Reconstructing propagation networks with natural diversity and identifying hidden sources

Nature Communications

Volumn 5, Issue , 2014, Pages

  Shen, Zhesi ^a   Wang, Wen Xu ^a,b   Fan, Ying ^a   Di, Zengru ^a   Lai, Ying Cheng ^b,c  

^a BEIJING NORMAL UNIVERSITY   (China)

^b Arizona State University   (United States)

^c ARIZONA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EPIDEMIC; NETWORK ANALYSIS; RECONSTRUCTION; STOCHASTICITY; TIME SERIES;

ARTICLE; COMPUTER NETWORK; CONCEPTUAL FRAMEWORK; CONTROL STRATEGY; DATA ANALYSIS; DATA PROCESSING; EPIDEMIC; GEOGRAPHIC INFORMATION SYSTEM; POPULATION DYNAMICS; STOCHASTIC MODEL; THEORETICAL MODEL; TIME SERIES ANALYSIS;

EID: 84904330360     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms5323     Document Type: Article

References (58)

1. Caldarelli, G., Chessa, A., Pammolli, F., Gabrielli, A. & Puliga, M. Reconstructing a credit network. Nat. Phys. 9, 125-126 (2013).
2. Gardner, T. S., di Bernardo, D., Lorenz, D. & Collins, J. J. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301, 102-105 (2003).
3. Timme, M. Revealing network connectivity from response dynamics. Phys. Rev. Lett. 98, 224101 (2007).
4. Bongard, J. & Lipson, H. Automated reverse engineering of nonlinear dynamical systems. Proc. Natl Acad. Sci. 104, 9943-9948 (2007).
5. Clauset, A., Moore, C. & Newman, M. E. Hierarchical structure and the prediction of missing links in networks. Nature 453, 98-101 (2008).
6. Ren, J., Wang, W.-X., Li, B. & Lai, Y.-C. Noise bridges dynamical correlation and topology in coupled oscillator networks. Phys. Rev. Lett. 104, 058701 (2010).
7. Levnajić, Z. & Pikovsky, A. Network reconstruction from random phase resetting. Phys. Rev. Lett. 107, 034101 (2011).
8. Hempel, S., Koseska, A., Kurths, J. & Nikoloski, Z. Inner composition alignment for inferring directed networks from short time series. Phys. Rev. Lett. 107, 054101 (2011).
9. Albert, R. & Barabási, A.-L. Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47 (2002).
10. Newman, M. E. The structure and function of complex networks. SIAM Rev. 45, 167-256 (2003).
11. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. & Hwang, D.-U. Complex networks: Structure and dynamics. Phys. Rep. 424, 175-308 (2006).
12. Newman, M. Networks: An Introduction (OUP Oxford, 2009).
13. Pastor-Satorras, R. & Vespignani, A. Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86, 3200-3203 (2001).
14. Eames, K. T. & Keeling, M. J. Modeling dynamic and network heterogeneities in the spread of sexually transmitted diseases. Proc. Natl Acad. Sci. 99, 13330-13335 (2002).
15. Watts, D. J., Muhamad, R., Medina, D. C. & Dodds, P. S. Multiscale, resurgent epidemics in a hierarchical metapopulation model. Proc. Natl Acad. Sci. USA 102, 11157-11162 (2005).
16. Colizza, V., Barrat, A., Barthélemy, M. & Vespignani, A. The role of the airline transportation network in the prediction and predictability of global epidemics. Proc. Natl Acad. Sci. USA 103, 2015-2020 (2006).
17. Gómez-Gardeñes, J., Latora, V., Moreno, Y. & Profumo, E. Spreading of sexually transmitted diseases in heterosexual populations. Proc. Natl Acad. Sci 105, 1399-1404 (2008).
18. Wang, P., González, M. C., Hidalgo, C. A. & Barabási, A.-L. Understanding the spreading patterns of mobile phone viruses. Science 324, 1071-1076 (2009).
19. Merler, S. & Ajelli, M. The role of population heterogeneity and human mobility in the spread of pandemic influenza. Proc. R. Soc. B 277, 557-565 (2010).
20. Balcan, D. & Vespignani, A. Phase transitions in contagion processes mediated by recurrent mobility patterns. Nat. Phys. 7, 581-586 (2011).
21. Riley, S. et al. Transmission dynamics of the etiological agent of sars in hong kong: impact of public health interventions. Science 300, 1961-1966 (2003).
22. Marra, M. A. et al. The genome sequence of the sars-associated coronavirus. Science 300, 1399-1404 (2003).
23. Ferguson, N. M. et al. Strategies for containing an emerging influenza pandemic in southeast asia. Nature 437, 209-214 (2005).
24. Zhang, Y. et al. H5n1 hybrid viruses bearing 2009/h1n1 virus genes transmit in guinea pigs by respiratory droplet. Science 340, 1459-1463 (2013).
25. Neumann, G., Noda, T. & Kawaoka, Y. Emergence and pandemic potential of swine-origin h1n1 influenza virus. Nature 459, 931-939 (2009).
