Scan barcode
Reviews
The Rules of Contagion: Why Things Spread and Why They Stop [With Battery] by Adam Kucharski
arirang's review against another edition
3.0
This is both an oddly well and oddly badly timed book as epitomised by this quote:
As infectious diseases wane, attention is gradually shifting to other threats, many of which can also be contagious.
Adam Kurchashki is one of the epidemologists leading the modelling effort for the UK Scientific Pandemic Influenza Group on Modelling, which advises the Scientific Advisory Group for Emergencies who in turn advise the UK government on Covid 19.
If you're not following him on Twitter you should (@AdamJKucharski) and just today his team put out a sobering analysis of the necessity for lockdown measures
To keep ICU bed demand below capacity in the model, more extreme restrictions were necessary. In a scenario where “lockdown”-type interventions were put in place to reduce transmission, these interventions would need to be in place for a large proportion of the coming year in order to prevent healthcare demand exceeding availability.
https://cmmid.github.io/topics/covid19/control-measures/report/uk_scenario_modelling_preprint_2020_04_01.pdf
He's rightly resisted the tempation to do a quick rewrite/update of this book but it does then make for a read rather different to how it would have read 3 months ago:
- mathematical topics like R0 reproduction number, SIR models and the threshold for herd immunity to kick in (1/(1-Ro)), which then might have been new information, are now part of every day conversation around the virtual office coffee machine;
- but at the time the book was written, one gets the distinct impression that straight epidemology was seen as out of fashion, and so both to sell the book and to make his research useful the author attempts to extend epidemology to thing "going viral" in the wider world (social media posts, computer viruses). However these were rather weaker sections: the scientific read over feels rather a stretch, and the resulting text becomes rather anecdotal and scattergun.
Nevertheless this is still a worthwhile read, particularly for the history of the epidemological tools that are now determining vital life and death decisions, for example the key role played by Ronald Ross, who studied and modelled malaria in the 19th century and:
[Mathematician Klaus Dietz] would help bring the theory of epidemics out of its mathematical niche and into the wider world of public health. Dietz outlined a quantity that would become known as the ‘reproduction number’, or R for short. R represented the number of new infections we’d expect a typical infectious person to generate on average.
And a comment relating to "Spanish" flu but equally pertinent today:
Blaming certain groups for outbreaks is not a new phenomenon. In the sixteenth century, the English believed syphilis came from France, so referred to it as the ‘French pox’. The French, believing it to be from Naples, called it the ‘Neopolitan disease’. In Russia, it was the Polish disease, in Poland it was Turkish, and in Turkey it was Christian.
A recent development he refers to in terms of more real-time modelling of epidemics has proven very valuable in tracing the spread of Covid-19 (see e.g. https://nextstrain.org/narratives/ncov/sit-rep/2020-03-27)
One of the best examples is the Nextstrain project, pioneered by computational biologists Trevor Bedford and Richard Neher. This online platform automatically collates genetic sequences to show how different viruses are related.
And there are some wise words on the use of models - these two in particular:
in essence, a model is just a simplification of the world, designed to help us understand what might happen in a given situation. Mechanistic models are particularly useful for questions that we can’t answer with experiments. If a health agency wants to know how effective their disease control strategy was, they can’t go back and rerun the same epidemic without it. Likewise, if we want to know what a future pandemic might look like, we can’t deliberately release a new virus and see how it spreads. Models give us the ability to examine outbreaks without interfering with reality. We can explore how things like transmission and recovery affect the spread of infection. We can introduce different control measures–from mosquito removal to vaccination–and see how effective they might be in different situations.
and
According to Chris Whitty, now the Chief Medical Officer for England, the best mathematical models are not necessarily the ones that try to make an accurate forecast about the future. What matters is having analysis that can reveal gaps in our understanding of a situation. ‘They are generally most useful when they identify impacts of policy decisions which are not predictable by commonsense,’ Whitty has suggested. ‘The key is usually not that they are “right”, but that they provide an unpredicted insight.
