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A Tale of Three Pandemic Models: Lessons Learned for Engagement with Policy Makers Before, During, and After a Crisis

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By Emil Johansson1,2, Vittorio Nespeca3, Mikhail Sirenko4, Mijke van den Hurk5, Jason Thompson6, Kavin Narasimhan7, Michael Belfrage1, 2, Francesca Giardini8, and Alexander Melchior5,9

  1. Department of Computer Science and Media Technology, Malmö University, Sweden
  2. Internet of Things and People Research Center, Malmö University, Sweden
  3. Computational Science Lab, University of Amsterdam, The Netherlands
  4. Faculty of Technology, Policy and Management, Delft University of Technology, The Netherlands
  5. Department of Information and Computing Sciences, Utrecht University, The Netherlands
  6. Transport, Health and Urban Design Research Lab, The University of Melbourne, Australia
  7. Centre for Research in Social Simulation, University of Surrey, United Kingdom
  8. Department of Sociology & Agricola School for Sustainable Development, University of Groningen, The Netherlands
  9. Ministry of Economic Affairs and Climate Policy and Ministry of Agriculture, Nature and Food Quality, The Netherlands

Motivation

Pervasive and interconnected crises such as the COVID-19 pandemic, global energy shortages, geopolitical conflicts, and climate change have shown how a stronger collaboration between science, policy, and crisis management is essential to foster societal resilience. As modellers and computational social scientists we want to help. Several cases of model-based policy support have shown the potential of using modelling and simulation as tools to prepare for, learn from (Adam and Gaudou, 2017), and respond to crises (Badham et al., 2021). At the same time, engaging with policy-makers to establish effective crisis-management solutions remains a challenge for many modellers due to lacking forums that promote and help develop sustained science-policy collaborations. Equally challenging is to find ways to provide effective solutions under changing circumstances, as it is often the case with crises.

Despite the existing guidance regarding how modellers can engage with policy makers e.g. (Vennix, 1996; Voinov and Bousquet, 2010), this guidance often does not account for the urgency that characterizes crisis response. In this article, we tell the stories of three different models developed during the COVID-19 pandemic in different parts of the world. For each of the models, we draw key lessons for modellers regarding how to engage with policy makers before, during, and after crises. Our goal is to communicate the findings from our experiences to  modellers and computational scientists who, like us, want to engage with policy makers to provide model-based policy and crisis management support. We use selected examples from Kurt Vonnegut’s 2004 lecture on ‘shapes of stories’ alongside analogy with Lewis Carroll’s Alice In Wonderland as inspiration for these stories.

Boy Meets Girl (Too Late)

A Social Simulation On the Corona Crisis’ (ASSOCC) tale

The perfect love story between social modellers and stakeholders would be they meet (pre-crisis), build a trusting foundation and then, when a crisis hits, they work together as a team, maybe have some fight, but overcome the crisis together and have a happily ever after.

In the case of the ASSOCC project, we as modellers met our stakeholders too late, (i.e., while we were already in the middle of the COVID-19 crisis). The stakeholders we aimed for had already met their ‘boy’: Epidemiological modellers. For them, we were just one of the many scientists showing new models and telling them that ours should be looked at. Although, for example, our model showed that using a track and tracing-app would not help reduce the rate of new COVID-19 infections (as turned out to be the case), our psychological and social approach was novel for them. It was not the right time to explain the importance of integrating these kinds of concepts in epidemiological models, so without this basic trust, they were reluctant to work with us.

The moral of our story is that not only should we invest in a (working) relationship during non-crisis times to get the stakeholders on board during a crisis, such an approach would be helpful for us modelers too. For example, we integrated both social and epidemiological models within the ASSOCC project. We wanted to validate our model with that used by Oxford University. However, our model choices were not compatible with this type of validation. Had we been working with these types of researchers before a pandemic, we could have built a proper foundation for validation.

So, our biggest lesson learned is the importance of having a good relationship with stakeholders before a crisis hits, when there is time to get into social models and show the advantages of using these. When you invest in building and consolidating this relationship over time, we promise a happily ever after for every social modeler and stakeholder (until the next crisis hits).

