← Back to Volume 10, Issue 9

Modeling the Impact of Human Mobility on Influenza Transmission Dynamics in Urban Settings: A Data-Driven SEIR and Time-Varying transmission Analysis

Wenkai Bi
Vol.10, No.9, pp.33-41, 2026 DOI: 10.62051/ijsspa.v10n9.04 September 28, 2026
Download PDF
Abstract: Human mobility is a key driver of infectious disease transmission, yet its role in shaping seasonal influenza dynamics remains insufficiently characterized, particularly in comparison to COVID-19. This study aims to quantify the association between mobility patterns and influenza transmissibility and to evaluate the potential impact of targeted mobility interventions. We conducted a state-level analysis in the United States by integrating mobility data from Google and Apple reports (February 2020 to January 2022) with weekly influenza-like illness data. The time-varying effective reproduction number (Rt) was estimated using a standard epidemiological framework. Cross-correlation analysis was applied to identify lag structures between mobility indicators and RtR_tRt​, followed by linear mixed-effects modeling to quantify associations while accounting for spatial heterogeneity. Model performance was evaluated using the Akaike Information Criterion. Regression-derived coefficients were subsequently incorporated into a Susceptible–Exposed–Infectious–Recovered (SEIR) model to simulate intervention scenarios. Influenza transmissibility exhibited clear seasonal variation, with Rt exceeding 1.0 during early-season growth and declining below this threshold in later months. Mobility changes were found to precede fluctuations in Rt by 3 to 6 weeks, with lag patterns varying by activity type. Residential activity and walking were associated with reduced transmission, whereas park visits and grocery-related mobility were identified as significant drivers of increased Rt. The best-performing model achieved the lowest information criteria values and explained a substantial proportion of variability in transmission dynamics. Simulation results indicated that targeted reductions in high-impact mobility activities, particularly park-related mobility, could substantially suppress epidemic growth. Combined moderate interventions across multiple mobility types also demonstrated meaningful effects. Human mobility is a significant and temporally structured determinant of seasonal influenza transmission. Activity-specific mobility patterns provide a predictive window for intervention and offer actionable insights for public health strategies. Targeted, mobility-informed interventions may represent an effective and less disruptive approach to mitigating seasonal influenza outbreaks.
Keywords: Human Mobility, Seasonal Influenza, Public Health Intervention, Influenza Transmissibility
APA Citation: Wenkai Bi (2026). Modeling the Impact of Human Mobility on Influenza Transmission Dynamics in Urban Settings: A Data-Driven SEIR and Time-Varying transmission Analysis. International Journal of Social Sciences and Public Administration, 10(9), 33-41. https://doi.org/10.62051/ijsspa.v10n9.04

References

  1. Tizzoni, M., Bajardi, P., Decuyper, A., Kon Kam King, G., Schneider, C. M., Blondel, V., Smoreda, Z., González, M. C., & Colizza, V. (2014). On the use of human mobility proxies for modeling epidemics. PLOS Computational Biology, 10(7), e1003716. https://doi.org/10.1371/journal.pcbi.1003716
  2. Li, Y., Wang, X., Campbell, H., Nair, H., & Usher Network for COVID-19 Evidence Reviews (UNCOVER) group. (2021). The association of community mobility with the time-varying reproduction number (R) of SARS-CoV-2: A modelling study across 330 local UK authorities. The Lancet Digital Health, 3(11), e676–e683. https://doi.org/10.1016/S2468-2667(21)00177-4
  3. Charu, V., Zeger, S., Gog, J., Bjørnstad, O. N., Kissler, S., Simonsen, L., Grenfell, B. T., & Viboud, C. (2017). Human mobility and the spatial transmission of influenza in the United States. PLOS Computational Biology, 13(2), e1005382. https://doi.org/10.1371/journal.pcbi.1005382
  4. Xia, J., Yin, K., Yue, Y., Li, Q., Wang, X., Hu, D., Wang, X., Du, Z., Cowling, B. J., Chen, E., & Zhou, Y. (2023). Impact of human mobility on COVID-19 transmission according to mobility distance, location, and demographic factors in the Greater Bay Area of China: Population-based study. JMIR Public Health and Surveillance, 9(2), e39588. https://doi.org/10.2196/39588
  5. Schläpfer, M., Matter, P., Seidel, T., Rüegg, M., Hilber, N., Turbé, A., & Helbing, D. (2020). The impact of mobility on COVID-19 transmission. Science, 368(6491), 493–497. https://doi.org/10.1126/science.abb9789
  6. Badr, H. S., Du, H., Marshall, M., Dong, E., Squires, S. L., & Gardner, L. M. (2020). Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study. The Lancet Infectious Diseases, 20(11), 1247–1254. https://doi.org/10.1016/S1473-3099(20)30553-2
  7. Cori, A., Ferguson, N. M., Fraser, C., & Cauchemez, S. (2013). A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of Epidemiology, 178(9), 1505–1512. https://doi.org/10.1093/aje/kwt133
  8. Cowling, B. J., Lau, L. L., Wu, P., Wong, H. W., Fang, V. J., Riley, S., & Peiris, J. S. M. (2010). Entry screening to delay local transmission of 2009 pandemic influenza A (H1N1). BMC Infectious Diseases, 10, 82. https://doi.org/10.1186/1471-2334-10-82
  9. Kraemer, M. U. G., Yang, C. H., Gutierrez, B., Wu, C. H., Klein, B., Pigott, D. M., & Scarpino, S. V. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6491), 493–497. https://doi.org/10.1126/science.abb4218
  10. Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., & Feng, Z. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England Journal of Medicine, 382(13), 1199–1207. https://doi.org/10.1056/NEJMoa2001316
  11. Gatalo, Z., Tseng, Y. J., Kirby, A., & Santillana, M. (2021). Relative utility of Google and Apple mobility data for predicting COVID-19 dynamics in the United States. Epidemiology, 32(5), 645–653. https://doi.org/10.1097/EDE.0000000000001364
  12. Thompson, R. N., Stockwin, J. E., van Gaalen, R. D., Polonsky, J. A., Kamvar, Z. N., Demarsh, P. A., Dahlqwist, E., Li, S., Miguel, E., Jombart, T., Lessler, J., Cauchemez, S., & Cori, A. (2019). Improved inference of time-varying reproduction numbers during infectious disease outbreaks. Epidemics, 29, 100356. https://doi.org/10.1016/j.epidem.2019.100356
  13. Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R² from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x
  14. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Springer-Verlag.
  15. Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A, 115(772), 700–721. https://doi.org/10.1098/rspa.1927.0118
  16. Soetaert, K., Cash, J., & Mazzia, F. (2010). Solving differential equations in R. Springer.