L
Lin Wang
Researcher at University of Cambridge
Publications - 90
Citations - 7300
Lin Wang is an academic researcher from University of Cambridge. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 32, co-authored 65 publications receiving 5356 citations. Previous affiliations of Lin Wang include City University of Hong Kong & Fudan University.
Papers
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Journal ArticleDOI
Age-specific mortality and immunity patterns of SARS-CoV-2.
Megan O'Driscoll,Megan O'Driscoll,Gabriel Ribeiro Dos Santos,Gabriel Ribeiro Dos Santos,Lin Wang,Lin Wang,Derek A. T. Cummings,Andrew S. Azman,Andrew S. Azman,Juliette Paireau,Arnaud Fontanet,Arnaud Fontanet,Simon Cauchemez,Henrik Salje,Henrik Salje +14 more
TL;DR: The relative risk of COVID-19-associated death for younger individuals (under 65) is consistent across countries and can be used to robustly compare the underlying number of infections in each country, and the age distribution of deaths in younger age groups is very consistent across different settings.
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Evolutionary games on multilayer networks: a colloquium
TL;DR: In this paper, a colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies.
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Serial Interval of COVID-19 among Publicly Reported Confirmed Cases.
Zhanwei Du,Xiao-Ke Xu,Ye Wu,Lin Wang,Benjamin J. Cowling,Lauren Ancel Meyers,Lauren Ancel Meyers +6 more
TL;DR: This work estimates the distribution of serial intervals for 468 confirmed cases of coronavirus disease reported in China as of February 8, 2020 and finds that 12.6% of case reports indicated presymptomatic transmission.
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Evolutionary games on multilayer networks: A colloquium
TL;DR: This colloquium highlights the importance of pattern formation and collective behavior for the promotion of cooperation under adverse conditions, as well as the synergies between network science and evolutionary game theory.
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Coupled disease–behavior dynamics on complex networks: A review
TL;DR: It is discussed how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies and pitfalls which might be faced by researchers in the field.