Abstract
In the pursuit of accurate infectious disease forecasting, micro-level contact modeling in contact networks emerges as a pivotal element. This research delves into the intricacies of nuanced micro-level modeling, presenting adaptable models tailored for specific locations, derived from a refined travel demand model. In our experiments, we observed that varied encounter patterns among individuals directly influence infection dynamics. Additionally, we observe distinct trends in the spreading dynamics between temporal dynamic networks and their static counterparts for certain encounter models. The study underscores the need for a deeper appreciation of micro-level encounter patterns in epidemiological modeling. Such understanding is pivotal in shaping effective interventions and public health strategies during pandemic scenarios.
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References
Balcan, D., Gonçalves, B., Hao, H., Ramasco, J.J., Colizza, V., Vespignani, A.: Modeling the spatial spread of infectious diseases: the GLobal epidemic and mobility computational model. J. Comput. Sci. 1(3), 132–145 (2010)
Dalziel, B.D., Pourbohloul, B., Ellner, S.P.: Human mobility patterns predict divergent epidemic dynamics among cities. Proc. Roy. Soc. B: Biol. Sci. 280(1766), 20130763 (2013)
Eubank, S., et al.: Modelling disease outbreaks in realistic urban social networks. Nature 429(6988), 180–184 (2004)
Firth, J.A., Hellewell, J., Klepac, P., Kissler, S., Kucharski, A.J., Spurgin, L.G.: Using a real-world network to model localized COVID-19 control strategies. Nat. Med. 26(10), 1616–1622 (2020)
Glass, L.M., Glass, R.J.: Social contact networks for the spread of pandemic influenza in children and teenagers. BMC Public Health 8(1), 61 (2008)
Heinrichs, M.: TAPAS: travel-activity-pattern simulation - parallelisiertes mikroskopisches verkehrsnachfragemodell. In: Next GEneration Forum 2011, pp. 74–74. Deutsches Zentrum für Luft und Raumfahrt e.V. (2011)
Hekmati, A., Luhar, M., Krishnamachari, B., Matarić, M.: Simulating COVID-19 classroom transmission on a university campus. Proc. Natl. Acad. Sci. 119(22), e2116165119 (2022)
Hethcote, H.W.: The mathematics of infectious diseases. SIAM Rev. 42(4), 599–653 (2000)
Hinch, R., et al.: OpenABM-covid19-an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing. PLoS Comput. Biol. 17(7), e1009146 (2021)
Holme, P., Saramäki, J.: Temporal networks. Phys. Rep. 519(3), 97–125 (2012)
Kerr, C.C., et al.: Covasim: an agent-based model of COVID-19 dynamics and interventions. PLoS Comput. Biol. 17(7), e1009149 (2021)
Klise, K., Beyeler, W., Finley, P., Makvandi, M.: Analysis of mobility data to build contact networks for COVID-19. PLoS ONE 16(4), e0249726 (2021)
Lee, B., et al.: Designing a multi-agent occupant simulation system to support facility planning and analysis for COVID-19. In: Designing Interactive Systems Conference 2021, pp. 15–30. ACM (2021)
Leitch, J., Alexander, K.A., Sengupta, S.: Toward epidemic thresholds on temporal networks: a review and open questions. Appl. Network Sci. 4(1), 105 (2019)
Liu, F., Li, X., Zhu, G.: Using the contact network model and metropolis-Hastings sampling to reconstruct the COVID-19 spread on the “diamond princess.’’. Sci. Bull. 65(15), 1297–1305 (2020)
Müller, S.A., et al.: Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data. PLoS ONE 16(10), e0259037 (2021)
Reveil, M., Chen, Y.-H.: Predicting and preventing COVID-19 outbreaks in indoor environments: an agent-based modeling study. Sci. Rep. 12(1), 16076 (2022)
Rocha, L.E.C., Liljeros, F., Holme, P.: Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS Comput. Biol. 7(3), 1–9 (2011)
Sharkey, K.J., et al.: Pair-level approximations to the SPATIO-temporal dynamics of epidemics on asymmetric contact networks. J. Math. Biol. 53(1), 61–85 (2006)
Thurner, S., Klimek, P., Hanel, R.: A network-based explanation of why most COVID-19 infection curves are linear. Proc. Natl. Acad. Sci. 117(37), 22684–22689 (2020)
Vestergaard, C.L., Génois, M.: Temporal Gillespie algorithm: fast simulation of contagion processes on time-varying networks. PLoS Comput. Biol. 11(10), 1–28 (2015)
Ying, F., O’Clery, N.: Modelling COVID-19 transmission in supermarkets using an agent-based model. PLoS ONE 16(4), e0249821 (2021)
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Diallo, D., Schönfeld, J., Hecking, T. (2024). Travel Demand Models for Micro-Level Contact Network Modeling. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-031-53499-7_27
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DOI: https://doi.org/10.1007/978-3-031-53499-7_27
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