Have a personal or library account? Click to login
The impact of contact tracing on the spread of COVID-19: an egocentric agent-based model Cover

The impact of contact tracing on the spread of COVID-19: an egocentric agent-based model

Open Access
|Jun 2021

References

  1. Asher, S. 2020. TraceTogether: Singapore turns to wearable contact-tracing Covid tech. BBC, available at: https://www.bbc.com/news/technology-53146360.
  2. Bernard, H. R., Killworth, P. and Sailer, L. 1981. Summary of research on informant accuracy in network data and the reverse small world problem. Connections 4: 11–25.
  3. Bernard, H. R., Killworth, P. D. and Sailer, L. 1982. Informant accuracy in social-network data V. An experimental attempt to predict actual communication from recall data. Social Science Research 11: 30–66.
  4. Bernard, H. R., Killworth, P. D., Kronenfeld, D. and Sailer, L. 1984. On the validity of retrospective data: the problem of informant accuracy. Annual Review of Anthropology 13: 17.
  5. Bidart, C. and Charbonneau, J. 2011. How to generate personal networks: issues and tools for a sociological perspective. Field Methods 23: 266–286.
  6. Borgatti, S. E., Everett, M. M. and Johnson, J. 2013. Analyzing Social Networks Sage, Thousand Oaks, CA.
  7. Brashears, M. E. and Quintane, E. 2015. The microstructures of network recall: how social networks are encoded and represented in human memory. Social Networks 41: 113–126.
  8. Breashears, M. E., Hoagland, E. and Quintane, E. 2016. Sex and network recall accuracy. Social Networks 44: 74–84.
  9. Brewer, D. D. 2000. Forgetting in the recall-based elicitation of personal and social networks. Social Networks 22: 29–43.
  10. Browne, K. 2005. Snowball sampling: using social networks to research non-heterosexual women. International Journal of Social Research Methodology 8: 47–60.
  11. Burt, R. S. 1984. Network items and the general social survey. Social Networks 6: 293–339.
  12. Butts, C. T. 2003. Network inference, error, and informant (in) accuracy: a Bayesian approach. Social Networks 25: 103–140.
  13. Butts, C. T. 2008. 4. A relational event framework for social action. Sociological Methodology 38: 155–200.
  14. Cheng, H. Y., Jian, S. W., Liu, D. P., Ng, T. C., Huang, W. T., Lin, H. -H. and Team, F. T. T. C. -O. I. 2020. Contact tracing assessment of COVID-19 transmission dynamics in Taiwan and risk at different exposure periods before and after symptom onset. JAMA Internal Medicine 180(9): 1156–1163, doi: 10.1001/jamainternmed.2020.2020.
  15. Clauset, A., Shalizi, C. R. and Newman, M. E. J. 2009. Power-Law distributions in empirical data. SIAM Review, 51(4): 661–703, doi: 10.1137/070710111.
  16. Cohn, S. and O’Brien, M. 2020. How physicians used contact tracing 500 years ago to control the bubonic plague, available at: https://blogs.ed.ac.uk/covid19perspectives/2020/06/10/how-physicians-used-contact-tracing-500-years-ago-to-control-the-bubonic-plague-by-samuel-cohn-and-mona-obrien/.
  17. Corman, S. R. 1996. “Cellular automata as models of unintended consequences of organizational communication”, In Watts, J. C. and VanLear, A. (Eds), Dynamic Patterns in Communication Processes Sage, Thousand Oaks, CA, pp. 191–212.
  18. Corman, S. R. and Scott, C. R. 1994. Perceived networks, activity foci, and observable communication in social collectives. Communication Theory 4: 171–190.
  19. Corman, S. R., Steiner, E., Proulx, J. D., Dutta, A., Yahja, A., Poole, M. S. and Bliss, D. W. B. 2021. Revisiting the accuracy problem in network analysis using a unique dataset. Social Networks 66: 1–9.
  20. Craik, F. I. M. and Lockhart, R. S. 1972. Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11(6): 671–684, doi: 10.1016/S0022-5371(72)80001-X.
  21. Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J. and Tranmer, M. 2015. Social Network Analysis for Ego-nets: Social Network Analysis for Actor-centred Networks Sage, Thousand Oaks, CA.
