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Relevant bibliographies by topics / Mathematical modelling - Epidemiology

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Academic literature on the topic 'Mathematical modelling - Epidemiology'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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Journal articles on the topic "Mathematical modelling - Epidemiology"

1

Michael, E. "Mathematical modelling of disease epidemiology." Parasitology Today 9, no. 11 (November 1993): 397–99. http://dx.doi.org/10.1016/0169-4758(93)90042-e.

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Gubernov, Vladimir, Sergey Minaev, Hong G. Im, Nam Il Kim, and Kaoru Maruta. "Modelling in Ecology, Epidemiology and Evolution." Mathematical Modelling of Natural Phenomena 13, no. 6 (2018): E2. http://dx.doi.org/10.1051/mmnp/2018066.

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Yanchevskaya, E. Ya, and O. A. Mesnyankina. "Mathematical Modelling and Prediction in Infectious Disease Epidemiology." RUDN Journal of Medicine 23, no. 3 (December 15, 2019): 328–34. http://dx.doi.org/10.22363/2313-0245-2019-23-3-328-334.

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Mathematical modeling of diseases is an urgent problem in the modern world. More and more researchers are turning to mathematical models to predict a particular disease, as they help the most correct and accurate study of changes in certain processes occurring in society. Mathematical modeling is indispensable in certain areas of medicine, where real experiments are impossible or difficult, for example, in epidemiology. The article is devoted to the historical aspects of studying the possibilities of mathematical modeling in medicine. The review demonstrates the main stages of development, achievements and prospects of this direction.

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Huppert, A., and G. Katriel. "Mathematical modelling and prediction in infectious disease epidemiology." Clinical Microbiology and Infection 19, no. 11 (November 2013): 999–1005. http://dx.doi.org/10.1111/1469-0691.12308.

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Mindell, J. "Mathematical modelling of health impacts." Journal of Epidemiology & Community Health 59, no. 8 (August 1, 2005): 617–18. http://dx.doi.org/10.1136/jech.2005.034355.

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de Jong, Mart C. M. "Mathematical modelling in veterinary epidemiology: why model building is important." Preventive Veterinary Medicine 25, no. 2 (December 1995): 183–93. http://dx.doi.org/10.1016/0167-5877(95)00538-2.

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Morozov, A. "Preface." Mathematical Modelling of Natural Phenomena 13, no. 3 (2018): E1. http://dx.doi.org/10.1051/mmnp/2018041.

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VYNNYCKY, EMILIA. "13. The application of reproduction number concepts to tuberculosis Vynnycky E, Fine PEM. Epidemiol Infect 1998; 121: 309–324." Epidemiology and Infection 133, S1 (October 2005): S45—S47. http://dx.doi.org/10.1017/s0950268805004334.

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Epidemiology & Infection probably attracts more papers on mathematical modelling of infectious diseases than does any other epidemiology journal. The most important modelling papers published in the journal were probably those of Anderson and May during the 1980s, which laid the foundations for much of the subsequent modelling work carried out by themselves and their colleagues. Since the start of their partnership, they authored 17 articles between them in the journal, including work quantifying the effect of different vaccination strategies against measles and rubella [1, 2], on the epidemiology of rubella in the United Kingdom [3], and on the effect of age-dependent contact between individuals on the critical level of vaccination coverage required for control [4]. The latter work, published in 1985, was particularly important, since it described methods for incorporating realistic assumptions about (heterogeneous) mixing between individuals into models, an issue which was beginning to be addressed in the mathematical literature but which had not yet reached many epidemiological journals. Other important modelling work published in Epidemiology and Infection includes that of McLean et al. (reproduced in this edition) on the control of measles in developing countries [5, 6], and by Garnett and Grenfell on the epidemiology of varicella zoster in developed countries [7, 8].

