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Get out of your silos and work together!

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By Peer-Olaf Siebers and Sudhir Venkatesan

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

The JASSS position paper ‘Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action’ (Squazzoni et al 2020) calls on the scientific community to improve the transparency, access, and rigour of their models. A topic that we think is equally important and should be part of this list is the quest to more “interdisciplinarity”; scientific communities to work together to tackle the difficult job of understanding the complex situation we are currently in and be able to give advice.

The modelling/simulation community in the UK (and more broadly) tend to work in silos. The two big communities that we have been exposed to are the epidemiological modelling community, and social simulation community. They do not usually collaborate with each other despite working on very similar problems and using similar methods (e.g. agent-based modelling). They publish in different journals, use different software, attend different conferences, and even sometimes use different terminology to refer to the same concepts.

The UK pandemic response strategy (Gov.UK 2020) is guided by advice from the Scientific Advisory Group for Emergencies (SAGE), which in turn has comprises three independent expert groups- SPI-M (epidemic modellers), SPI-B (experts in behaviour change from psychology, anthropology and history), and NERVTAG (clinicians, epidemiologists, virologists and other experts). Of these, modelling from member SPI-M institutions has played an important role in informing the UK government’s response to the ongoing pandemic (e.g. Ferguson et al 2020). Current members of the SPI-M belong to what could be considered the ‘epidemic modelling community’. Their models tend to be heavily data-dependent which is justifiable given that their most of their modelling focus on viral transmission parameters. However, this emphasis on empirical data can sometimes lead them to not model behaviour change or model it in a highly stylised fashion, although more examples of epidemic-behaviour models appear in recent epidemiological literature (e.g. Verelst et al 2016; Durham et al 2012; van Boven et al 2008; Venkatesan et al 2019). Yet, of the modelling work informing the current response to the ongoing pandemic, computational models of behaviour change are prominently missing. This, from what we have seen, is where the ‘social simulation’ community can really contribute their expertise and modelling methodologies in a very valuable way. A good resource for epidemiologists in finding out more about the wide spectrum of modelling ideas are the Social Simulation Conference Proceeding Programmes (e.g. SSC2019 2019). But unfortunately, the public health community, including policymakers, are either unaware of these modelling ideas or are unsure of how these are relevant to them.

As pointed out in a recent article, one important concern with how behaviour change has possibly been modelled in the SPI-M COVID-19 models is the assumption that changes in contact rates resulting from a lockdown in the UK and the USA will mimic those obtained from surveys performed in China, which unlikely to be valid given the large political and cultural differences between these societies (Adam 2020). For the immediate COVID-19 response models, perhaps requiring cross-disciplinary validation for all models that feed into policy may be a valuable step towards more credible models.

Effective collaboration between academic communities relies on there being a degree of familiarity, and trust, with each other’s work, and much of this will need to be built up during inter-pandemic periods (i.e. “peace time”). In the long term, publishing and presenting in each other’s journals and conferences (i.e. giving the opportunity for other academic communities to peer-review a piece of modelling work), could help foster a more collaborative environment, ensuring that we are in a much better to position to leverage all available expertise during a future emergency. We should aim to take the best across modelling communities and work together to come up with hybrid modelling solutions that provide insight by delivering statistics as well as narratives (Moss 2020). Working in silos is both unhelpful and inefficient.

References

Adam D (2020) Special report: The simulations driving the world’s response to COVID-19. How epidemiologists rushed to model the coronavirus pandemic. Nature – News Feature. https://www.nature.com/articles/d41586-020-01003-6 [last accessed 07/04/2020]

Durham DP, Casman EA (2012) Incorporating individual health-protective decisions into disease transmission models: A mathematical framework. Journal of The Royal Society Interface. 9(68), 562-570

Ferguson N, Laydon D, Nedjati Gilani G, Imai N, Ainslie K, Baguelin M, Bhatia S, Boonyasiri A, Cucunuba Perez Zu, Cuomo-Dannenburg G, Dighe A (2020) Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf [last accessed 07/04/2020]

Gov.UK (2020) Scientific Advisory Group for Emergencies (SAGE): Coronavirus response. https://www.gov.uk/government/groups/scientific-advisory-group-for-emergencies-sage-coronavirus-covid-19-response [last accessed 07/04/2020]

Moss S (2020) “SIMSOC Discussion: How can disease models be made useful? “, Posted by Scott Moss, 22 March 2020 10:26 [last accessed 07/04/2020]

Squazzoni F, Polhill JG, Edmonds B, Ahrweiler P, Antosz P, Scholz G, Borit M, Verhagen H, Giardini F, 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

SSC2019 (2019) Social simulation conference programme 2019. https://ssc2019.uni-mainz.de/files/2019/09/ssc19_final.pdf [last accessed 07/04/2020]

van Boven M, Klinkenberg D, Pen I, Weissing FJ, Heesterbeek H (2008) Self-interest versus group-interest in antiviral control. PLoS One. 3(2)

Venkatesan S, Nguyen-Van-Tam JS, Siebers PO (2019) A novel framework for evaluating the impact of individual decision-making on public health outcomes and its potential application to study antiviral treatment collection during an influenza pandemic. PLoS One. 14(10)

