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Pandemics and flexible lockdowns: In praise of agent-based modeling

  • Paper in the Philosophy of the Biomedical Sciences
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Abstract

Philosophers have recently questioned the methodological status of agent-based modeling. Meanwhile, this methodology has been central to various studies of the COVID-19 pandemic. Few agent-based COVID-19 models are accessible to philosophers for inspection or experimentation. We make available a package for modeling the COVID-19 pandemic and similar pandemics and give an impression of what can be achieved with it. In particular, it is shown that by coupling an agent-based model to a standard optimizer we are able to identify strategies for implementing non-pharmacological interventions that flexibly lower or raise social activity, depending on how the outbreak develops, while balancing various desiderata that cannot be fully satisfied together. The simulation outcomes to be presented testify to the power of agent-based modeling and thereby help to push back against the recent philosophical critique of this methodology.

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Notes

  1. See https://krugman.blogs.nytimes.com/2010/11/30/learned-helplessness/.

  2. The Imperial College model has been criticized in Winsberg et al. (2020), but see Basshuysen and White (2021) for a rejoinder.

  3. The SIR model has many variants that allow for more fine-grained compartmentalizations, such as the SIRD model, which separates the recovered (in the ordinary sense of the word) from the deceased (D), and the SEIR model, which includes a compartment of individuals who have been exposed (E) to the pathogen but are not (yet) sick. For each of these models, there is also a variant that reckons with the possibility that recovered people become susceptible again, although most of the models that have been proposed in the COVID-19 literature so far assume that recovered individuals have long-lasting immunity (Flaxman et al., 2020; Moghadas et al., 2020), an assumption which also underlies other approaches to the COVID-19 pandemic (Eichenberger et al., 2020). (Our own model will rely on this assumption as well.)

  4. The role of such networks has been studied in relation to the spread of measles, influenza, HIV and other sexually transmitted diseases (Klovdahl et al., 2001; Klovdahl et al., 1994; Needle et al., 1995), and recently also in relation to COVID-19 (Block et al., 2020).

  5. The package can be installed via Julia’s package manager; instructions on installation and basic usage are given in the README of the package’s GitHub repository: https://github.com/IgorDouven/COVID.jl. For gentle introductions to Julia, see for instance Kochenderfer and Wheeler (2019) and Douven (2022, Appx. E).

  6. One could consider other types of graphs, or even consider switching from one type to another as part of a mitigation strategy (Block et al., 2020). Experiments we conducted with a number of plausible alternatives yielded results not very different from those to be reported here. Using the Julia package in the Supplementary Materials, readers may want to run their own experiments with different graphs representing different assumptions about the community’s social structure.

  7. A more complicated solution would be to introduce two or even more types of nodes and then model their sizes differently per type. Again, the Supplementary Materials allows interested readers to experiment with this and other variants of our setup. Here, too, our own experiments showed qualitative conclusions to be remarkably robust under different parameter settings and other modeling choices.

  8. The data for Anhui were retrieved from https://github.com/CSSEGISandData/COVID-19, which is the COVID-19 data repository of the Center for Systems Science and Engineering at Johns Hopkins University. It is to be noted that the data have been scaled to match the size of the population in the model.

  9. See https://gateway.euro.who.int/en/indicators/hfa_478-5060-acute-care-hospital-beds-per-100-000/.

  10. Monitoring the state of the pandemic in real time is also known to face various practical challenges (see Gostic et al., 2020). In our simulations, we will ignore these problems.

  11. This may still be overly optimistic. The real maximum may well be closer to .7, as assumed in Tuite et al. (2020).

  12. The size of the population had a purely practical motivation: we had 24 cores available to run the computations on, so that having a population of 24 agents, or a multiple thereof, made for efficient parallel computing. In general, such practical considerations are important when using evolutionary algorithms, which tend to be computationally expensive.

  13. Concretely, this is how the principles work in our case: There are 24 slots to be filled by the parent population of the next generation. We first look whether all agents in the first front fit in. If not (i.e., there are more than 24 agents in the first front), then we select from the first front the 24 agents with greatest crowding distance. If the agents in the first front do all fit into the new parent population and if some slots still remain open then, we look whether all agents in the second front fit into those remaining slots. If not, we select from that front as many agents as there are slots still to be filled, again on the basis of crowding distance. If they do all fit in, we turn to the third front. And so on, until all 24 slots are filled.

  14. I am greatly indebted to two anonymous referees for valuable comments on a previous version of this paper. I am also grateful to the editors for helpful advice.

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    Igor Douven

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Douven , I. Pandemics and flexible lockdowns: In praise of agent-based modeling. Euro Jnl Phil Sci 13, 35 (2023). https://doi.org/10.1007/s13194-023-00541-w

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