Fredrik LiljerosProfessor of Sociology
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: 4685 h-index: 23 (2022-04-22)
GS Citations: 8729 index: 29 (2021-02-09) Please notice !!! GS has a clear tendency to overestimate citations because it reports non-referee reviewed publications such as Working papers and self-quotes in them. The onel reaseon I have to show them is that other reserchers seems to prefer them;-)
I am responsible for the following courses
Mesosociology: Organizations and Networks. 7,5 credits
The module introduces basic sociological theory about the interaction between individual action and social structures. Special attention is paid to different theories about different types of organizations, the tension between actor and structure and social networks.
Introduction to sociology - thinking sociologically, 2.5 credits
The course introduces the subject of sociology as a scientific subject and how sociological questions are formulated and answered.
Power and social stratification, 7.5 credits
The course exemplifies sociology as a scientific subject with a study of power and social stratification. Social stratification and power have been a central research field in sociology ever since the subject was established as an academic discipline. The course introduces basic sociological theory and empirical evidence regarding power and social stratification. Special attention is given to the importance of class, gender and ethnicity for life chances and distribution of power resources.
Social networks 7.5 credits
By using a network perspective, the focus of social analysis shifts from the social actors themselves to the relationships that connect them. The course provides an introduction to modern network analysis and includes an overview of theoretical and methodological literature in this area as well as computer-based exercises.
Models of social stability and social change 7.5 credits
This course provides a critical in-depth overview of how mathematical models and simulation models can be used to model and analyze social change and social stability.
A selection from Stockholm University publication database
Nontrivial resource requirement in the early stage for containment of epidemics
2019. Xiaolong Chen (et al.). Physical review. E 100 (3)Article
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.
Identification of influential spreaders in complex networks
2010. Maksim Kitsak (et al.). Nature Physics 6 (11), 888-893Article
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.
Exploiting Temporal Network Structures of Human Interaction to Effectively Immunize Populations
2012. Sungmin Lee (et al.). PLoS ONE 7 (5), e36439Article
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.
Finding a better immunization strategy
2008. Y Chen (et al.). Phys Rev Lett 101 (5)Article
Birth and death of links control disease spreading in empirical contact networks
2014. Petter Holme, Fredrik Liljeros. Scientific Reports 4, 4999Article
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.
Identifying asymptomatic spreaders of antimicrobial-resistant pathogens in hospital settings
2021. Sen Pei, Fredrik Liljeros, Jeffrey Shaman. Proceedings of the National Academy of Sciences of the United States of America 118 (37)Article
Antimicrobial-resistant organisms (AMROs) can colonize people without symptoms for long periods of time, during which these agents can spread unnoticed to other patients in healthcare systems. The accurate identification of asymptomatic spreaders of AMRO in hospital settings is essential for supporting the design of interventions against healthcare-associated infections (HAIs). However, this task remains challenging because of limited observations of colonization and the complicated transmission dynamics occurring within hospitals and the broader community. Here, we study the transmission of methicillin-resistant Staphylococcus aureus (MRSA), a prevalent AMRO, in 66 Swedish hospitals and healthcare facilities with inpatients using a data-driven, agent-based model informed by deidentified real-world hospitalization records. Combining the transmission model, patient-to-patient contact networks, and sparse observations of colonization, we develop and validate an individual-level inference approach that estimates the colonization probability of individual hospitalized patients. For both model-simulated and historical outbreaks, the proposed method supports the more accurate identification of asymptomatic MRSA carriers than other traditional approaches. In addition, in silica control experiments indicate that interventions targeted to inpatients with a high-colonization probability outperform heuristic strategies informed by hospitalization history and contact tracing.
Dynamic contact networks of patients and MRSA spread in hospitals
2020. Luis E. C. Rocha (et al.). Scientific Reports 10 (1)Article
Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection. Increasing efforts have been taken to mitigate the epidemics and to avoid potential outbreaks in low endemic settings. Understanding the population dynamics of MRSA is essential to identify the causal mechanisms driving the epidemics and to generalise conclusions to different contexts. Previous studies neglected the temporal structure of contacts between patients and assumed homogeneous behaviour. We developed a high-resolution data-driven contact network model of interactions between 743,182 patients in 485 hospitals during 3,059 days to reproduce the exact contact sequences of the hospital population. Our model captures the exact spatial and temporal human contact behaviour and the dynamics of referrals within and between wards and hospitals at a large scale, revealing highly heterogeneous contact and mobility patterns of individual patients. A simulation exercise of epidemic spread shows that heterogeneous contacts cause the emergence of super-spreader patients, slower than exponential polynomial growth of the prevalence, and fast epidemic spread between wards and hospitals. In our simulated scenarios, screening upon hospital admittance is potentially more effective than reducing infection probability to reduce the final outbreak size. Our findings are useful to understand not only MRSA spread but also other hospital-acquired infections.