  22. Danquah, L. O., Hasham, N., MacFarlane, M., Conteh, F. E., Momoh, F., Tedesco, A. A. and Weiss, H. A. 2019. Use of a mobile application for Ebola contact tracing and monitoring in northern Sierra Leone: a proof-of-concept study. BMC Infectious Diseases 19: 810.
  23. de Anda-Jáuregui, G., Guzmán, P. and Hernández-Rosales, M. 2020. The contact network of Mexico City. arXiv preprint arXiv:2007.14596.
  24. Del Valle, S. Y., Hyman, J. M., Hethcote, H. W. and Eubank, S. G. 2007. Mixing patterns between age groups in social networks. Social Networks 29: 539–554.
  25. Donnelly, C. A., Ghani, A. C., Leung, G. M., Hedley, A. J., Fraser, C., Riley, S. and Chan, K. P. 2003. Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. The Lancet 361: 1761–1766.
  26. Eames, K. T. and Keeling, M. J. 2003. Contact tracing and disease control. Proceedings of the Royal Society of London. Series B: Biological Sciences 270: 2565–2571.
  27. Eames, K. T., Webb, C., Thomas, K., Smith, J., Salmon, R. and Temple, J. M. F. 2010. Assessing the role of contact tracing in a suspected H7N2 influenza A outbreak in humans in Wales. BMC Infectious Diseases 10: 141.
  28. Eddens, K. and Fagan, J. M. 2018. Comparing nascent approaches for gathering alter-tie data for egocentric studies. Social Networks, 55: 130–141, doi: 10.1016/j.socnet.2018.05.009.
  29. Elmer, T. and Stadtfeld, C. 2020. Depressive symptoms are associated with social isolation in face-to-face interaction networks. Scientific Reports 10: 1–12.
  30. Endo, A. 2020. Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China. Wellcome Open Research 5(67): 1–, doi: 10.12688/wellcomeopenres.15842.3.
  31. Epstein, J. M. 2009. Modelling to contain pandemics. Nature 460: 687–687.
  32. Feld, S. L. 1981. The focused organization of social ties. American Journal of Sociology 86: 1015–1035.
  33. Fine, P., Eames, K. and Heymann, D. L. 2011. “Herd Immunity”: A rough guide. Clinical Infectious Diseases, 52(7): 911–916, doi: 10.1093/cid/cir007.
  34. Freeman, L. C., Romney, A. K. and Freeman, S. C. 1987. Cognitive structure and informant accuracy. American Anthropologist 89: 310–325.
  35. Gilovich, T. 1991. How We Know What isn’t so: the Fallibility of Human Reason in Everyday Life The Free Press, New York.
  36. Greenberg, A. 2020. How Apple and Google are enabling Covid-19 contact-tracing. WIRED, available at: https://www.wired.com/story/apple-google-bluetooth-contact-tracing-covid-19/.
  37. Gurley, E. 2020. COVID-19 contact tracing. Online Coursera lecture. John Hopkins University, available at: https://www.coursera.org/learn/covid-19-contact-tracing.
  38. Hammond, R. A. 2015. “Considerations and best practices in agent-based modeling to inform policy”, In Wallace, R., Geller, A. and Ogawa, A. (Eds), Assessing the Use of Agent-based Models for Tobacco Regulation, National Academies Press, Washington, DC, pp. 164–191.
  39. Hébert-Dufresne, L., Althouse, B. M., Scarpino, S. V. and Allard, A. 2020. Beyond R0: the importance of contact tracing when predicting epidemics. arXiv preprint arXiv:2002.04004.
  40. Hellewell, J., Abbott, S., Gimma, A., Bosse, N. I., Jarvis, C. I., Russell, T. W. and Flasche, S. 2020. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet Global Health 8: 488–496.
  41. Hlebec, V. and Ferligoj, A. 2001. Respondent mood and the instability of survey network measurements. Social Networks 23: 125–140.
  42. Hogan, B., Janulis, P., Phillips, G., Melville, J., Mustanski, B., Contractor, N. and Birkett, M. 2020. Assessing the stability of egocentric networks over time using the digital participant-aided sociogram tool Network Canvas. Network Science 8(2): 204–222, doi: 10.1017/nws.2019.27.
  43. Hollstein, B., Töpfer, T. and Pfeffer, J. 2020. Collecting egocentric network data with visual tools: A comparative study. Network Science 8(2): 223–250, doi: 10.1017/nws.2020.4.