Modeller’s Adventures in Wonderland

A Health Emergency Response in Interconnected Systems (HERoS) tale

If you are a modeler, you are likely to be curious and imaginative, like Alice from “Alice’s Adventures in Wonderland.” You like to think about how the world works and make models that can capture these sometimes weird mechanisms. We are the same. When Covid came, we made a model of a city to understand how its citizens would behave.

But there is more. When Alice first saw the White Rabbit, she found him fascinating. A rabbit with a pocket watch which is too late, what could be more interesting? Similarly, our attention got caught by policymakers who wear waistcoats, who are always busy but can bring change. They must need a model that we made! But why are they running away? Our model is so helpful, just let us explain! Or maybe our model is not good enough?

Yes, we fell down deep into a rabbit hole. Our first encounter with a policymaker didn’t result in a happy “yes, let’s try your model out.” However, we kept knocking on doors. How many did Alice try? But alright, there is one. It seems too tiny. We met with a group of policymakers but had only 10 minutes to explain our large-scale data-driven agent-based-like model. How can we possibly do that? Drink from a “Drink me” bottle, which will make our presentation smaller! Well, that didn’t help. We rushed over all the model complexities too fast and got applause, but that’s it. Ok, we have the next one, which will last 1 hour. Quickly! Eat an “Eat me” cake that will make the presentation longer! Oh, too many unnecessary details this time. To the next venue!

We are in the garden. The garden of crisis response. And it is full of policymakers: Caterpillar, Duchess, Cheshire Cat and Mad Hatter. They talk riddles: “We need to consult with the Head of Paperclip Optimization and Supply Management,” want different things: “Can you tell us what will be the impact of a curfew. Hmm, yesterday?” and shift responsibility from one to another. Thankfully there is no Queen of Hearts who would order to behead us.

If the world of policymaking is complex, then the world of policymaking during the crisis is a wonderland. And we all live in it. We must overgrow our obsession with building better models, learn about its fuzzy inhabitants, and find a way to instead work together. Constant interaction and a better understanding of each other’s needs must be at the centre of modeler-policymaker relations.

“But I don’t want to go among mad people,” Alice remarked.

“Oh, you can’t help that,” said the Cat: “we’re all mad here. I’m mad. You’re mad.”

“How do you know I’m mad?” said Alice.

“You must be,” said the Cat, “or you wouldn’t have come here.”

Lewis Carroll, Alice in Wonderland

Cinderella – A city’s tale

Everyone thought Melbourne was just too ugly to go to the ball…..until a little magic happened.

Once upon a time, the bustling Antipodean city of Melbourne, Victoria found itself in the midst of a dark and disturbing period. While all other territories in the great continent of Australia had ridded themselves of the dreaded COVID-19 virus, it was itself, besieged. Illness and death coursed through the land.

Shunned, the city faced scorn and derision. It was dirty. Its sisters called it a “plague state” and the people felt great shame and sadness as their family, friends and colleagues continued to fall to the virus. All they wanted was a chance to rejoin their families and countryfolk at the ball. What could they do?

Though downtrodden, the kind-hearted and resilient residents of Melbourne were determined to regain control over their lives. They longed for a glimmer of sunshine on these long, gloomy days – a touch of magic, perhaps? They turned to their embattled leaders for answers. Where was their Fairy Godmother now?

In this moment of despair, a group of scientists offered a gift in the form of a powerful agent-based model that was running on a supercomputer. This model, the scientists said, might just hold the key to transforming the fate of the city from vanquished to victor (Blakely et al., 2020). What was this strange new science? This magical black box?

Other states and scientists scoffed. “You can never achieve this!”, they said. “What evidence do you have? These models are not to be trusted. Such a feat as to eliminate COVID-19 at this scale has never been done in the history of the world!” But what of it? Why should history matter? Quietly and determinedly, the citizens of Melbourne persisted. They doggedly followed the plan.

Deep down, even the scientists knew it was risky. People’s patience and enchantment with the mystical model would not last forever. Still, this was Melbourne’s only chance. They needed to eliminate the virus so it would no longer have a grip on their lives. The people bravely stuck to the plan and each day – even when schools and businesses began to re-open – the COVID numbers dwindled from what seemed like impossible heights. Each day they edged down…

and down…

and down…until…

Finally! As the clock struck midnight, the people of Melbourne achieved the impossible: they had defeated COVID-19 by eliminating transmission. With the help of the computer model’s magic, illness and death from the virus stopped. Melbourne had triumphed, emerging stronger and more united than ever before (Thompson et al., 2022a).