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Groner, Maya L., Luke A. Rogers, Andrew W. Bateman, Brendan M. Connors, L. Neil Frazer, Sean C. Godwin, Martin Krkošek, et al. "Lessons from sea louse and salmon epidemiology." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1689 (March 5, 2016): 20150203. http://dx.doi.org/10.1098/rstb.2015.0203.

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Effective disease management can benefit from mathematical models that identify drivers of epidemiological change and guide decision-making. This is well illustrated in the host–parasite system of sea lice and salmon, which has been modelled extensively due to the economic costs associated with sea louse infections on salmon farms and the conservation concerns associated with sea louse infections on wild salmon. Consequently, a rich modelling literature devoted to sea louse and salmon epidemiology has been developed. We provide a synthesis of the mathematical and statistical models that have been used to study the epidemiology of sea lice and salmon. These studies span both conceptual and tactical models to quantify the effects of infections on host populations and communities, describe and predict patterns of transmission and dispersal, and guide evidence-based management of wild and farmed salmon. As aquaculture production continues to increase, advances made in modelling sea louse and salmon epidemiology should inform the sustainable management of marine resources.

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Blance, Andrew, Yu-Kang Tu, and Mark S. Gilthorpe. "A multilevel modelling solution to mathematical coupling." Statistical Methods in Medical Research 14, no. 6 (December 2005): 553–65. http://dx.doi.org/10.1191/0962280205sm418oa.

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Dissertations / Theses on the topic "Mathematical modelling - Epidemiology"

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Pask, Melanie Juanita. "The epidemiology and mathematical modelling of malaria transmission." Thesis, Imperial College London, 1989. http://hdl.handle.net/10044/1/47611.

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Neilson, Stuart D. "Mathematical modelling of inherent susceptibility to fatal diseases." Thesis, Brunel University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262570.

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Turner, Elizabeth L. "Marginal modelling of capture-recapture data." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103302.

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The central theme of this dissertation is the development of a new approach to conceptualize and quantify dependence structures of capture-recapture data for closed populations, with specific emphasis on epidemiological applications. We introduce a measure of source dependence: the Coefficient of Incremental Dependence (CID). Properties of this and the related Coefficient of Source Dependence (CSD) of Vandal, Walker, and Pearson (2005) are presented, in particular their relationships to the conditional independence structures that can be modelled by hierarchical joint log-linear models (HJLLM). From these measures, we develop a new class of marginal log-linear models (MLLM), which we compare and contrast to HJLLMs.
We demonstrate that MLLMs serve to extend the universe of dependence structures of capture-recapture data that can be modelled and easily interpreted. Furthermore, the CIDs and CSDs enable us to meaningfully interpret the parameters of joint log-linear models previously excluded from the analysis of capture-recapture data for reasons of non-interpretability of model parameters.
In order to explore the challenges and features of MLLMs, we show how to produce inference from them under both a maximum likelihood and a Bayesian paradigm. The proposed modelling approach performs well and provides new insight into the fundamental nature of epidemiological capture-recapture data.

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Bentil, Daniel Ekow. "Aspects of dynamic pattern generation in embryology and epidemiology." Thesis, University of Oxford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.276528.

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Agaba, Grace Omeche. "Mathematical modelling of epidemics with account for population awareness." Thesis, University of Sussex, 2016. http://sro.sussex.ac.uk/id/eprint/65367/.

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In this thesis I developed and analysed several mathematical models that describe the dynamics of infectious diseases spreading in a population simultaneously with people becoming aware of the presence of the disease and thus modifying their behaviour. This is achieved using compartmental models, with further extensions to models with time delays and the administration of vaccines. Resulting mathematical models were analysed using the techniques of dynamical systems and bifurcations theory, complemented by direct numerical simulations. Design of optimal strategies maximising the reduction of infection rates subject to logistical constraints were studied within the new modelling framework and with a view to be used in realistic contexts. Of particular interest is the design and analysis of the impact of local and global awareness campaigns, as well as the administration of vaccines to minimise the spread of infections.

