Quiescence Raises the Risk of Major Pandemic Outbreaks: Insights from Mathematical Modelling
DOI:
https://doi.org/10.56919/usci.2323.003Keywords:
Quiescence, Pandemic, Outbreak, Mathematical Modelling, InfectiousAbstract
The impact of quiescence or dormancy periods on the dynamics of infectious diseases and their possible involvement in significant pandemic outbreaks are investigated in this study. By using of simulation and mathematical modelling, we show that quiescence greatly raises the likelihood of widespread pandemics. Quiescent people, who are infected but aren’t actively spreading the disease, build up an undiscovered reservoir that can drive virulent epidemics when the conditions are right for them to change from passive to active infectious states. Insights from this study can help public health efforts to lessen the effects of transmissible diseases with quiescent phases on global health. It also advances our understanding of pandemic dynamics.
References
Brauer, F., van den Driessche, P., & Wu, J. (Eds.). (2008). Mathematical Epidemiology. Lecture Notes in Mathematics. pp 81-108. Springer. https://doi.org/10.1007/978-3-540-78911-6
Cox, F. E. (2010). History of the discovery of the malaria parasites and their vectors. Parasites & Vectors, 3(1), 1-9. https://doi.org/10.1186/1756-3305-3-5
Jochen Blath, Felix Hermann, and Martin Slowik (2021). A branching process model for dormancy and seed banks in randomly fluctuating environments. Journal of Mathematical Biology, 83(2):17, https://doi.org/10.1007/s00285-021-01639-6
Jochen Blath, Tobias Paul, Andra's Tobi'as, and Maite Wilke Berenguer. (2022).The impact of dormancy on evolutionary branching. arXiv preprint arXiv:2209.01792,
Lennon, J. T., & Jones, S. E. (2011). Microbial seed banks: the ecological and evolutionary implications of dormancy. Nature Reviews Microbiology, 9(2):119-130, 2011. https://doi.org/10.1038/nrmicro2504
Linda JS Allen. (2003). An introduction to stochastic processes with applications to biology. Prentice Hall, Upper Saddle River, NJ.
Linda JS Allen. (2015). Stochastic population and epidemic models. Mathematical biosciences lecture series, stochastics in biological systems, page 128. https://doi.org/10.1007/978-3-319-21554-9
Matt J Keeling, Mark EJ Woolhouse, Darren J Shaw, Louise Matthews, Margo Chase- Topping, Dan T Haydon, Stephen J Cornell, Jens Kappey, John Wilesmith, and Bryan T Grenfell. Dynamics of the 2001 UK foot and mouth epidemic: stochastic dispersal in a heterogeneous landscape. Science, 294(5543):813-817, 2001. https://doi.org/10.1126/science.1065973
Nil Gural, Liliana Mancio-Silva, Alex B Miller, Ani Galstian, Vincent L Butty, Stuart S Levine, Rapatbhorn Patrapuvich, Salil P Desai, Sebastian A Mikolajczak, Stefan HI Kappe, et al. (2018). In vitro culture, drug sensitivity, and transcriptome of plasmodium vivax hypnozoites. Cell host & microbe, 23(3):395-406. https://doi.org/10.1016/j.chom.2018.01.002
Norman TJ Bailey. (1990). The elements of stochastic processes with applications to the natural sciences. John Wiley & Sons.
RB Schinazi.(1999). Classical and spatial stochastic processes. Birkauser Boston. https://doi.org/10.1007/978-1-4612-1582-0
Sanusi, U., John, S., Mueller, J., & Tellier, A. (2022). Quiescence Generates Moving Average in a Stochastic Epidemiological Model with One Host and Two Parasites. Mathematics, 10(13), 2289. https://doi.org/10.3390/math10132289
Sorrell, I., White, A., Pedersen, A. B., Hails, R. S., & Boots, M. (2009). The evolution of covert, silent infection as a parasite strategy. Proceedings of the Royal Society B: Biological Sciences, 276(1665), 2217-2226. https://doi.org/10.1098/rspb.2008.1915
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 UMYU Scientifica
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
&
