Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility

Balcan, Duygu; Hu, Hao; Gonçalves, Bruno; Bajardi, Paolo; Poletto, Chiara; Ramasco, José J.; Paolotti, Daniela; Perra, Nicola; Tizzoni, Michele; Broeck, Wouter Van den; Colizza, Vittoria; Vespignani, Alessandro

doi:10.1186/1741-7015-7-45

Cited by 340 publications

(462 citation statements)

References 39 publications

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“…This suggests that cities with different mobility patterns may also differ in the rate at which their inhabitants have infectious contact, leading to variation among cities in the risk of an epidemic [5][6][7]. Human movement patterns are heterogeneous at a wide range of scales-from within a building [8] to among countries [9][10][11], as evidenced by diverse sources of data, including the movements of mobile phone users [12,13], air travel patterns [9][10][11] and census data on commuting patterns [10,14,15]. At each scale, there appear collective mobility patterns maintained far from those predicted by homogeneous random movement.…”

Section: Introductionmentioning

confidence: 99%

“…Individual variation in rates of infectious contact can significantly alter patterns of disease spread [6,7,15,[19][20][21] and theoretical models of disease dynamics within and among cities (both individual-based simulations [5,7,10,15,[22][23][24] and metapopulation models [10,11,14,15,[25][26][27]) have shown that heterogeneous contact patterns are potentially important in determining urban epidemic dynamics. However, few studies have examined whether empirical variation in intracity mobility patterns is sufficient to drive detectable differences in epidemic dynamics among cities.…”

Section: Introductionmentioning

confidence: 99%

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Human mobility patterns predict divergent epidemic dynamics among cities

2013

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The epidemic dynamics of infectious diseases vary among cities, but it is unclear how this is caused by patterns of infectious contact among individuals. Here, we ask whether systematic differences in human mobility patterns are sufficient to cause inter-city variation in epidemic dynamics for infectious diseases spread by casual contact between hosts. We analyse census data on the mobility patterns of every full-time worker in 48 Canadian cities, finding a power-law relationship between population size and the level of organization in mobility patterns, where in larger cities, a greater fraction of workers travel to work in a few focal locations. Similarly sized cities also vary in the level of organization in their mobility patterns, equivalent on average to the variation expected from a 2.64-fold change in population size. Systematic variation in mobility patterns is sufficient to cause significant differences among cities in infectious disease dynamics—even among cities of the same size—according to an individual-based model of airborne pathogen transmission parametrized with the mobility data. This suggests that differences among cities in host contact patterns are sufficient to drive differences in infectious disease dynamics and provides a framework for testing the effects of host mobility patterns in city-level disease data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human mobility patterns predict divergent epidemic dynamics among cities

2013

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“…PACS numbers: 89.75.Fb, 05.45.Df, 64.60.al Epidemic spreading is one of the most successful application areas of the new science of networks [1,2]. Indeed, the general acceptance within the scientific community that many diseases, like sexually transmitted diseases or the H1N1 virus, spread over networked systems represents a major step toward their understanding and control [3][4][5]. From a physics perspective, epidemic processes have been widely studied as a paradigm of nonequilibrium phase transitions with absorbing states [6].…”

