Frank makes an important point. There seem to be two main strategies in play - in the early stages of the epidemic you work to identify sick individuals and their contacts, to quarantine these. Then we move to the delay phase and the focus shifts to places - to identify the places that we might expect to be hot spots for contagion - hospitals, places of work, pubs and mass gatherings. Spatially these are thought of as super connectors, and all the public health interventions are about closing them. The problem seems to be that behaviourally some people respond by shifting their socialisation to other locations - parks and beaches for example. If one could identify superconnectors as individuals - or categories of people - one might be able to think of different measures. I guess we already do this for specific groups such as healthcare front line with the use of PPE but perhaps we could develop something more generally applicable using social simulation modelling to devise a new class of intervention?
A group of us did some work about ten years ago to use bluetooth dongles to see who was co-present with whom in the city of Bath over a several month period. We used the resulting temporal graph of co-presnce over time to emulate information spread - thinking of this as a phone virus spreading through bluetooth. This does identify superconnectors but also the opposite - the brief encounters that hold the whole network together. The paper is here:
https://dl.acm.org/doi/10.1145/1721831.1721833
Alan
> On 23 Mar 2020, at 10:46, Frank Dignum <[log in to unmask]> wrote:
>
> Hi Luzius,
> Thanks for the document. It is very good to use in arguments about the importance of social networks.
> It triggers a new question with me: what are the social networks that we have to look at in this present crisis?
> Friends might hardly meet now, but many people go to the supermarket and can infect employees there that in their turn can infect other customers.
> So, we might have to reconsider which networks still exist and important when all kinds of restrictions apply.
> Is there anyone with insights on this already?
> Cheers,
> Frank.
>
> On 2020-03-23 11:33, Luzius Meisser wrote:
>> Dear Bruce
>> Dear Colleagues
>>
>> Thanks for your thoughts. I'd like to add one additional dimension where social modeling could help with the qualitative understanding of the diffusion of the virus. Most researches simply assume exponential growth (or logistic growth), without taking the network structure of society into account. But it has been shown that the connectivity in social networks often follows a power-law: some people are extremely well connected, others not at all. Taking this into consideration, one should expect the growth rate of a new disease to be very high initially but then drop significantly over time as the "super-spreaders" (those who are the most connected) are either cured or dead. Details attached. (I hope this list supports attachments.) It also implies that herd immunity can be reached much faster than under traditional assumptions.
>>
>> If anyone here is interested in the details, I'm happy to provide the source code and additional documentation.
>>
>> Best
>> Luzius
>>
>>
>> On Mon, 23 Mar 2020 at 10:40, Bruce Edmonds <[log in to unmask]> wrote:
>> Dear Colleagues
>>
>> The compartmental epidemiological models divide population into states (such as infected, recovered etc.) that individuals have. Individuals might be randomly mixing or in some structure (such as a series of locations or a social network). The models represent the rates of change between the states based on their contact with other states. Some of these are quite simple, but there is no reason why these can not be complicated if there was enough data on people's movements and behaviour. The models used for policy are variants of these.
>>
>> One product of the analysis of these models is the R0 factor - the number of others an infected individual will infect within an otherwise uninfected population. It is an average unimpeded growth factor. R0 does give us useful information - e.g. measles with an R0 of over 8, is not containable and the only response is the vaccination of almost everyone.
>>
>> However R0 only tells you so much - it does not touch on social structural change or how people react to policies (their micro-behaviour). Considering virus spread at only this average level can lead to very inefficient policies, where one has to go to extremes because we do not have a good fine-grained analysis - it is like warming up the whole countryside (increasing average temperature) to stop people getting cold.
>>
>> This is where social simulation could contribute, looking at issues such as the following:
>> • How might policies premised on achieving drastic behavioural change go wrong?
>> • How might one work with existing social norms and habits to effectively limit virus spread - what will work with populations and what will not?
>> • How might we reintroduce people who have recovered from the disease back into society to help others and revive the economy without this leading to social division and a general breakdown of social distancing?
>> • What are the possible dangers of social polarisation between vulnerable older people and the young who want to get together, how might we keep younger people "on board", how might we stop them losing contact with other generations?
>> • For particular groups within societies, at particular times of year or day are there safe gathering activities with very low risk of contagion? Are there practices that are particularly dangerous (like washing the body of the deceased with Ebola)?
>> • What new social practices might we develop that allow life in a world susceptible to waves of new infection (e.g. red and blue teams in hospital so there is no overlap)?
>>
>> So a lot we could contribute, but we need to be scrupulously honest about what we can and cannot do - we have nowhere like enough data on how people behave or the real structure of social networks to forecast/predict. We can contribute to the understanding of the complexity of processes (its possible counter-intuitive exceptions) and suggest hypotheses for further empirical study.
>>
>> Regards.
>>
>> ---------------------------------------------------
>> Bruce Edmonds
>> Centre for Policy Modelling
>> Manchester Metropolitan University Business School,
>> All Saints Campus, Oxford Road, Manchester M15 6BH, UK.
>> Tel. +161 247 6479 Fax. +161 247 6802
>> http://bruce.edmonds.name
>>
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>>
>>
>>
>> --
>> Luzius Meisser
>> meissereconomics.com
>>
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>
> --
> Med vänlig hälsning/Best regards,
>
> Frank Dignum *
> Professor Socially Aware AI *
> Department of Computer Science *
> Umeå University *
> Sverige * Knowledge is only one point,
> e-mail:
> [log in to unmask]
> * the ignorant have multiplied it
> telephone: +46-90-7869101 *
>
>
>
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