26. Smith, G. J. et al. Origins and evolutionary genomics of the 2009 swine-origin h1n1 influenza a epidemic. Nature 459, 1122-1125 (2009).
27. Hvistendahl, M., Normile, D. & Cohen, J. Despite large research effort, h7n9 continues to baffle. Science 340, 414-415 (2013).
28. Horby, P. H7n9 is a virus worth worrying about. Nature 496, 399-399 (2013).
29. Boguná, M., Pastor-Satorras, R. & Vespignani, A. Absence of epidemic threshold in scale-free networks with degree correlations. Phys. Rev. Lett. 90, 028701 (2003).
30. Parshani, R., Carmi, S. & Havlin, S. Epidemic threshold for the susceptibleinfectious- susceptible model on random networks. Phys. Rev. Lett. 104, 258701 (2010).
31. Castellano, C. & Pastor-Satorras, R. Thresholds for epidemic spreading in networks. Phys. Rev. Lett. 105, 218701 (2010).
32. Gleeson, J. P. High-accuracy approximation of binary-state dynamics on networks. Phys. Rev. Lett. 107, 068701 (2011).
33. Gomez Rodriguez, M., Leskovec, J. & Krause, A. in: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1019-1028 (ACM, 2010).
34. Pinto, P. C., Thiran, P. & Vetterli, M. Locating the source of diffusion in large-scale networks. Phys. Rev. Lett. 109, 068702 (2012).
35. Candès, E. J., Romberg, J. & Tao, T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52, 489-509 (2006).
36. Candes, E. J., Romberg, J. K. & Tao, T. Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59, 1207-1223 (2006).
37. Donoho, D. L. Compressed sensing. IEEE Trans. Inf. Theory 52, 1289-1306 (2006).
38. Baraniuk, R. G. Compressive sensing [lecture notes]. Sig. Proc. Mag. IEEE 24, 118-121 (2007).
39. Candès, E. J. & Wakin, M. B. An introduction to compressive sampling. Sig. Proc. Mag. IEEE 25, 21-30 (2008).
40. Romberg, J. Imaging via compressive sampling. Sig. Proc. Mag. IEEE 25, 14-20 (2008).
41. Wang, W.-X., Yang, R., Lai, Y.-C., Kovanis, V. & Grebogi, C. Predicting catastrophes in nonlinear dynamical systems by compressive sensing. Phys. Rev. Lett. 106, 154101 (2011).
42. Wang, W.-X., Yang, R., Lai, Y.-C., Kovanis, V. & Harrison, M. A. F. Time-series-based prediction of complex oscillator networks via compressive sensing. EPL 94, 48006 (2011).
43. Wang, W.-X., Lai, Y.-C., Grebogi, C. & Ye, J. Network reconstruction based on evolutionary-game data via compressive sensing. Phys. Rev. X 1, 021021 (2011).
44. Myers, S. A. & Leskovec, J. On the Convexity of Latent Social Network Inference. Adv. Neural Inf. Process Syst. 23, 1741-1749 (2010).
45. Castellano, C. & Pastor-Satorras, R. Non-mean-field behavior of the contact process on scale-free networks. Phys. Rev. Lett. 96, 038701 (2006).
46. Volz, E. & Meyers, L. A. Epidemic thresholds in dynamic contact networks. J. R. Soc. Interface 6, 233-241 (2009).
47. Cohen, R., Havlin, S. & ben Avraham, D. Efficient immunization strategies for computer networks and populations. Phys. Rev. Lett. 91, 247901 (2003).
48. Forster, G. A. & Gilligan, C. A. Optimizing the control of disease infestations at the landscape scale. Proc. Natl Acad. Sci 104, 4984-4989 (2007).
49. Klepac, P., Laxminarayan, R. & Grenfell, B. T. Synthesizing epidemiological and economic optima for control of immunizing infections. Proc. Natl Acad. Sci 108, 14366-14370 (2011).
50. Kleczkowski, A., Ole, K., Gudowska-Nowak, E. & Gilligan, C. A. Searching for the most cost-effective strategy for controlling epidemics spreading on regular and small-world networks. J. R. Soc. Interface 9, 158-169 (2012).
51. Su, R.-Q., Wang, W.-X. & Lai, Y.-C. Detecting hidden nodes in complex networks from time series. Phys. Rev. E 85, 065201 (2012).
52. Slotine, J.-J. E. et al. Applied Nonlinear Control vol. 1 (Prentice hall New Jersey, 1991).
53. Liu, Y.-Y., Slotine, J.-J. & Barabási, A.-L. Controllability of complex networks. Nature 473, 167-173 (2011).
54. Nepusz, T. & Vicsek, T. Controlling edge dynamics in complex networks. Nat. Phys. 8, 568-573 (2012).
55. Yan, G., Ren, J., Lai, Y.-C., Lai, C.-H. & Li, B. Controlling complex networks: How much energy is needed? Phys. Rev. Lett. 108, 218703 (2012).
56. Liu, Y.-Y., Slotine, J.-J. & Barabási, A.-L. Observability of complex systems. Proc. Natl Acad. Sci 110, 2460-2465 (2013).
57. Galbiati, M., Delpini, D. & Battiston, S. The power to control. Nat. Phys. 9, 126-128 (2013).
58. Yuan, Z., Zhao, C., Di, Z., Wang, W.-W. & Lai, Y.-C. Exact controllability of complex networks. Nat. Commun. 4, 2447-1-9 (2013).