Fredrik Liljeros


Visa sidan på svenska
Works at Department of Sociology
Visiting address Universitetsvägen 10 B, plan 9
Room B 951
Postal address Sociologiska institutionen 106 91 Stockholm

About me

Fredrik Liljeros is Professor in Sociology. His research is mainly focused on how human interaction patterns affect how social influences and diseases are spread in different populations. Fredrik Liljeros has developed mathematical models for different types of infections are spread. Fredrik Liljeros did his post doc at the Swedish institute for infectious diseases and control, worked with pandemic planning, and studied, among other things, how travel restrictions affect the spread of diseases. He is also working on developing software for the prediction of complex nonlinear social systems together with Doloresgroup.

ISI Citations: 4159 h-index: 21  (2021-03-18)

GS Citations: 8076 h-index: 29  (2021-03-18)


A selection from Stockholm University publication database
  • 2019. Xiaolong Chen (et al.). Physical review. E 100 (3)

    During epidemic control, containment of the disease is usually achieved through increasing a devoted resource to reduce the infectiousness. However, the impact of this resource expenditure has not been studied quantitatively. For disease spread, the recovery rate can be positively correlated with the average amount of resource devoted to infected individuals. By incorporating this relation we build a novel model and find that insufficient resource leads to an abrupt increase in the infected population size, which is in marked contrast with the continuous phase transitions believed previously. Counterintuitively, this abrupt phase transition is more pronounced in less contagious diseases. Furthermore, we find that even for a single infection source, the public resource needs to be available in a significant amount, which is proportional to the total population size, to ensure epidemic containment. Our findings provide a theoretical foundation for efficient epidemic containment strategies in the early stage.

  • 2010. Maksim Kitsak (et al.). Nature Physics 6 (11), 888-893

    Networks portray a multitude of interactions through which people meet, ideas are spread and infectious diseases propagate within a society(1-5). Identifying the most efficient 'spreaders' in a network is an important step towards optimizing the use of available resources and ensuring the more efficient spread of information. Here we show that, in contrast to common belief, there are plausible circumstances where the best spreaders do not correspond to the most highly connected or the most central people(6-10). Instead, we find that the most efficient spreaders are those located within the core of the network as identified by the k-shell decomposition analysis(11-13), and that when multiple spreaders are considered simultaneously the distance between them becomes the crucial parameter that determines the extent of the spreading. Furthermore, we show that infections persist in the high-k shells of the network in the case where recovered individuals do not develop immunity. Our analysis should provide a route for an optimal design of efficient dissemination strategies.

  • 2012. Sungmin Lee (et al.). PLoS ONE 7 (5), e36439

    Decreasing the number of people who must be vaccinated to immunize a community against an infectious disease could both save resources and decrease outbreak sizes. A key to reaching such a lower threshold of immunization is to find and vaccinate people who, through their behavior, are more likely than average to become infected and to spread the disease further. Fortunately, the very behavior that makes these people important to vaccinate can help us to localize them. Earlier studies have shown that one can use previous contacts to find people that are central in static contact networks. However, real contact patterns are not static. In this paper, we investigate if there is additional information in the temporal contact structure for vaccination protocols to exploit. We answer this affirmative by proposing two immunization methods that exploit temporal correlations and showing that these methods outperform a benchmark static-network protocol in four empirical contact datasets under various epidemic scenarios. Both methods rely only on obtainable, local information, and can be implemented in practice. For the datasets directly related to contact patterns of potential disease spreading ( of sexually-transmitted and nosocomial infections respectively), the most efficient protocol is to sample people at random and vaccinate their latest contacts. The network datasets are temporal, which enables us to make more realistic evaluations than earlier studies-we use only information about the past for the purpose of vaccination, and about the future to simulate disease outbreaks. Using analytically tractable models, we identify two temporal structures that explain how the protocols earn their efficiency in the empirical data. This paper is a first step towards real vaccination protocols that exploit temporal-network structure-future work is needed both to characterize the structure of real contact sequences and to devise immunization methods that exploit these.

  • 2008. Y Chen (et al.). Phys Rev Lett 101 (5)
  • 2014. Petter Holme, Fredrik Liljeros. Scientific Reports 4, 4999

    We investigate what structural aspects of a collection of twelve empirical temporal networks of human contacts are important to disease spreading. We scan the entire parameter spaces of the two canonical models of infectious disease epidemiology-the Susceptible-Infectious-Susceptible (SIS) and Susceptible-Infectious-Removed (SIR) models. The results from these simulations are compared to reference data where we eliminate structures in the interevent intervals, the time to the first contact in the data, or the time from the last contact to the end of the sampling. The picture we find is that the birth and death of links, and the total number of contacts over a link, are essential to predict outbreaks. On the other hand, the exact times of contacts between the beginning and end, or the interevent interval distribution, do not matter much. In other words, a simplified picture of these empirical data sets that suffices for epidemiological purposes is that links are born, is active with some intensity, and die.

Show all publications by Fredrik Liljeros at Stockholm University

Last updated: March 18, 2021

Bookmark and share Tell a friend