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Melegaro, Alessia. "Epidemiology, mathematical modelling and economics of Streptococcus pneumoniae : assessing the potential impact of vaccination." Thesis, University of Warwick, 2005. http://wrap.warwick.ac.uk/61760/.

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This thesis explores aspects of Streptococcus pneumoniae (pneumococcus) epidemiology and control, in view of the possible widespread introduction of conjugate vaccines in England and Wales. A review and analysis of a range of different epidemiological data sources showed that the burden of pneumococcal disease in England and Wales is high and remains mostly a condition of the very young and the elderly. A meta-analysis demonstrated the effectiveness of the polysaccharide vaccine against invasive pneumococcal disease among healthy elderly, to whom vaccination was not recommended at the start of this work. Using this result, a costeffectiveness analysis assessed the economic acceptability of such a programme, from the public health perspective. A better understanding of pneumococcal carriage and transmission is required to assess the effectiveness and cost-effectiveness of mass vaccination strategies with the pneumococcal conjugate vaccine. A novel model framework was developed and fitted to a longitudinal dataset of carriage in UK families. The results demonstrated an inverse relationship between duration of carriage and age and highlighted the importance of both family size and composition for persistence in a household. Great dissimilarities were estimated among the specific serotypes in terms of transmissibility, duration of carriage and level of competition. Realistic age structured dynamic models were developed and used to investigate the impact of a range of vaccine strategies. The importance of serotype replacement, as a consequence of vaccination, was demonstrated. The economic acceptability of alternative interventions with the conjugate vaccine depended on the magnitude of its indirect effects. Herd immunity had a considerable impact on the overall cost-effectiveness of the programmes since it may substantially reduce the burden of disease in older age groups. However, serotype replacement may counterbalance this reduction and lead to a non cost-effective result.

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Esra, Rachel. "Mathematical modelling of the population impact of screening for Chlamydia Trachomatis and Neisseria gonorrhoeae in South Africa." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29629.

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A large proportion of chlamydial and gonococcal infections are asymptomatic. In lower- and middle-income countries like South Africa, where syndromic management is practiced, it is likely that a large proportion of curable STIs go untreated, as screening for asymptomatic STIs is rarely conducted. Due to the lack of empirical data on the efficacy of STI screening programs, dynamic mathematical modelling has been used to assess the impact of screening, but most previous modelling studies have focused on high-income settings. Here we utilize dynamic mathematical modelling to evaluate the potential impact of opportunistic STI screening programs on the incidence and prevalence of Chlamydia trachomatis and Neisseria gonorrhea in South Africa. We extended an existing agent-based model of heterosexual HIV and STI transmission in South Africa to investigate the impact of targeted screening strategies directed at high risk groups including youth, female sex workers, pregnant women and patients in HIV care. All four screening strategies resulted in reductions in general and key population STI transmission. Opportunistic STI screening of youth and ART patients were shown to be most effective and represent viable interventions for reducing STI transmission in the South African population. Additionally, we compared the modelled impact of a standardized screening program to results obtained from other published mathematical models of chlamydia screening. Differences between models could be attributed to differences in the modelled heterogeneity in sexual behaviour as well as differences in assumptions about immunity following chlamydia recovery.

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Kajunguri, Damian. "Modelling the impact of TB superinfection on the dynamics of HIV-TB coinfection." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/4070.