From that day forth, Melbourne was internationally celebrated as a shining example of resilience, determination, and the transformative power of hope. Tens of thousands of lives were saved – and after enduring great personal and community sacrifice, its people could once again dance at the ball.

But what was the fate of the scientists and the model? Did such an experience change the way agent-based social simulation was used in public health? Not really. The scientists went back to their normal jobs and the magic of the model remained just that – magic. Its influence vanished like fairy dust on a warm Summer’s evening.

Even to this day the model and its impact largely remains a mystery (despite over 10,000 words of ODD documentation). Occasionally, policy-makers or researchers going about their ordinary business might be heard to say, “Oh yes, the model. The one that kept us inside and ruined the economy. Or perhaps it was the other way around? I really can’t recall – it was all such a blur. Anyway, back to this new social problem – Shall we attack it with some big data and ML techniques?”.

The fairy dust has vanished but the concrete remains.

And in fairness, while agent-based social simulation remains mystical and our descriptions opaque, we cannot begrudge others for ever choosing concrete over dust (Thompson et al, 2022b).

Conclusions

So what is the moral of these tales? We consolidate our experiences into these main conclusions:

  • No connection means no impact. If modellers wish for their models to be useful before, during or after a crisis, then it is up to them to start establishing a connection and building trust with policymakers.
  • The window of opportunity for policy modelling during crises can be narrow, perhaps only a matter of days. Capturing it requires both that we can supply a model within the timeframe (impossible as it may appear) and that our relationship with stakeholders is already established.
  • Engagement with stakeholders requires knowledge and skills that might be too much to ask of modelers alone, including project management, communication with individuals without a technical background, and insight into the policymaking process.
  • Being useful only sometimes means being excellent. A good model is one that is useful. By investing more in building relationships with policymakers and learning about each other, we have a bigger chance of providing the needed insight. Such a shift, however, is radical and requires us to give up our obsession with the models and engage with the fuzziness of the world around us.
  • If we cannot communicate our models effectively, we cannot expect to build trust with end-users over the long term, whether they be policy-makers or researchers. Individual models – and agent-based social simulation in general – needs better understanding that can only be achieved through greater transparency and communication, however that is achieved.

As taxing, time-consuming and complex as the process of making policy impact with simulation models might be, it is very much a fight worth fighting; perhaps even more so during crises. Assuming our models would have a positive impact on the world, not striving to make this impact could be considered admitting defeat. Making models useful to policymakers starts with admitting the complexity of their environment and willingness to dedicate time and effort to learn about it and work together. That is how we can pave the way for many more stories with happy endings.

Acknowledgements

This piece is a result of discussions at the Lorentz workshop on “Agent Based Simulations for Societal Resilience in Crisis Situations” at Leiden, NL in earlier this year! We are grateful to the organisers of the workshop and to the Lorentz Center as funders and hosts for such a productive enterprise.

References

Adam, C. and Gaudou, B. (2017) ‘Modelling Human Behaviours in Disasters from Interviews: Application to Melbourne Bushfires’ Journal of Artificial Societies and Social Simulation 20(3), 12. http://jasss.soc.surrey.ac.uk/20/3/12.html. doi: 10.18564/jasss.3395

Badham, J., Barbrook-Johnson, P., Caiado, C. and Castellani, B. (2021) ‘Justified Stories with Agent-Based Modelling for Local COVID-19 Planning’ Journal of Artificial Societies and Social Simulation 24 (1) 8 http://jasss.soc.surrey.ac.uk/24/1/8.html. doi: 10.18564/jasss.4532

Crammond, B. R., & Kishore, V. (2021). The probability of the 6‐week lockdown in Victoria (commencing 9 July 2020) achieving elimination of community transmission of SARS‐CoV‐2. The Medical Journal of Australia, 215(2), 95-95. doi:10.5694/mja2.51146

Thompson, J., McClure, R., Blakely, T., Wilson, N., Baker, M. G., Wijnands, J. S., … & Stevenson, M. (2022). Modelling SARS‐CoV‐2 disease progression in Australia and New Zealand: an account of an agent‐based approach to support public health decision‐making. Australian and New Zealand Journal of Public Health, 46(3), 292-303. doi:10.1111/1753-6405.13221

