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Graphs to Help plan Deconfinement? | |
admin | |
2020-05-04 | |
发布年 | 2020 |
语种 | 英语 |
国家 | 法国 |
领域 | 地球科学 ; 资源环境 |
正文(英文) |
Graphs showing the evolution of contacts between students from different classes.
The computer scientist Claire Mathieu explains how graph theory can help model the propagation of Covid-19, and assess the relevance of several confinement scenarios.
You are proposing to use tools from graph theory to study the propagation of Covid-19. How did this project come to life, and what are its objectives? What are graphs, and how are they connected to the propagation of epidemics? For example, the graph of social interactions in my neighbourhood during this period of confinement will show that I am connected to very few people, essentially my family and the sales assistants where I shop, while my baker’s activity means they are linked to a very large number of locals. We can then use this network to provide a probability for virus transmission during an interaction between two people in the network, which helps understand how the infection spreads within the population. Using similar models, computer science researchers in the past have studied how best to disseminate information within a group. This in short revealed the importance of highly connected individuals, as well as those belonging to multiple communities at the same time. These people should be given preference in spreading information, and conversely should be the ones that are isolated first and foremost in order to contain an epidemic. What does this type of model offer in comparison to those already used by epidemiologists? What precisely will this involve? Should they be opened part time? Should students be separated into different groups? If so, what would be the optimal size? Should children eat lunch in the classroom? Is it preferable for secondary school teachers to rotate from class to class, instead of their students? Rigorous quantitative analysis can help answer these questions. We then plan to theoretically model interaction networks that are representative of various situations, which we can subsequently use to conduct similar analyses. It would be interesting to model the typical links within a family, school, or company, and to connect the corresponding graphs in order to understand the specific “pathways” travelled by the epidemic. This could shed light on the deconfinement scenarios being studied on the scale of a city or neighbourhood, by concretely indicating the most relevant links to keep inactive. The French President announced that schools would reopen on 11 May. What is your opinion thereon at this stage of your research? For that matter, suppose that in the next few weeks we find ourselves in a situation similar to the first epidemiological stage (except for those who have acquired immunity). It would then be necessary to isolate new individuals who are infected, and test their contacts in order to quarantine them where necessary, even before they themselves become contagious. Our approach could help determine priorities in this process. Indeed, while some links should obviously be severed, other more subtle ones could only be revealed through rigorous mathematical analysis. Could your research results serve as input data to ‘feed’ epidemiological models on a larger scale, thereby making their predictions more accurate? Footnotes
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来源平台 | CNRS News |
文献类型 | 新闻 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/251711 |
专题 | 地球科学 资源环境科学 |
推荐引用方式 GB/T 7714 | admin. Graphs to Help plan Deconfinement?. 2020. |
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