  44. Hsieh, Y. P. 2014. Check the phone book: testing information and communication technology (ICT) recall aids for personal network surveys. Social Networks 41: 101–112.
  45. Imai, N., Cori, A., Dorigatti, I., Baguelin, M., Donnelly, C. A., Riley, S. and Ferguson, N. M. 2020. Report 3: Transmissibility of 2019-nCoV Imperial College, London.
  46. Johnson, J. C. 1990. Selecting Ethnographic Informants Sage, Newbury Park, CA.
  47. Kendall, C., Kerr, L. R., Gondim, R. C., Werneck, G. L., Macena, R. H. M., Pontes, M. K. and McFarland, W. 2008. An empirical comparison of respondent-driven sampling, time location sampling, and snowball sampling for behavioral surveillance in men who have sex with men, Fortaleza, Brazil. AIDS and Behavior 12: 97.
  48. Killworth, P. and Bernard, H. 1976. Informant accuracy in social network data. Human Organization 35: 269–286.
  49. Klinkenberg, D., Fraser, C. and Heesterbeek, H. 2006. The effectiveness of contact tracing in emerging epidemics. PLoS ONE 1: e12.
  50. Kogovšek, T., Ferligoj, A., Coenders, G. and Saris, W. E. 2002. Estimating the reliability and validity of personal support measures: full information ML estimation with planned incomplete data. Social Networks 24: 1–20.
  51. Krackhardt, D. 1987. Cognitive social structures. Social Networks 9: 109–134.
  52. Kretzschmar, M. E., Rozhnova, G., Bootsma, M. C. J., van Boven, M., van de Wijgert, J. H. H. M. and Bonten, M. J. M. 2020. Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study. The Lancet Public Health 5(8): e452–e459, doi: 10.1016/S2468-2667(20)30157-2.
  53. Kucharski, A. J., Klepac, P., Conlan, A., Kissler, S. M., Tang, M. and Fry, H. 2020a. Effectiveness of isolation, testing, contact tracing and physical distancing on reducing transmission of SARS-CoV-2 in different settings. MedRxiv, 2004-2023.
  54. Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S. and Flasche, S. 2020b. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet Infectious Diseases 20: 553–558.
  55. Larson, J. Jr 2012. “Computer simulation methods for groups”, In Hollingshead, A. and Poole, M. S. (Eds), Research Methods for Studying Groups and Teams Routledge, New York, NY, pp. 329–357.
  56. Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y. and Feng, Z. 2020a. Early transmission dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. New England Journal of Medicine 382: 1199–1207.
  57. Li, T., Liu, Y., Li, M., Qian, X. and Dai, S. Y. 2020b. Mask or no mask for COVID-19: a public health and market study. PLoS ONE 15(8): e0237691, doi: 10.1371/journal.pone.0237691.
  58. Liu, T., Hu, J., Xiao, J., He, G., Kang, M., Rong, Z. and Ma, W. 2020b. Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China. bioRxiv. doi: 10.1101/2020.01.25.919787.
  59. Liu, Y., Gayle, A. A., Wilder-Smith, A. and Rocklöv, J. 2020a. The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of Travel Medicine.
  60. McCarty, C., Lubbers, M. J., Vacca, R. and Molina, J. L. 2019. Conducting Personal Network Research: A Practical Guide Guilford Publications, New York.
  61. Macke, B. A. and Maher, J. E. 1999. Partner notification in the United States: an evidence-based review. American Journal of Preventive Medicine 17: 230–242.
  62. Majumder, M. and Mandl, K. D. 2020. Early transmissibility assessment of a Novel Coronavirus in Wuhan, China (January 26), available at: http://dx.doi.org/10.2139/ssrn.3524675.
  63. Mandalakas, A. M., Ngo, K., Alonso Ustero, P., Golin, R., Anabwani, F., Mzileni, B. and Stevens, R. 2017. BUTIMBA: Intensifying the hunt for child TB in Swaziland through household contact tracing. PLoS ONE 12: e0169769.
  64. Marin, A. 2004. Are respondents more likely to list alters with certain characteristics?: implications for name generator data. Social Networks 26: 289–307.
  65. Marineau, J. E., Labianca, G. J., Brass, D. J., Borgatti, S. P. and Vecchi, P. 2018. Individuals’ power and their social network accuracy: a situated cognition perspective. Social Networks 54: 145–161.
  66. Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R. and Heijne, J. 2008. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med 5: e74.