Thompson, J., McClure, R., Scott, N., Hellard, M., Abeysuriya, R., Vidanaarachchi, R., … & Sundararajan, V. (2022). A framework for considering the utility of models when facing tough decisions in public health: a guideline for policy-makers. Health Research Policy and Systems, 20(1), 1-7. doi:10.1186/s12961-022-00902-6

Voinov, A., & Bousquet, F. (2010). Modelling with stakeholders. Environmental modelling & software, 25(11), 1268-1281. doi:10.1016/j.envsoft.2010.03.007

Vennix, J.A.M. (1996). Group Model Building: Facilitating Team Learning Using System Dynamics. Wiley.

Vonnegut, K. (2004). Lecture to Case College. https://www.youtube.com/watch?v=4_RUgnC1lm8

Johansson,E., Nespeca, V., Sirenko, M., van den Hurk, M., Thompson, J., Narasimhan, K., Belfrage, M., Giardini, F. and Melchior, A. (2023) A Tale of Three Pandemic Models: Lessons Learned for Engagement with Policy Makers Before, During, and After a Crisis. Review of Artificial Societies and Social Simulation, 15 Mar 2023. https://rofasss.org/2023/05/15/threepandemic

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)

Content

The ASSOCC Simulation Model: A Response to the Community Call for the COVID-19 Pandemic

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Amineh Ghorbani1 , Fabian Lorig2 , Bart de Bruin1 , Paul Davidsson2, Frank Dignum3, Virginia Dignum3, Mijke van der Hurk4, Maarten Jensen3, Christian Kammler3, Kurt Kreulen1, Luis Gustavo Ludescher3, Alexander Melchior4, René Mellema3, Cezara Păstrăv3, Loïs Vanhée5, and Harko Verhagen6

1TU Delft, Netherlands, 
2Malmö University, Sweden, 
3Umeå University, Sweden, 
4Utrecht University, Netherlands, 
5University of Caen, France, 
6Stockholm University, Sweden
*

(A contribution to the: JASSS-Covid19-Thread)

Abstract: This article is a response to the call for action to the social simulation community to contribute to research on the COVID-19 pandemic crisis. We introduce the ASSOCC model (Agent-based Social Simulation for the COVID-19 Crisis), a model that has specifically been designed and implemented to address the societal challenges of this pandemic. We reflect on how the model addresses many of the challenges raised in the call for action. We conclude by pointing out that the focus of the efforts of the social simulation community should be less on the data and prediction-based simulations but rather on the explanation of mechanisms and exploration of social dependencies and impact of interventions.

Introduction

The COVID-19 crisis is a pandemic that is currently spreading all over the world. It has already had a dramatic toll on humanity affecting the daily life of billions of people and causing a global economic crisis resulting in deficits and unemployment rates never experienced before. Decision makers as well as the general public are in dire need of support to understand the mechanisms and connections in the ongoing crisis as well as support for potentially life-threatening and far-reaching decisions that are to be made with unknown consequences. Many countries and regions are struggling to deal with the impacts of the COVID-19 crisis on healthcare, economy and social well-being of communities, resulting in many different interventions. Examples are the complete lock-down of cities and countries, appeals to the individual responsibility of citizens, and suggestions to use digital technology for tracking and tracing of the disease spread. All these strategies require considerable behavioural changes by all individuals.

In such an unprecedented situation, agent-based social simulation seems to be a very suitable technique for achieving a better understanding of the situation and for providing decision-making support. Most of the available simulations for pandemics focus either on specific aspects of the crisis, such as epidemiology (Chang et al., 2020) or simplified general agglomerated mechanics (e.g., IndiaSIM). Many models, repurposing existing models that were originally developed for other pandemics such as influenza are mostly illustrative and intend to provide theory exposition (Squazzoni et al., 2020). Although current simulations are based on advanced statistical modelling that enables sound predictions of specific aspects of the disease, they use very limited models of human motives and cultural differences. Yet, understanding the possible consequences of drastic policy measures requires more than statistical analysis such as R0 factor (the basic reproduction number, which denotes the expected number of cases directly generated by one case in a population) or economic variables. Measures impact people and thus need to consider individuals’ needs (e.g., affiliation, control, or self-fulfilment), social networks (norms, relationships), and how these attributes and conditions can quickly change during difficult situations (e.g., need for job and food security, overloaded hospitals, loss of relatives).