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Thesis (MSc (Mathematics))--University of Stellenbosch, 2009.
ENGLISH ABSTRACT: In this thesis, a mathematical model describing the interaction between HIV and TB in the presence of TB superinfection is presented. The model takes into account two strains of Mycobacterium tuberculosis (MTB), where one strain is drug-sensitive and the other is resistant to at least one of the first-line anti-tuberculosis drugs. The impact of TB superinfection on the incidence and prevalence of TB in HIV-negative and HIVTB coinfected individuals is evaluated. Various control measures such as condom use, antiretroviral therapy, isoniazid preventive therapy and increased TB detection are studied using this model. Numerical results show that TB superinfection increases the prevalence and incidence of TB and its impact is more in HIV-negative than HIV-TB coinfected individuals. The results also show that TB superinfection promotes strain coexistence and increases the associated HIV mortality. Increased condom use was found to have a high positive impact towards the control of the two epidemics. Antiretroviral therapy decreases the TB notification rate and its impact on HIV prevalence increases with the coverage and efficacy. Isoniazid preventive therapy has a clear effect on the TB prevalence. Finally, increased TB detection was found to have a less impact on the TB incidence in HIV-TB coinfected individuals
AFRIKAANSE OPSOMMING: In hierdie verhandeling word ´n wiskundige model vir die interaksie tussen MIV en TB, in ´n situasie met TB superinfeksie voorgelˆe. Die model neem twee variante van TB in ag. Een van die variante is sensitief vir MTB behandeling, terwyl die ander weerstandig is vir ten minste een van die eerste-linie TB behandenings. Die impak van TB superinfeksie op die insidensie and prevalensie van TB in MIV negatiewe en MIV-TB ko-ge˜ınfekteerde individu word ondersoek. Veskeie beheer maatreels soos kondoom gebruik, anti-retrovirale behandeling (vir MIV) en isonazid voorkomende behandeling en verhoodge TB deteksie (vir TB) word ondersoek. Numeriese resultate wys TB superinfeksie verhoog die prevalense en insidensie van TB en dat dit ´n groter bydrae maak by MIV negatief as by MIV-TB ko-geinfekteerde individu. Die resultate wys veder TB superinfeksie promofeer variant kohabitasie en verhoog MIV verwante mortalitieit. Verhoogde kondoom gebruik is gevind om ´n positiewe bydrae te maak tot die beheer van beide epidemies. Anti-retrovirale terapie verlaag die TB aanmeldings koers en die impak van ART verhoog saam met ´n verhoging in die dekking en effektiwiteit daarvan. Voorkomende behandeling het ´n beduidende impak op TB prevalensie. Ons vind dat TB deteksie ´n beperkte impak maak op TB insidensie by MIV-TB ko-geinfekteerde individu

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Gao, Zhanhai School of Mathematics UNSW. "Modelling Human Immunodeficiency Virus and Hepatitis C Virus Epidemics in Australia." Awarded by:University of New South Wales. School of Mathematics, 2001. http://handle.unsw.edu.au/1959.4/18187.

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This thesis is concerned with the mathematical modelling for human immunodeficiency virus (HIV) and hepatitis C virus (HCV) epidemics in Australia. There are two parts to this thesis. Part I is aimed at modelling the transmission of HIV and HCV via needle sharing among injecting drug users (IDUs). The dynamical model of an epidemic through needle sharing among IDUs is derived. This model reveals the correlation between needle sharing and the epidemic prevalence among IDUs. The simulations of HIV and HCV prevalence and incidence among IDUs in Australia are made with this model. The comparison of simulated results with literature estimates shows that the modelled results are consistent with the literature estimates. The effects of needle sharing and cleaning on HIV and HCV prevalence and incidence among IDUs in Australia are evaluated. Part II is devoted to modelling the spread of HIV in the general community in Australia. A mathematical model is formulated to assess the epidemiological consequences of injecting drug use and sexual transmission in Australia. The effects of highly active antiretroviral therapies (HAART) on the HIV epidemic are included. The modelled results are in broad agreement with the literature estimates and observed data. The long-term effects of HAART are also discussed.

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McBryde, Emma Sue. "Mathematical and statistical modelling of infectious diseases in hospitals." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16330/.