  67. Omodei, E., Brashears, M. E. and Arenas, A. 2017. A mechanistic model of human recall of social network structure and relationship affect. Scientific Reports 7: 17133.
  68. Perry, B. L., Pescosolido, B. A. and Borgatti, S. P. 2018. Egocentric Network Analysis: Foundations, Methods, and Models, Vol. 44 Cambridge University Press.
  69. Pichery, C. 2014. “Sensitivity analysis”, In Wexler, P. (Ed.), Encyclopedia of Toxicology Springer, New York.
  70. Pilny, A. and Huber, C. J. 2021. An egocentric network contact tracing experiment: testing different procedures to elicit contacts and places. International Journal of Environmental Research and Public Health 18: 1466.
  71. Pilny, A., Proulx, J. D., Dinh, L. and Bryan, A. L. 2017. An adapted structurational framework for the emergence of communication networks. Communication Studies 68: 72–94.
  72. Prem, K., Cook, A. R. and Jit, M. 2017. Projecting social contact matrices in 152 countries using contact surveys and demographic data. PLoS Computational Biology 13: e1005697.
  73. Quintane, E. 2012. “Comparing networks: a structural examination of the correspondence between behavioral and recall networks”, In Lusher, D., Koskinen, J. and Robins, G. L. (Eds), Exponential Random Graph Models for Social Networks: Theories, Methods and Applications, Vol. 35 Cambridge University Press, Cambridge, pp. 272–284.
  74. Rainwater-Lovett, K., Rodriguez-Barraquer, I. and Moss, W. J. 2016. “Chapter 18 - viral epidemiology: tracking viruses with smartphones and social media”, In Katze, M. G., Korth, M. J., Law, G. L. and Nathanson, N. (Eds), Viral Pathogenesis, 3rd ed., Academic Press, Boston, pp. 241–252.
  75. Riou, J. and Althaus, C. L. 2020. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance 25.
  76. Rothwell, J. 2020. Americans’ social contacts during the COVID-19 pandemic. GALLUP BLOG, APRIL 21, 2020, available at: https://news.gallup.com/opinion/gallup/308444/americans-social-contacts-during-covid-pandemic.aspx.
  77. Schneider, M., Thornell, C., Parvaneh, D. and Chakraborty, R. 2020. The big lesson from South Korea’s coronavirus response. Vox, available at: https://www.vox.com/videos/2020/8/6/21356265/south-korea-coronavirus-response-testing.
  78. Smith, E. B., Brands, R. A., Brashears, M. E. and Kleinbaum, A. M. 2020. Social networks and cognition. Annual Review of Sociology, 46(1): 159–174, doi: 10.1146/annurev-soc-121919-054736.
  79. Ten Broeke, G., Van Voorn, G. and Ligtenberg, A. 2016. Which sensitivity analysis method should I use for my agent-based model?. Journal of Artificial Societies and Social Simulation 19(1): 5.
  80. Tulving, E. 1974. Cue-dependent forgetting: when we forget something we once knew, it does not necessarily mean that the memory trace has been lost; it may only be inaccessible. American Scientist 62: 74–82.
  81. Valente, T. W. 2010. Social Networks and Health: Models, Methods, and Applications Oxford University Press, Oxford.
  82. Wilensky, U. 1999. Center for connected learning and computer-based modeling. NetLogo, Northwestern University.
  83. Wu, J. T., Leung, K. and Leung, G. M. 2020. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet 395: 689–697.
  84. Zhang, S., Diao, M., Yu, W., Pei, L., Lin, Z. and Chen, D. 2020. Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: a data-driven analysis. International Journal of Infectious Diseases 93: 201–204.
  85. Zhao, S., Lin, Q., Ran, J., Musa, S. S., Yang, G., Wang, W. and Wang, M. H. 2020. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: a data-driven analysis in the early phase of the outbreak. International Journal of Infectious Diseases 92: 214–217.
DOI: https://doi.org/10.21307/connections-2021.022 | Journal eISSN: 2816-4245 | Journal ISSN: 0226-1766
Language: English
Page range: 25 - 46
Published on: Jun 17, 2021
Published by: International Network for Social Network Analysis (INSNA)
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2021 Andrew Pilny, Lin Xiang, Corey Huber, Will Silberman, Sean Goatley-Soan, published by International Network for Social Network Analysis (INSNA)
This work is licensed under the Creative Commons Attribution 4.0 License.