Challenge faced worldwide: a new spread of Covid
We all have lived through Covid19, across the world. Prior to the availability of vaccines, Non-Pharmaceutic Interventions by health-aware governments were implemented, with significant success, well into a stage of lockdown, where residents of a country were asked and then required to stay at home, with stringent conditions to get out of their homes. The logistics of food supply was usually well managed, even if there were cases of people remaining isolated from food supply at times.
Anticipating the risk through propagation models
The key to not letting Covid-19 take its toll, and it actually did take its toll, especially among elderly residents of care homes, in Italy, France, the UK, etc, was to model in an anticipatory manner the spread of the disease and assess its risk realistically.
Macro-models available (statistics), but what about micro-level (few humans)?
Modelling was mostly at macro-level: cities, regions, countries. However the different context of human interaction in daily life received much less attention, although large data sets and use cases build on a number of elementary interactions, and smaller numbers of humans involved in each.
Elementary interactions of few humans
Our endeavour, which could not afford the ambition of health statisticians in larger teams, to model the spread of the disease at country level, focused instead then, in the years 2019-2020, onto elementary use cases of interaction, with few humans involved (few starting from 2). Such use cases covered elderly patients of care homes, and their interactions during joint meals in the care home meal area, with tables shared, it also covered households in close (and closed) interaction during lockdown. It also tried to make sense of large events, where many humans interact during a limited time (football game, women's day celebration, etc).
The typology of likely propagation in such use cases was modeled, and parameters of a simple but robust model were tuned to known data, and in turn simulations could be run, and such simulation could be assessed on other known outcomes (such as the observation of virus propagation among the citizen team running a polling center during elections in France).
Next steps: anticipating the wave coming, with micro-models?
Can we ask the researchgate community if anyone is interested to undertake similar micro-level models of elementary human interaction leading to a likely spread of the virus?
Could we consider building a federated collaborative project, with data fed by anyone having access to these (literature, publications, etc)?
What approach do you recommend? Have you published on the topic?
REF
Here is a reference to the model mentioned above, with associated training/verification data:
[1] Agent Based Model for Covid 19 Transmission: -field approach based on context of interaction, July 2020, R. Di Francesco, DOI: 10.13140/RG.2.2.24583.83364
Preprint Agent Based Model for Covid 19 Transmission: -field approach...
We generally advance by post-mortems, dear Renaud Di Francesco
Modelling better would mean that we are able to learn from such post-mortems, i.e. to apply Markov chains with exactness.
Many thanks Stephen I. Ternyik , I agree we have a Markovian approach in need of training/tuning: what impact of protection will the new vaccines have? etc
-to "visualise", let me remind me of the protection degree offered by a helmet in a heavy motorcycle crash: roughly 60%! probability to survive is roughly 0.6, as an estimate. Without a helmet, this probability goes towards zero. I know, many people cycle with no helmet, but maybe it's ok if you have no brain or if you don't need your brains? [sad joke, I recognise]
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