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Antibiotic resistant pathogens, such as methicillin-resistant Staphylococcus aureus (MRSA), and vancomycin-resistant enterococci (VRE), are an increasing burden on healthcare systems. Hospital acquired infections with these organisms leads to higher morbidity and mortality compared with the sensitive strains of the same species and both VRE and MRSA are on the rise worldwide including in Australian hospitals. Emerging community infectious diseases are also having an impact on hospitals. The Severe Acute Respiratory Syndrome virus (SARS Co-V) was noted for its propensity to spread throughout hospitals, and was contained largely through social distancing interventions including hospital isolation. A detailed understanding of the transmission of these and other emerging pathogens is crucial for their containment. The statistical inference and mathematical models used in this thesis aim to improve understanding of pathogen transmission by estimating the transmission rates of contagions and predicting the impact of interventions. Datasets used for these studies come from the Princess Alexandra Hospital in Brisbane, Australia and Shanxi province, mainland China. Epidemiological data on infection outbreaks are challenging to analyse due to the censored nature of infection transmission events. Most datasets record the time on symptom onset, but the transmission time is not observable. There are many ways of managing censored data, in this study we use Bayesian inference, with transmission times incorporated into the augmented dataset as latent variables. Hospital infection surveillance data is often much less detailed that data collected for epidemiological studies, often consisting of serial incidence or prevalence of patient colonisation with a resistant pathogen without individual patient event histories. Despite the lack of detailed data, transmission characteristics can be inferred from such a dataset using structured HiddenMarkovModels (HMMs). Each new transmission in an epidemic increases the infection pressure on those remaining susceptible, hence infection outbreak data are serially dependent. Statistical methods that assume independence of infection events are misleading and prone to over-estimating the impact of infection control interventions. Structured mathematical models that include transmission pressure are essential. Mathematical models can also give insights into the potential impact of interventions. The complex interaction of different infection control strategies, and their likely impact on transmission can be predicted using mathematical models. This dissertation uses modified or novel mathematical models that are specific to the pathogen and dataset being analysed. The first study estimates MRSA transmission in an Intensive Care Unit, using a structured four compartment model, Bayesian inference and a piecewise hazard methods. The model predicts the impact of interventions, such as changes to staff/patient ratios, ward size and decolonisation. A comparison of results of the stochastic and deterministic model is made and reason for differences given. The second study constructs a Hidden Markov Model to describe longitudinal data on weekly VRE prevalence. Transmission is assumed to be either from patient to patient cross-transmission or sporadic (independent of cross-transmission) and parameters for each mode of acquisition are estimated from the data. The third study develops a new model with a compartment representing an environmental reservoir. Parameters for the model are gathered from literature sources and the implications of the environmental reservoir are explored. The fourth study uses a modified Susceptible-Exposed-Infectious-Removed (SEIR) model to analyse data from a SARS outbreak in Shanxi province, China. Infectivity is determined before and after interventions as well as separately for hospitalised and community symptomatic SARS cases. Model diagnostics including sensitivity analysis, model comparison and bootstrapping are implemented.

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Books on the topic "Mathematical modelling - Epidemiology"

1

Daley, Daryl J. Epidemic modelling: An introduction. Cambridge: Cambridge University Press, 1999.

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Shaw, M. W. Mathematical modelling of aspects of black leaf streak (Mycosphaerella fijiensis) epidemiology. Final report. Jan 1990. Chatham Maritime: Natural Resources Institute, 1993.

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Dorrington, Rob. The demographic impact of HIV/AIDS in Botswana: Modelling the impact of HIV/AIDS in Botswana. Gaborone: NACA, 2006.

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Dorrington, Rob. The demographic impact of HIV/AIDS in Botswana: Modelling the impact of HIV/AIDS in Botswana. Gaborone: NACA, 2006.

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1947-, Arino Ovide, Axelrod David E. 1940-, Kimmel Marek 1959-, and Capasso V. 1945-, eds. Advances in mathematical population dynamics--molecules, cells, and man: Proceedings of the 4th International Conference on Mathematical Population Dynamics, 23-27 May 1995. Singapore: World Scientific, 1997.

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Jürgen, Kranz, ed. Epidemics of plant diseases: Mathematical analysis and modelling. 2nd ed. Berlin: Springer-Verlag, 1990.

