Epidemics and diseases

2020-03-10 — 2025-11-20

Wherein contagion mechanics and countermeasures are set forth, and the proposal that elastomeric respirators be stockpiled for critical workers is described as a physics‑based defence against airborne threats.

branching
count data
drugs
health
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networks
SDEs
social graph
statistics
stochastic processes
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Figure 1

A grab-bag of links about disease spread in its filthy glory. I’m particularly examining COVID-19, out of necessity.

Figure 2: Microbescope by David McCandless, Omid Kashan, Miriam Quick, Karl Webster, Dr Stephanie Starling

The spread of diseases in populations. A nitty-gritty, messy empirical application for those abstract contagion models.

Connection with global trade networks: Cosma Shalizi on Ebola and Mongol Modernity.

Figure 3: Buy this from sam.

1 Modelling

I used to know a little about agent-based behavioural epidemic simulation, but I’m no longer in that field and don’t regard myself as a practical expert.

I do know a little more about contagion models.

2 Ameliorations

2.1 Low tech solutions

Andrew Snyder-Beattie, of defensesindepth.bio: The Four Pillars: A Hypothesis for Countering Catastrophic Biological Risk

The authors start from the premise that humanity might be seriously underestimating biological risks, specifically from “unknown unknowns” like engineered pathogens or hypothetical “mirror bacteria.” They argue that our current strategy—making a list of bad bugs and building specific defences for each—is a game of whack-a-mole we’re destined to lose against a creative adversary. Instead, they propose a “generalized” defence strategy that ignores what the bug is and focuses on the physical constraints any pathogen faces, like the need to physically enter a human body, the eponymous four-pillar strategy.

Pillar 1: Personal Protective Equipment (PPE). They aren’t talking about standard surgical masks. They claim the “sweet spot” for cost and protection is the elastomeric respirator —those rubbery half-face masks you might see on a construction site. Their hypothesis is that stockpiling these for critical infrastructure workers (not necessarily everyone) could prevent societal collapse. At bulk-purchase rates these things are very cheap and have a long shelf life, leading to a population-level cost of cents per day; nations could store enough of these to keep the lights on and water running, even if the air outside were teeming with a super-virus.

Pillar 2: “Biohardening,” which is a fancy way of saying we need to make our buildings pathogen-resistant. The post suggests that if a pathogen persists in the environment (like dust or soil), social distancing won’t save us; we’d need actual physical barriers. They toss around ideas like Far-UVC light and advanced air filtration, but the most interesting idea is the potential for DIY biohardening. They suggest that with a guidebook and basic hardware store supplies (like furnace fans and insulation), regular people might be able to turn their homes into positive-pressure cleanrooms within two weeks.

Pillar 3 is detection, specifically “pathogen-agnostic” detection systems — essentially always-on genetic sequencers at airports and wastewater plants looking for anything weird, rather than just specific known viruses. The goal here is to catch ‘stealth’ pathogens that might spread silently for years (like an airborne HIV) before causing symptoms.

The fourth pillar is rapid medical countermeasures (making new drugs fast), but the authors largely gloss over this to focus on the first three, arguing that physical defences are currently the most neglected and actionable.

The authors wrap up with a surprisingly optimistic feasibility claim: they think the first three pillars could be substantially implemented by the end of 2027 for less than $1 billion (it’s not clear who they mean by “we” — the USA?). They argue that because these defences rely on physics (blocking particles) rather than biology (fighting specific proteins), they are “future-proof.” It’s definitely a hypothesis rather than a proven roadmap — they admit they need to stress-test ideas like the DIY cleanrooms — but they present it as a solvable engineering problem rather than a biological guessing game.

2.2 Contact tracing

Contact tracing is its own miniature study.