While enjoying the book, I'd commend the author's twitter feed as a better and more timely read, hence the 3 stars.
As infectious diseases wane, attention is gradually shifting to other threats, many of which can also be contagious.
Adam Kurchashki is one of the epidemologists leading the modelling effort for the UK Scientific Pandemic Influenza Group on Modelling, which advises the Scientific Advisory Group for Emergencies who in turn advise the UK government on Covid 19.
If you're not following him on Twitter you should (@AdamJKucharski) and just today his team put out a sobering analysis of the necessity for lockdown measures
To keep ICU bed demand below capacity in the model, more extreme restrictions were necessary. In a scenario where “lockdown”-type interventions were put in place to reduce transmission, these interventions would need to be in place for a large proportion of the coming year in order to prevent healthcare demand exceeding availability.
https://cmmid.github.io/topics/covid19/control-measures/report/uk_scenario_modelling_preprint_2020_04_01.pdf
He's rightly resisted the tempation to do a quick rewrite/update of this book but it does then make for a read rather different to how it would have read 3 months ago:
- mathematical topics like R0 reproduction number, SIR models and the threshold for herd immunity to kick in (1/(1-Ro)), which then might have been new information, are now part of every day conversation around the virtual office coffee machine;
- but at the time the book was written, one gets the distinct impression that straight epidemology was seen as out of fashion, and so both to sell the book and to make his research useful the author attempts to extend epidemology to thing "going viral" in the wider world (social media posts, computer viruses). However these were rather weaker sections: the scientific read over feels rather a stretch, and the resulting text becomes rather anecdotal and scattergun.
Nevertheless this is still a worthwhile read, particularly for the history of the epidemological tools that are now determining vital life and death decisions, for example the key role played by Ronald Ross, who studied and modelled malaria in the 19th century and:
[Mathematician Klaus Dietz] would help bring the theory of epidemics out of its mathematical niche and into the wider world of public health. Dietz outlined a quantity that would become known as the ‘reproduction number’, or R for short. R represented the number of new infections we’d expect a typical infectious person to generate on average.
And a comment relating to "Spanish" flu but equally pertinent today:
Blaming certain groups for outbreaks is not a new phenomenon. In the sixteenth century, the English believed syphilis came from France, so referred to it as the ‘French pox’. The French, believing it to be from Naples, called it the ‘Neopolitan disease’. In Russia, it was the Polish disease, in Poland it was Turkish, and in Turkey it was Christian.
A recent development he refers to in terms of more real-time modelling of epidemics has proven very valuable in tracing the spread of Covid-19 (see e.g. https://nextstrain.org/narratives/ncov/sit-rep/2020-03-27)
One of the best examples is the Nextstrain project, pioneered by computational biologists Trevor Bedford and Richard Neher. This online platform automatically collates genetic sequences to show how different viruses are related.
And there are some wise words on the use of models - these two in particular:
in essence, a model is just a simplification of the world, designed to help us understand what might happen in a given situation. Mechanistic models are particularly useful for questions that we can’t answer with experiments. If a health agency wants to know how effective their disease control strategy was, they can’t go back and rerun the same epidemic without it. Likewise, if we want to know what a future pandemic might look like, we can’t deliberately release a new virus and see how it spreads. Models give us the ability to examine outbreaks without interfering with reality. We can explore how things like transmission and recovery affect the spread of infection. We can introduce different control measures–from mosquito removal to vaccination–and see how effective they might be in different situations.
and
According to Chris Whitty, now the Chief Medical Officer for England, the best mathematical models are not necessarily the ones that try to make an accurate forecast about the future. What matters is having analysis that can reveal gaps in our understanding of a situation. ‘They are generally most useful when they identify impacts of policy decisions which are not predictable by commonsense,’ Whitty has suggested. ‘The key is usually not that they are “right”, but that they provide an unpredicted insight.
While enjoying the book, I'd commend the author's twitter feed as a better and more timely read, hence the 3 stars.
More...