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Modeling Microbial Dynamics: Effects on Environmental and Human Health Open Access


Other title
Infectious diseases
Haldane model
Environmental health
Prevalence algorithm
Exponential model
Blackman model
Fourier transform
Logarithmic model
Global stability analysis
Mature fine tailings
Petroleum hydrocarbons
Sensitivity analysis
Ecological stoichiometry
Time-dependent transmission rate
Monod model
Oil sands
Incidence algorithm
Greenhouse gases
Contois model
End Pit lakes
Moser model
Inverse problem
Indirect transmission
Methane emission
Immunological threshold
Organic matter
Paraffinic solvents
Grasers elemental mismatch
Microbial growth kinetic models
Droop's cell quota model
Type of item
Degree grantor
University of Alberta
Author or creator
Kong, Jude D
Supervisor and department
Wang, Hao (Mathematical and Statistical Sciences)
Examining committee member and department
Wang, Hao (Mathematical and Statistical Sciences)
Siddique, Tariq (Renewable Resources)
Menge, Duncan (Ecology, Evolution, and Environmental Biology)
Lewis, Mark (Mathematical and Statistical Sciences)
Yi, Yingfei (Mathematical and Statistical Sciences)
Kong, Linglong (Mathematical and Statistical Sciences)
Department of Mathematical and Statistical Sciences
Applied Mathematics
Date accepted
Graduation date
2017-11:Fall 2017
Doctor of Philosophy
Degree level
This thesis focuses on formulating and analyzing non linear models for microbial dynamics vis-a-vis human and environmental health. Firstly, we develop and investigate a stoichiometric organic matter decomposition model in a chemostat culture that incorporates the dynamics of grazers. This mechanistic biodegradation model lead to reliable and suggestive ecological insights in the preservation and restoration of our fragile ecosystems. Questions we attempt to answer include: (i) What mechanisms allow microbes and resources to persist uniformly or go extinct? (ii) How do grazing and dead microbial residues affect decomposition? (iii) How can the rate of decomposition be maximized or minimized? Secondly, we designed a greenhouse gas biogenesis model, which may be used to (i) predict the volume of greenhouse gasses emitted at any given time in an oil sands tailing pond and an end pit lake, (ii) calculate the time required to produce a given volume of cumulative greenhouse gases from them and (iii) estimate how long it will take for an oil sands tailing pond and an end pit lake to stop emitting greenhouse gases. Lastly, we formulate and analyze directly and indirectly transmitted infectious disease models. The questions aim to answer include: (i) Why are there irregularities in seasonal patterns of outbreaks amongst different countries? (ii) How can we estimate the transmission function of an infectious disease from a given incidence or prevalence data set? (iii) What is the estimated value of the basic reproduction number in affected regions? (iv) How can we control the period and intensity of pathogenic disease outbreaks?
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Jude D. Kong, Paul Salceanu, and Hao Wang. ``A stoichiometric organic matter decomposition model in a chemostat culture.'' Journal of Mathematical Biology: 1-36 (2017).Jude D. Kong, William Davis, and Hao Wang. ``Dynamics of a cholera transmission model with immunological threshold and natural phage control in reservoir.'' Bulletin of Mathematical Biology, Vol. 76: 2025-2051 (2014).Jude D. Kong, William Davis, Xiong Li, and Hao Wang. ``Stability and Sensitivity Analysis of the iSIR Model for Indirectly Transmitted Infectious Diseases with Immunological Threshold.'' SIAM Journal on Applied Mathematics 74, no. 5: 1418-1441 (2014).Jude D. Kong, Chaochao Jin, and Hao Wang. `` The inverse method for a childhood infectious disease model with its application to pre-vaccination and post-vaccination measles data.'' Bulletin of Mathematical Biology, Vol. 77: 2231-2263 (2015).

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