In this context we have developed ASSOCC (Agent-based Social Simulation for the COVID-19 Crisis; see Figure 1) as a many-faceted observatory of scenarios. In ASSOCC, we connect the many involved aspects in a cohesive simulation, for helping stakeholders to raise their general awareness on all critical aspects of the problem and especially the dependencies between them. Of course, one can hardly aim to cover a large variety of aspects and have very complete models on each of them. Thus, we strike a balance between broadness of the model and accuracy on all aspects. This simulation delivers a complementary perspective to state of the art disciplinary models. Where most of other simulations offer sharp yet isolated pieces of the image, our approach is valuable for combining the pieces of the puzzle since a specific modelling focus can limit space for debate (ní Aodha & Edmonds, 2017).

The ASSOCC approach puts the human behaviour central as a linking pin between many disciplines and aspects: psychology (needs, values, beliefs, plans), social sensitivity (norms, social networks, work relationships), infrastructures (transportation, supplies), epidemiology (spreading), economy (transactions, bankruptcy), cultural influences and public measures (closing activities, lock-down, social distancing, testing). The already complex model is extended on a daily basis. This is done in a largely modular fashion such that specific aspects can be switched on and off during the runs. This leads to some limitations and also requires re-calibration of variables, but overall it seems worth the effort when looking at the first results of the scenarios we have simulated.

In this article, we aim to share our approach to simulating the COVID-19 pandemic, outline how the building and use of ASSOCC takes up a number of the challenges that were posed in (Squazzoni et al., 2020), and emphasize the potentials of agent-based simulation as method in mastering pandemics.

Figure 1: A screenshot of the Graphical User Interface of the ASSOCC simulation

Figure 1: A screen shot of the Graphical User Interface of the ASSOCC simulation

Introducing the ASSOCC Model

The goal of the ASSOCC simulation model is to integrate different parts of our daily life that are affected by the pandemic in order to support decision makers when trading off different policies against each other. It facilitates the identification of potential interdependencies that might exist and need to be addressed. This is important as different countries, cultures and populations affect the suitability and consequences of measures thus requiring local conditions to be taken into account. The model allows stakeholders to study individual and social reactions to different policies, to explore different scenarios, and to analyse their potential effects.

Figure 2: A screenshot of the base simulation model.

Figure 2: A screen shot of the base simulation model.

How it works

The ASSOCC simulation model is based on a synthetic population that consists of a set of artificial individuals (see Figure 1), each with given needs, demographic characteristics and attitude towards regulations and risks. By having all these agents decide over time what they should be doing, we can analyse their reactions to many different policies, such as total lock-down or voluntary isolation. Agents can move, perceive other agents, and decide on their actions based on their individual characteristics and their perception of the environment. The environment constrains the physical actions of the agents but can also impose norms and regulations on their behaviour. Through interaction, agents can take over characteristics from the other agents, such as becoming infected with COVID-19, or receiving information.

Agents

In the ASSOCC model, there are four types of agents: children, students, workers, and retirees. These types represent different age groups with different socio-demographic attributes, common activities, infection risks and behaviours. Each agent has a health status that represents being infected, symptomatic or asymptomatic contagiousness, and a critical state. Moreover, agents have needs and capabilities as well as personal characteristics such as risk aversion and the propensity to follow the law. Needs of the agent include health, wealth and belonging. They are modelled using the water tank model introduced by Dörner et al. (2006). Agent capabilities capture for instance their jobs or family situations. Agents need a minimum wealth value to survive which they receive by working or through subsidies (or by living together with a working agent). In shops and workplaces, agents trade wealth for products and services. Agents pay tax to a central government that then uses this money for subsidies and the maintenance of public services such as hospitals and schools.

Places

During the simulation, agents can move between different places according to their needs and obligations. Places represent homes, shops, hospitals, workplaces, schools, airports and stations. By assigning agents to homes, different households can be represented: single adults, families, retirement homes, and multi-generational households with children, adults and elderly people. The configuration of households is assumed to have an impact on the spreading of COVID-19 and great differences in household configurations exist between countries. Thus, the distribution of these households can be set in the simulation to analyse the situation in different cities 
or countries.

