Modeling latent infection transmissions through biosocial stochastic dynamics
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DOI: 10.1371/journal.pone.0241163
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Cited by:
- Đorđević, J. & Papić, I. & Šuvak, N., 2021. "A two diffusion stochastic model for the spread of the new corona virus SARS-CoV-2," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
- Sebastian A Müller & Michael Balmer & William Charlton & Ricardo Ewert & Andreas Neumann & Christian Rakow & Tilmann Schlenther & Kai Nagel, 2021. "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, Public Library of Science, vol. 16(10), pages 1-32, October.
- Weiwei Zhang & Shiyong Liu & Nathaniel Osgood & Hongli Zhu & Ying Qian & Peng Jia, 2023. "Using simulation modelling and systems science to help contain COVID‐19: A systematic review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 40(1), pages 207-234, January.
- Tadić, Bosiljka & Mitrović Dankulov, Marija & Melnik, Roderick, 2023. "Evolving cycles and self-organised criticality in social dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
- Nagel, Kai & Rakow, Christian & Müller, Sebastian A., 2021. "Realistic agent-based simulation of infection dynamics and percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
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