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confidence: 99%

Epidemic spreading on interconnected networks

2012

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Many real networks are not isolated from each other but form networks of networks, often interrelated in non trivial ways. Here, we analyze an epidemic spreading process taking place on top of two interconnected complex networks. We develop a heterogeneous mean field approach that allows us to calculate the conditions for the emergence of an endemic state. Interestingly, a global endemic state may arise in the coupled system even though the epidemics is not able to propagate on each network separately, and even when the number of coupling connections is small. Our analytic results are successfully confronted against large-scale numerical simulations. PACS numbers: 89.75.Fb, 05.45.Df, 64.60.al Epidemic spreading is one of the most successful application areas of the new science of networks [1,2]. Indeed, the general acceptance within the scientific community that many diseases, like sexually transmitted diseases or the H1N1 virus, spread over networked systems represents a major step toward their understanding and control [3][4][5]. From a physics perspective, epidemic processes have been widely studied as a paradigm of nonequilibrium phase transitions with absorbing states [6]. When applied to complex networks, these processes have become a source of new and striking phenomena that do not have a counterpart in regular lattices. Germane examples are the absence of epidemic and percolation thresholds in scale-free networks with a power law degree distribution P (k) ∼ k −γ with γ ∈ (2, 3], and an anomalous critical behavior when γ ∈ (3, 4) [7][8][9].We currently have a solid understanding of epidemic processes when they take place on single isolated networks. In contrast, our comprehension is very limited when epidemics happen on coupled interconnected networks. For example, sexually transmitted diseases can propagate both in heterosexual and homosexual networks of sexual contacts [3]. These two networks are not completely isolated due to the existence of bisexual individuals, which act as an effective coupling between the two networks and potentially affect their epidemic properties [10]. To the best of our knowledge, a theory describing these type of systems has not yet been fully developed.In this paper, we fill this gap and present a rigorous heterogeneous mean field study of the susceptibleinfected-susceptible (SIS) model taking place on two interconnected complex networks. Our analysis reveals a highly non-trivial behavior of the epidemic process depending on the strength and nature of the coupling between the networks. We calculate the global epidemic threshold of the process, which turns out to be smaller than the epidemic thresholds of the two networks separately under certain conditions. This implies that an endemic state may arise even if the epidemics is not endemic in any of the two networks separately, as we prove analytically and with large-scale computer simulations.To begin our analysis, we have to specify the topological properties of our networks. Let A and B be two interconnected random netwo...

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“…An estimated 1·96 secondary cases were generated from each primary case early in New Zealand’s first pandemic wave (the reproduction estimate) 7 . Reproduction estimates in other countries range from 1·2 to 2·4, 8 , 9 , 10 , 11 , 12 , 13 and reached 2·8 for a subanalysis of persons under 20 years of age 10 . Certainly rapid transmission amongst students was commonly observed internationally 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 …”

Section: Discussionmentioning

confidence: 95%

Limited novel influenza A (H1N1) 09 infection in travelling high-school tour group

Mardani

Calder

Laurie

et al. 2010

Influenza and Other Respiratory Viruses

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Please cite this paper as: Mardani et al. (2011) Limited novel influenza A (H1N1) 09 infection in travelling high‐school tour group. Influenza and Other Respiratory Viruses 5(1), 47–51. Background A single case of novel influenza A (H1N1) 09 infection was identified by PCR among a New Zealand high‐school group that toured California in April 2009. Close monitoring of the tour group and their New Zealand contacts identified 11 other tour members with respiratory symptoms who were investigated. In all nine instances where nasopharyngeal swabs were indicated, tests were negative for novel influenza A (H1N1) 09 by PCR. Objective To determine whether serology could identify any cases of novel influenza A (H1N1) 09 that had not been detected by PCR. Methods Acute and convalescent serological testing for antibodies against pandemic (H1N1) 2009 and seasonal A (H1N1) influenza viruses using haemagglutination inhibition assays and microneutralisation assays. Results Serological analysis of symptomatic tour members identified a further possible case of novel influenza A (H1N1) 09 infection. The possible case had not been tested by PCR because he or she had already received prophylaxis with oseltamivir. Conclusions These findings suggest infection among tour group members was limited despite prolonged periods of close contact during travel. Furthermore, multiple public health interventions are likely to have effectively prevented an outbreak following the tour group’s return.

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Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility

Cited by 340 publications

References 39 publications

Human mobility patterns predict divergent epidemic dynamics among cities

Human mobility patterns predict divergent epidemic dynamics among cities

Epidemic spreading on interconnected networks

Limited novel influenza A (H1N1) 09 infection in travelling high-school tour group

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