Policies

Policies describe interventions that can be taken by decision makers such as social distancing, infection and immunity testing or closing of schools and workplaces. Policies have complex effects on health, wealth and well-being of all agents. Policies can be extended in many different ways to provide an experimentation environment for decision makers. It is not only the decision of whether or not to implement certain policies but also the point in time when the policy is implemented that influences its success.

Conceptual Design

The ASSOCC model has been conceptualized based on many theories from various scientific disciplines, including psychology (basic motives and needs (McClelland, 1987; Jerome, 2013)), sociology (Schwartz value system (Schwartz, 2012)), culture (Hofstede’s cultural dimensions (Hofstede et al., 2010)), economy (circular flow of income (Murphy, 1993)), and epidemiology (the SEIR model (Cope et al., 2018)). For the disease model, we looked at the following sources: a case study of a corona time lapse (Xu et al., 2020), a cohort study showing the general time lapse of the disease with and without fatality (Zhou et al., 2020) and the incubation period determined by confirmed cases (Lauer et al., 2020). This theory-driven model, determines the reaction of agents to policies and their physical and social context.

A short description of the conceptual architecture of ASSOCC as well as an overview of the agent architecture are available at the project website.

Tools

The simulation is built in Netlogo (see Figure 2 with a visual interface in Unity (see Figure 1. The Netlogo model can be used as a standalone simulation model. For the scenarios, we use the Unity interface for better visualisation of the simulation. The complete source code is available on Github under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Note that at the time of publication of this article, this is still a beta version of the model, which we are continuously developing. The complete description of the agent-based model using the ODD protocol can as well be found on the ASSOCC website.

Addressing Key Challenges

Having explained the ASSOCC framework, in this section, we explain how our modelling effort addresses the 
challenges raised by Squazzoni et al. (2020).

Like any model, the ASSOCC model cannot be a complete representation of reality and has its own limitations. Yet, we believe that the dimensions of social complexity that we have included provide a promising ground to draw useful insights. As rightfully highlighted by Squazzoni et al. (2020), the quality of a model depends on its purpose, its theoretical assumptions, the level of abstraction, and the quality of data.

The purpose of the ASSOCC model is to illustrate and investigate mechanisms. Through the simulation of scenarios, ASSOCC shows dependencies between human behaviour and the spread of the virus, the economic incentives and the psychological needs of people.

In the next sections we aim to explain how the ASSOCC model addresses the main issues raised in (Squazzoni et al., 2020).

Social Complexity

In order to incorporate pre-existing behavioral attitudes, network effects, social norms and culture that influence people’s response to policy measure, we have built a cross-disciplinary extended team of researchers. We have spent extra time and effort to construct a complex model where social complexity is extensively taken into account. As an example, the Maslow theory for individual needs takes pre-existing behavioral attitudes of individuals into account (Jerome, 2013). By connecting this theory to Schwartz value dimensions (Schwartz, 2012) and connecting these dimensions to the cultural dimensions of Hofstede (Hofstede et al., 2010), we incorporate a whole spectrum of individual biological and social needs all the way to cultural diversity among nations.

Yet, the limitations of ASSOCC are in the richness of each of the societal dimensions. We use some rather simple models, for example, in the economic, culture, social network and transport aspects. We document which choices have been made to indicate which complexities we left out and why they were left out and why we think this does not affect the validity of our results. For example, in the transport dimension we do not distinguish between cars and bikes. We do not need that as we do not have large distances and both cars and bikes can be used as solo transport means. We are aware that there are differences in economic terms and also in values for choosing between the two means of transport, but these aspects are not very relevant for the spread of the virus.

Transparency

Although there is pressure on the community to respond to this crisis and to provide expert judgement, we have not sacrificed the complexity of our model, nor it’s transparency to provide rapid answers. In fact, we have aimed to make our modelling process as transparent as possible. Starting from low level programming code, ASSOCC uses Github repository to make the code publicly available. Besides code documentation, our large scale model makes use of the ODD protocol to make the model transparent at the conceptual level. Additionally, by building the Unity interface layer on the Netlogo model, we aim to connect policy scenarios to the parameter setup of the model, so that policy makers themselves can see how changes to scenarios leads to various outcomes.

