptcy (Nicola, et al., 2020). grated into the epidemiological models, quantitative models will be in a An important challenge to managing systemic risk is modelling the better position to cover a wide ranges of critical interventions or system as a complex web of individuals and institutions with often leverage points to control the disease (van Bavel et al. 2020; Eaton and different perspectives and sometimes conflicting goals. A resilience Kalichman 2020; West et al., 2020). approach anticipates that many potential threats cannot be sufficiently The first objective of this article has been to unravel the nexus of predicted, nor can their effects be fully comprehended. A resilience social and institutional forces that affect the parameters of ‘system dy­ mindset implies rethinking priorities and especially the sacrifices made namics’ models by means of causal loop diagrams (CLDs) which have the in the face of optimization and efficiency. ‘Systems thinking’ has been ability to uncover the underlying feedback structures and leverage applied to modelling complex systems as networks of actors with points in a system (Bures, 2017; Garrity, 2018). In addition, causal loop emergent interactions and uncontrolled events migrating to other areas modelling has been widely applied in healthcare research (Rees et al., (Richardson, 2005; Rusoja et al., 2018). Systems thinking can become a 2018; Littlejohns et al, 2018). In this sense, it is interesting to examine powerful modelling tool so long as it is complemented with a resilience the capacity of healthcare systems to pace with the surge of patients, the viewpoint. mobilization of economic agents in the production of hospital supplies, The severity of the covid-19 situation has created a large research the role of governments in mobilizing individuals and organizations, the output in terms of quantitative models of the transmission of virus, the diffusion of risk information to the healthcare system and the general recovery and the fatality rates as different suppression and mitigation public and so on. Quantitative models can have significantcontributions policies have been tried out by governments. A similar approach has but they reduce the complexity of the world into a limited number of been adopted by many system dynamics studies in their efforts to make dimensions (Hulme, 2020). So it is worth investing, not only on aspects predictions of the impact of various mitigation strategies in the control of our experience that can be expressed numerically, but also on of the pandemic (Ghaffarzadegan and Rahmandad, 2020; Homer, 2020; exploring more subtle aspects of social reality that are not so easily Struben, 2020; Bordehore et al., 2020; Venkateswaran and Damani, quantifiableand pertain to collective societal responses in the control of 2020). System dynamics models have relied mostly on epidemiological the disease. It is interesting therefore to examine the sorts of leverage and human biology models regarding the rate of infection, the contact points that can be deducted from the integration of a variety of quali­ rate, the availability of medical resources and so on. These parameters tative models. remain hidden until more data become available which provides a basis The second objective of this article has been to use an old ‘systems for refining the models. Hence, quantitative models make use of his­ thinking’ method (i.e. Causal Loop Diagrams, CLDs) and try to model torical trends to make projections about the future. new resilience-based approaches in the control of the disease. Resilience By focusing on the micro-level, however, these quantitative models has commonly been definedas ‘the ability of organizations and systems have lost sight of the wider social and the economic landscape that af­ to recover quickly from difficultiesand attacks and regain their original fects the evolution of the disease. More questions should be addressed to functions’ (Hollnagel, 2011; Woods, 2015). Mitchell et al. (2020) have enhance our understanding of the disease and examine potential in­ provided a summary of resilience principles that could be considered in terventions or ‘leverage points’. Hence, it is important to incorporate