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. 2022 Sep 23;55(20):451–456. doi: 10.1016/j.ifacol.2022.09.136

Contact Tracing for Disease Containment: a Network-Based Analysis*⁎

Felix Gigler *,⁎⁎⁎, Christoph Urach ⁎⁎, Martin Bicher *,⁎⁎
PMCID: PMC9507115  PMID: 38620972

Abstract

Since the outbreak of the COVID-19 pandemic in spring 2020, the concept of test, trace, and isolate (TTI) was used as a non-pharmaceutical intervention against further spreading of the disease. Hereby, recent contact partners of newly confirmed SARS-CoV-2 infected persons were identified and isolated along with the originally detected case to avoid potential secondary infections. While the policy is, given the compliance of the traced persons, generally deemed efficient, not much is known about network-specific impact factors.

In this work, we aim to evaluate the effectiveness of the TTI strategy when used (1) for diseases with different infectiousness levels and (2) on different contact networks. For the prior, we vary the infection probability per contact, for the latter, we analyse different clustering coefficients. Our goal is to test the validity of two hypotheses: First, we expect the policy to be more efficient if the infectiousness of the disease is small, since the time delay for isolating persons is crucial. Second, due to the implications of the friendship paradox, we expect the policy to be more effective if the clustering coefficient of the underlying contact network is high. We make use of an agent-based network model consisting of three intertwined model parts: an epidemiological SEIR model, a quarantine model and a contact-tracing model. To test the hypotheses, the disease parameters and the clustering coefficient of the underlying contact network are varied.

The simulation results show that, indeed, tracing seems to have a slightly larger containment impact for networks with higher clustering, in particular for fast-spreading diseases. Yet, the effects are small compared to the impact of the infectiousness of the disease. Therefore, we find a significant decrease of the policy effectiveness the higher the transmission probability. The latter implies that the containment impact of tracing and isolating contacts becomes more efficient, if supported by additional measures that limit the infection probability or if applied in periods with low negative seasonality effects.

Keywords: COVID-19, SARS-CoV-2, epidemiology, contact-tracing, agent-based modelling, network modelling, discrete-event simulation, clustering

Footnotes

⁎

The study was partially funded by the Austrian Research Promotion Agency (FFG) Covid 19 Emergency Call

REFERENCES

  1. Amaku M., de Hildebrand Grisi-Filho J.H., Negreiros R.L., Dias R.A., Ferreira F., Neto J.S.F., Cipullo R.I., Marques F.S., Ossada R. Infectious disease surveillance in animal movement networks: An approach based on the friendship paradox. Preventive Veterinary Medicine. 2015;121(3):306–313. doi: 10.1016/j.prevetmed.2015.08.002. [DOI] [PubMed] [Google Scholar]
  2. Barabási A.L., Albert R. Emergence of scaling in random networks. science. 1999;286(5439):509–512. doi: 10.1126/science.286.5439.509. [DOI] [PubMed] [Google Scholar]
  3. Bicher M., Rippinger C., Urach C., Brunmeir D., Siebert U., Popper N. Evaluation of contact-tracing policies against the spread of sars-cov-2 in austria: An agent-based simulation. Medical Decision Making. 2021 doi: 10.1177/0272989X211013306. 0272989X211013306. [DOI] [PubMed] [Google Scholar]
  4. Contreras S., Dehning J., Loidolt M., Zierenberg J., Spitzner F.P., Urrea-Quintero J.H., Mohr S.B., Wilczek M., Wibral M., Priesemann V. The challenges of containing sars-cov-2 via test-trace-and-isolate. Nature communications. 2021;12(1):1–13. doi: 10.1038/s41467-020-20699-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Engelbrecht F.A., Scholes R.J. Test for covid-19 seasonality and the risk of second waves. One Health. 2021;12 doi: 10.1016/j.onehlt.2020.100202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Feld S.L. Why your friends have more friends than you do. American journal of sociology. 1991;96(6):1464–1477. [Google Scholar]
  7. Gilbert E.N. Random graphs. The Annals of Mathematical Statistics. 1959;30(4):1141–1144. [Google Scholar]
  8. Grantz K.H., Lee E.C., McGowan D'Agostino, L. Lee, K.H. Metcalf, C.J.E. Gurley, E.S., Lessler J. Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study. PLoS medicine. 2021;18(4) doi: 10.1371/journal.pmed.1003585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Hagberg A.A., Schult D.A., Swart P.J. In: Proceedings of the 7th Python in Science Conference. Varoquaux G., Vaught T., Millman J., editors.  ; Pasadena, CA USA: 2008. Exploring network structure, dynamics, and function using networkx; pp. 11–15. [Google Scholar]
  10. He B., Zaidi S., Elesedy B., Hutchinson M., Paleyes A., Harling G., Johnson A.M., Whye Teh Y., group R.S.D. Effectiveness and resource requirements of test, trace and isolate strategies for covid in the uk. Royal Society open science. 2021;8(3) doi: 10.1098/rsos.201491. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Holme P. Efficient local strategies for vaccination and network attack. EPL (Europhysics Letters) 2004;68(6):908. [Google Scholar]
  12. Lee H.W., Malik N., Shi F., Mucha P.J. Social clustering in epidemic spread on coevolving networks. Physical Review E. 2019;99(6) doi: 10.1103/PhysRevE.99.062301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Liu X., Huang J., Li C., Zhao Y., Wang D., Huang Z., Yang K. The role of seasonality in the spread of covid-19 pandemic. Environmental research. 2021;195 doi: 10.1016/j.envres.2021.110874. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. McGail A.M., Feld S.L., Schneider J.A. You are only as safe as your riskiest contact: Effective covid-19 vaccine distribution using local network information. Preventive medicine reports. 2022;27 doi: 10.1016/j.pmedr.2022.101787. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Oliveira R.I., Ribeiro R., Sanchis R. Disparity of clustering coefficients in the holme–kim network model. Advances in Applied Probability. 2018;50(3):918–943. [Google Scholar]
  16. Ritchie, H., Mathieu, E., Rodes-Guirao, L., Appel, C., Giattino, C., Ortiz-Ospina, E., Hasell, J., Macdon-ald, B., Beltekian, D., and Roser, M. (2020). Coro-navirus pandemic (covid-19). Our World in Data. Https://ourworldindata.org/coronavirus.
  17. Smith L.E., Potts H.W., Amlôt R., Fear N.T., Michie S., Rubin G.J. Adherence to the test, trace, and isolate system in the uk: results from 37 nationally representative surveys. bmj. 2021:372. doi: 10.1136/bmj.n608. [DOI] [PMC free article] [PubMed] [Google Scholar]

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