By emphasizing that ASSOCC creates simulations of policy scenarios, we step away from giving a particular advice for a “best” policy. Rather we highlight the fundamental questions and priorities that have to be dealt with to choose among various policies. This is done by showing the consequences of the implementation of various scenarios and comparing them. This comparison can for example show how different groups of people are affected economically and health-wise by a policy. The most appropriate policy thus depends on the outcomes that are deemed more desirable.

Data

Given the short time since the outbreak, accurate data on the COVID-19 outbreak suitable for complex agent-based models is not yet available. It is not clear how various cases are defined and how the data is collected. However, in our view, this should not limit our modelling abilities for this much-needed rapid response.

In our view, detailed data is not required to build a useful model. In fact, our model is a ’SimCity’ to study various policy scenarios rather than actual data-driven representation of cities. While we have made sure that our model can show similar patterns to the ones observed in reality for overall validity, small fine-grain data is not included. The data used for the simulation comes from particular epidemiological models, from economic models and from calibration of the model against known, normal situations.

As illustrated in models that were described in (Squazzoni et al., 2020), even models that are calibrated with real-world data fail to capture important aspects such as network effects as these changes are still based on stochastic randomized processes. Therefore, being aware that the current data is not yet available nor reliable, 
we have built our model on strong theoretical basis in order to avoid oversimplification of factors that play important roles in this crisis.

Interface between modelling and policy

As highlighted by Squazzoni et al. (2020), “good pandemic models are not always good policy advice models”. We fully agree with this point, which is central to our modelling efforts. A user-interface has been especially developed in Unity (see Figure 1) to support comprehension of the model by policy makers and to facilitate experimentation. In the Unity interface, one can explore the different parameters of a scenario, see the results of the simulations in graph form and also follow several aspects live through the elements available in the spatial representation of the town. This spatial interface is meant purely for better understanding of the model. We believe that having clarity regarding our modelling goal increases policy makers trust in our insights.

In addition, we have been in close contact with policy makers around the world to, on the one hand, understand their needs and immediate and long-term concerns, and on the other hand, communicate our model’s capabilities in the most concise manner to support their decisions. To date, we have engaged with policy makers in the Netherlands, Italy and Sweden.

Predictive Power

In our interactions with policy makers and other users, we make clear that the ASSOCC platform is not meant for giving detailed predictions, but to support the generation of insights. Such a broad model is best used to indicate dependencies and trends between different aspects of the society. Due to the computing power needed for each agent running the complex reasoning, it is difficult to scale this type of model to more than a few thousand agents, at least in NetLogo. 
The validation of the model can be done through the causal chains that can be followed throughout the model. I.e. certain outcomes can be linked through agent states to certain causes in the environment or the actions of other agents. If these causal chains can be interpreted as plausible stories that can be confirmed by the theories of those respective aspects, it is possible to achieve a certain type of high level validation. So, this is not a validation on data, but validation based on expert opinion.

A second type of validation that can be done on this type of ABM is to make a detailed comparison with established epidemiological models. For instance, we are comparing our simulation with the one used for (Ferretti et al., 2020) in a particular scenario where the effect of using tracking and tracing apps is investigated. By translating the assumptions and parameters very carefully to ASSOCC parameters and comparing the resulting simulations, we can validate the underlying models against more traditional ones and also show possible deviations that might come up and that highlights advantages or lacunas in the ASSOCC model. The results of this comparison will be published jointly by the two groups. Finally, we are calibrating ASSOCC parameters by using statistical data, such as R0, number of deaths, and demographic data as means to improve validity.

Conclusion

In this article, we presented the ASSOCC model as a comprehensive modelling endeavour that aims to contribute to the efforts for managing the COVID-19 crisis. By modelling multiple aspects of the society and interrelating them, we provide insights into the underlying mechanisms in the society that are influenced both by the outbreak as well as policy measures that aim to control it.

Being aware of the challenges, we have aimed to include as much social complexity as possible in the model to avoid biases and oversimplification. At the same time, by being in close contact with policy makers around the world, we have taken the actual needs and considerations into account, while providing a traceable, usable and comprehensible user interface that brings the modelling insights within the reach of policy makers. In our modelling efforts, we have paid extra attention to transparency, providing well-documented and open-source code that can be used by the rest of the simulation community.