3 References

Andris, Koylu, and Porter. 2021. Human-Network Regions as Effective Geographic Units for Disease Mitigation.”
Baker, Biazzo, Braunstein, et al. 2021. Epidemic Mitigation by Statistical Inference from Contact Tracing Data.” Proceedings of the National Academy of Sciences.
Barratt, and Aston. 2025. A Nonparametric and Functional Wombling Methodology.”
Bass. 1969. A New Product Growth for Model Consumer Durables.” Management Science.
———. 2004. Comments on ‘A New Product Growth for Model Consumer Durables The Bass Model’.” Management Science.
Berkessel, Ebert, Gebauer, et al. 2022. Pandemics Initially Spread Among People of Higher (Not Lower) Social Status: Evidence From COVID-19 and the Spanish Flu.” Social Psychological and Personality Science.
Braunstein, and Ingrosso. 2016. Inference of Causality in Epidemics on Temporal Contact Networks.” Scientific Reports.
Bretherton, and Dunbar. 2020. Dunbar’s Number Goes to Church: The Social Brain Hypothesis as a Third Strand in the Study of Church Growth.” Archive for the Psychology of Religion.
Epstein. 2007. Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton Studies in Complexity.
———. 2009. Modelling to Contain Pandemics.” Nature.
Ferguson, Neil M., Cummings, Fraser, et al. 2006. Strategies for Mitigating an Influenza Pandemic.” Nature.
Ferguson, N., Laydon, Nedjati Gilani, et al. 2020. Report 9: Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce COVID19 Mortality and Healthcare Demand.” Report.
Halloran, Ferguson, Eubank, et al. 2008. Modeling Targeted Layered Containment of an Influenza Pandemic in the United States.” Proceedings of the National Academy of Sciences.
Hayward. 1999. Mathematical Modeling of Church Growth.” The Journal of Mathematical Sociology.
———. 2005. A General Model of Church Growth and Decline.” The Journal of Mathematical Sociology.
Kimmitt, and Redway. 2016. Evaluation of the Potential for Virus Dispersal During Hand Drying: A Comparison of Three Methods.” Journal of Applied Microbiology.
Kiss, Miller, and Simon. 2017. Mathematics of Epidemics on Networks: From Exact to Approximate Models. Interdisciplinary Applied Mathematics.
Klinkenberg, Fraser, and Heesterbeek. 2006. The Effectiveness of Contact Tracing in Emerging Epidemics.” PLoS ONE.
Madar, Kalisky, Cohen, et al. 2004. Immunization and Epidemic Dynamics in Complex Networks.” The European Physical Journal B.
Meade, and Islam. 2006. “Modeling and Forecasting the Diffusion of Innovation - A 25 Year Review.” International Journal of Forecasting.
Ormerod, and Wiltshire. 2009. ‘Binge’ Drinking in the UK: A Social Network Phenomenon.” Mind & Society.
Pastor-Satorras, and Vespignani. 2002. Immunization of Complex Networks.” Physical Review E.
Randall, Ewing, Marr, et al. 2021. How Did We Get Here: What Are Droplets and Aerosols and How Far Do They Go? A Historical Perspective on the Transmission of Respiratory Infectious Diseases.” Interface Focus.
Raskar, Schunemann, Barbar, et al. 2020. Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic.” arXiv:2003.08567 [Cs].
Shalizi, and Thomas. 2011. Homophily and Contagion Are Generically Confounded in Observational Social Network Studies.” Sociological Methods & Research.
Shen, Taleb, and Bar-Yam. 2020. Review of Ferguson Et Al ‘Impact of Non-Pharmaceutical Interventions…’.”
Sinha, and Chandrashekaran. 1992. A Split Hazard Model for Analyzing the Diffusion of Innovations.” Journal of Marketing Research.
St-Onge, Thibeault, Allard, et al. 2020. School Closures, Event Cancellations, and the Mesoscopic Localization of Epidemics in Networks with Higher-Order Structure.” arXiv:2003.05924 [Nlin, Physics:physics].
Törnberg. 2018. Echo Chambers and Viral Misinformation: Modeling Fake News as Complex Contagion.” PLOS ONE.
Weitz, Beckett, Coenen, et al. 2020. Modeling shield immunity to reduce COVID-19 epidemic spread.” Nature Medicine.