Verelst F, Willem L, Beutels P (2016) Behavioural change models for infectious disease transmission: A systematic review (2010–2015). Journal of The Royal Society Interface. 13(125)

Siebers, P-O. and Venkatesan, S. (2020) Get out of your silos and work together. Review of Artificial Societies and Social Simulation, 8th April 2020. https://rofasss.org/2020/0408/get-out-of-your-silos

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

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Go for DATA

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By Gérard Weisbuch

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

I totally share the view on the importance of DATA. What we need is data driven models and the reference to weather forecasting and data assimilation is very appropriate. This probably implies the establishment of a center for epidemics forecasting similar to Reading in the UK or Météo-France in Toulouse. The persistence of such an institution in “normal times” would be hard to warrant, but its operation could be organised as the military reserve.

Let me stress three points.

  1. Models are needed not only by National Policy makers but by a wide range of decision makers such as hospitals and even households. These meso-scales units face hard problems of supplies: hospitals have to manage the supplies of material, consumables, personnel to face hard to predict demand from patients. The same holds true for households: e.g. how to program errands in view of the dynamics of the epidemics? All the supply chain issues also exist for firms, including the chain of deliveries of consumables to hospitals. Hence the importance of available data provided by a center for epidemics forecasting.
  2. The JASSS call (Flaminio et al. 2020) stresses the importance DATA, but does not provide many clues about how to get them. One can hope that some institutions would provide them, but my limited experience is that you have to dig for them. Do It Yourself is a leitmotiv of the Big Data industry. I am thinking of processing patient records to build models of the disease, or private diaries and tweets to model individual behaviour. One then needs collaboration from the NLP (Natural Language Processing) community.
  3. The public and even the media have a very low understanding of dynamical systems and of exponential growth. We know since D. Kahneman book “Thinking, Fast and Slow” (2011) that we have a hard time reasoning on probabilities for instance, but this also applies to dynamics and exponential. We face situations that mandate different actions at different stage of the epidemics such as doing errands or moving to the country-side for town dwellers. The issue is even more difficult for firms, who have to manage employment. Simple models and experimental cognitive science results should be brought to journalists and the general public concerning these issues, in the style of Kahneman if possible.

References

Kahneman, D., & Patrick, E. (2011). Thinking, fast and slow. Allen Lane.

Squazzoni, Flaminio, Polhill, J. Gareth, Edmonds, Bruce, Ahrweiler, Petra, Antosz, Patrycja, Scholz, Geeske, Chappin, Émile, Borit, Melania, Verhagen, Harko, Giardini, Francesca and Gilbert, Nigel (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

Weisbuch, G. (2020) Go for DATA. Review of Artificial Societies and Social Simulation, 7th April 2020. https://rofasss.org/2020/04/07/go-for-data/

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

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Vision for a more rigorous “replication first” modelling journal

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By David Hales

A proposal for yet another journal? My first reaction to any such suggestion is to argue that we already have far too many journals. However, hear me out.

My vision is for a modelling journal that is far more rigorous than what we currently have. It would be aimed at work in which a significant aspect of the result is derived from the output of a complex system type computer model in an empirical way.

I propose that the journal would incorporate, as part of the reviewing process, at least one replication of the model by an independent reviewer. Hence models would be verified as independently replicated before being published.

In addition the published article would include an appendix detailing the issues raised during the replication process.

Carrying out such an exercise would almost certainly lead to clarifications of the original article such that it would easier to replicate by others and give more confidence in the results. Both readers and authors would gain significantly from this.

I would be much more willing to take modelling articles seriously if I knew they had already been independently replicated.

Here is a question that immediately springs to mind: replicating a model is a time consuming and costly business requiring significant expertise. Why would a reviewer do this?

One possible solution would be to provide an incentive in the following form. Final articles published in the journal would include the replicators as co-authors of the paper – specifically credited with the independent replication work that they write up in the appendix.

This would mean that good, clear and interesting initial articles would be desirable to replicate since the reviewer / replicator would obtain citations.

This could be a good task for an able graduate student allowing them to gain experience, contacts and citations.

Why would people submit good work to such a journal? This is not as easy to answer. It would almost certainly mean more work from their perspective and a time delay (since replication would almost certainly take more time than traditional review). However there is the benefit of actually getting a replication of their model and producing a final article that others would be able to engage with more easily.

Also I think it would be necessary, given the above aspects, to put quite a high bar on what is accepted for review / replication in the first place. Articles reviewed would have to present significant and new results in areas of fairly wide interest. Hence incremental or highly specific models would be ruled out. Also articles that did not contain enough detail to even attempt a replication would be rejected on that basis. Hence one can envisage a two stage review process where the editors decide if the submitted paper is “right” for a full replication review before soliciting replications.

My vision is of a low output, high quality, high initial rejection journal. Perhaps publishing 3 articles every 6 months. Ideally this would support a reputation for high quality over time.

Hales, D. (2018) Vision for a more rigorous “replication first” modelling journal. Review of Artificial Societies and Social Simulation, 5th November 2018. https://rofasss.org/2018/11/05/dh/

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