All the assumptions, underlying theories and the source code of ASSOCC are available on the project website and on Github. We invite people to use it, give feedback and based on this feedback we continuously improve the model and its parameters. According to the development of the pandemic and the state of discussion, new scenarios will be added as well.

We hope that the ASSOCC model can contribute to handling this crisis in a way that shows the capabilities and usefulness of agent-based modelling.

References

Chang, S. L., Harding, N., Zachreson, C., Cliff, O. M. & Prokopenko, M. (2020). Modelling transmission and control of the covid-19 pandemic in australia. arXiv preprint arXiv:2003.10218 <https://arxiv.org/abs/2003.10218>

Cope, R. C., Ross, J. V., Chilver, M., Stocks, N. P., & Mitchell, L. (2018). Characterising seasonal influenza epidemiology using primary care surveillance data. PLoS computational biology, 14(8), e1006377. doi:10.1371/journal.pcbi.1006377

Dörner, D., Gerdes, J., Mayer, M., & Misra, S. (2006, April). A simulation of cognitive and emotional effects of overcrowding. In Proceedings of the Seventh International Conference on Cognitive Modeling (pp. 92-98). Triest, Italy: Edizioni Goliardiche.

Ferretti, L., Wymant, C., Kendall, M., Zhao, L., Nurtay, A., Abeler-Dörner, L., Parker, M., Bonsall, D. & Fraser, C. (2020). Quantifying sars-cov-2 transmission suggests epidemic control with digital contact tracing. Science,  31 Mar 2020:eabb6936. doi:10.1126/science.abb6936

Hofstede, G., Hofstede, G. J. & Minkov, M. (2010). Cultures and organizations: Software of the mind. revised and expanded 3rd edition. N.-Y.: McGraw-Hill.

Jerome, N. (2013). Application of the Maslow’s hierarchy of need theory; impacts and implications on organizational culture, human resource and employee’s performance. International Journal of Business and Management Invention, 2(3), 39–45.

Lauer, S. A., Grantz, K. H., Bi, Q., Jones, F. K., Zheng, Q., Meredith, H. R., Azman, A. S., Reich, N. G. & Lessler, J. (2020). The incubation period of coronavirus disease 2019 (covid-19) from publicly reported confirmed cases: estimation and application. Annals of internal medicine

McClelland, D. (1987). Human Motivation. Cambridge Univ. Press
Murphy, A. E. (1993). John law and richard cantillon on the circular flow of income. The European Journal of the History of Economic Thought, 1(1), 47–62.

Aodha, L. & Edmonds, B. (2017) Some pitfalls to beware when applying models to issues of policy relevance. In Edmonds, B. & Meyer, R. (eds.) Simulating Social Complexity – a handbook, 2nd edition. Springer, 801-822.

Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., Chappin, É., Borit, M., Verhagen, H., Giardini, F. and Gilbert, N. (2020) Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action. Journal of Artificial Societies and Social Simulation, 23(2):10. <http://jasss.soc.surrey.ac.uk/23/2/10.html>. doi: 10.18564/jasss.4298

Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Liu, S., Zhao, P., Liu, H., Zhu, L. et al. (2020). Pathological findings of covid-19 associated with acute respiratory distress syndrome. The Lancet respiratory medicine, 8(4), 420–422

Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X. et al. (2020). Clinical course and risk factors for mortality of adult inpatients with covid-19 in wuhan, china: a retrospective cohort study. The Lancet, 395(10229), 1054-1062. doi:10.1016/S0140-6736(20)30566-3

Ghorbani, A., Lorig, F., de Bruin, B., Davidsson, P., Dignum, F., Dignum, V., van der Hurk, M., Jensen, M., Kammler, C., Kreulen, K., Ludescher, L. G., Melchior, A., Mellema, R., Păstrăv, C., Vanhée, L. and Verhagen, H. (2020) The ASSOCC Simulation Model: A Response to the Community Call for the COVID-19 Pandemic. Review of Artificial Societies and Social Simulation, 25th April 2020. https://rofasss.org/2020/04/25/the-assocc-simulation-model/

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