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An Introduction To Infectious Disease Modelling. Oxford University Press, USA, 2010.

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C, Jager J., and Ruitenberg E. J, eds. Statistical analysis and mathematical modelling of AIDS. Oxford: Oxford University Press, 1988.

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C, Jager J., Ruitenberg E. Joost, and Commission of the European Communities. Working Party on AIDS., eds. Statistical analysis and mathematical modelling of AIDS. Oxford: Oxford University Press, 1988.

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D, McLean George, Garrett Ronald G, and Ruesink William G, eds. Plant virus epidemics: Monitoring, modelling, and predicting outbreaks. Sydney: Academic Press, 1986.

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Book chapters on the topic "Mathematical modelling - Epidemiology"

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Earn, David J. D. "A Light Introduction to Modelling Recurrent Epidemics." In Mathematical Epidemiology, 3–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78911-6_1.

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Yan, Ping. "Distribution Theory, Stochastic Processes and Infectious Disease Modelling." In Mathematical Epidemiology, 229–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78911-6_10.

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Müller, Johannes, and Christina Kuttler. "Epidemiology." In Lecture Notes on Mathematical Modelling in the Life Sciences, 415–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27251-6_4.

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White, Peter J., and Geoff P. Garnett. "Mathematical Modelling of the Epidemiology of Tuberculosis." In Advances in Experimental Medicine and Biology, 127–40. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6064-1_9.

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Friedman, Avner, and Chiu-Yen Kao. "Epidemiology of Infectious Diseases." In Lecture Notes on Mathematical Modelling in the Life Sciences, 33–47. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08314-8_4.

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Davies, Toby P., Hannah M. Fry, Alan G. Wilson, and Steven R. Bishop. "The London Riots &;#x02013; 1: Epidemiology, Spatial Interaction and Probability of Arrest." In Approaches to Geo&;#x02010;mathematical Modelling, 153–69. Chichester, UK: John Wiley &;#38; Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118937426.ch9.

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Dodd, P. J., C. Pretorius, and B. G. Williams. "Modelling the HIV-Associated TB Epidemic and the Impact of Interventions Aimed at Epidemic Control." In HIV and Tuberculosis, 25–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29108-2_3.

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Abstract In this chapter, we focus on mathematical models of tuberculosis epidemiology (TB) that include interactions with HIV and an explicit representation of transmission. We review the natural history of TB and illustrate how its features are simplified and incorporated in mathematical models. We then review the ways HIV influences the natural history of TB, the interventions that have been considered in models, and the way these individual-level effects are represented in models. We then go on to consider population-level effects, reviewing the TB/HIV modelling literature. We first review studies whose focus was on purely epidemiological modelling, and then studies whose focus was on modelling the impact of interventions. We conclude with a summary of the uses and achievements of TB/HIV modelling and some suggested future directions.

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Lee, Lloyd W. F., and Mohd Hafiz Mohd. "Agent Based Modelling Using GAMA 1.8 with Applications to Biological System in Epidemiology." In Springer Proceedings in Mathematics & Statistics, 109–29. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2629-6_6.

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"Mathematical modelling." In Veterinary Epidemiology, 520–39. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781118280249.ch23.

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"MATHEMATICAL MODELLING IN EPIDEMIOLOGY." In An Introduction to Mathematical Physiology and Biology, 109–17. Cambridge University Press, 1999. http://dx.doi.org/10.1017/cbo9781139173278.008.

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Conference papers on the topic "Mathematical modelling - Epidemiology"

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Ghosh, Uttam, Sourav Chowdhury, and Dilip Kumar Khan. "Mathematical Modelling of Epidemiology in Presence of Vaccination and Delay." In National Conference on Advancement of Computing in Engineering Research. Academy & Industry Research Collaboration Center (AIRCC), 2013. http://dx.doi.org/10.5121/csit.2013.3209.

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