Relevant bibliographies by topics / Epidemiology – Mathematical models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Epidemiology – Mathematical models'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Epidemiology – Mathematical models"

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Lopatin, A. A., E. V. Kuklev, V. A. Safronov, A. S. Razdorsky, L. V. Samoilova, and V. P. Toporkov. "Verification of Mathematical Models of Plague." Problems of Particularly Dangerous Infections, no. 3(113) (June 20, 2012): 26–28. http://dx.doi.org/10.21055/0370-1069-2012-3-26-28.

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Mathematic modeling and prognostication of infectious diseases epidemic process is a promising trend of epidemiologic investigations. The complex of mathematic models (SEIRF type) of plague epidemic process was developed for this purpose by the Russian Research Anti-Plague Institute “Microbe” and laboratory of epidemiologic cybernetics of N.F.Gamaleya Institute for Epidemiology and Microbiology. The data on the plague outbreak in 1945 in the rural settlement Avan’ (Aral region of Kzyl-Orda district of Kazakh SSR) were used to test working efficiency of this complex. The data analysis permitted to obtain the starting and boundary conditions for epidemic process modeling. In the process of modeling the mathematical models of epidemic process of plague with various ways of infection transmission for each epidemic focus in regard with historical data were used. The data were processed by the analytical platform Deductor 5.1. Identified was strong positive correlation between estimated and historical data – r = +0,71. The results received testify that mathematic models are effective and give high degree of confidence. They can be used to receive quantitative characteristics of prognosis for plague epidemic process development with different transmission routes considering that anti-epidemic measures have been taken.
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Henson, Shandelle M., Fred Brauer, and Carlos Castillo-Chavez. "Mathematical Models in Population Biology and Epidemiology." American Mathematical Monthly 110, no. 3 (March 2003): 254. http://dx.doi.org/10.2307/3647954.

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Rosner, Bernard A., Graham A. Colditz, Penny M. Webb, and Susan E. Hankinson. "Mathematical Models of Ovarian Cancer Incidence." Epidemiology 16, no. 4 (July 2005): 508–15. http://dx.doi.org/10.1097/01.ede.0000164557.81694.63.

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BLOWER, SALLY, and GRAHAM MEDLEY. "Epidemiology, HIV and drugs: mathematical models and data." Addiction 87, no. 3 (March 1992): 371–79. http://dx.doi.org/10.1111/j.1360-0443.1992.tb01938.x.

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King, P. "Mathematical models in population biology and epidemiology [Book Reviews]." IEEE Engineering in Medicine and Biology Magazine 20, no. 4 (July 2001): 101. http://dx.doi.org/10.1109/memb.2001.940057.

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Naz, R., I. Naeem, and F. M. Mahomed. "A Partial Lagrangian Approach to Mathematical Models of Epidemiology." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/602915.

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This paper analyzes the first integrals and exact solutions of mathematical models of epidemiology via the partial Lagrangian approach by replacing the three first-order nonlinear ordinary differential equations by an equivalent system containing one second-order equation and a first-order equation. The partial Lagrangian approach is then utilized for the second-order ODE to construct the first integrals of the underlying system. We investigate the SIR and HIV models. We obtain two first integrals for the SIR model with and without demographic growth. For the HIV model without demography, five first integrals are established and two first integrals are deduced for the HIV model with demography. Then we utilize the derived first integrals to construct exact solutions to the models under investigation. The dynamic properties of these models are studied too. Numerical solutions are derived for SIR models by finite difference method and are compared with exact solutions.
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Rosenberg, Noah A. "Population models, mathematical epidemiology, and the COVID-19 pandemic." Theoretical Population Biology 137 (February 2021): 1. http://dx.doi.org/10.1016/j.tpb.2021.01.001.

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Chu, Kenneth C. "A nonmathematical view of mathematical models for cancer." Journal of Chronic Diseases 40 (January 1987): 163S—170S. http://dx.doi.org/10.1016/s0021-9681(87)80019-x.

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&NA;. "Biologically Based Mathematical Models of Lung Cancer Risk." Epidemiology 4, no. 3 (May 1993): 193–94. http://dx.doi.org/10.1097/00001648-199305000-00002.

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Tom, Eric, and Kevin A. Schulman. "Mathematical Models in Decision Analysis." Infection Control and Hospital Epidemiology 18, no. 1 (January 1997): 65–73. http://dx.doi.org/10.2307/30141966.

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Dissertations / Theses on the topic "Epidemiology – Mathematical models"

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Bate, Andrew M. "Mathematical models in eco-epidemiology." Thesis, University of Bath, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.616875.

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Diseases have the capacity to not only influence the dynamics of their hosts, but also interacting species like predators, prey and competitors. Likewise, interacting species can influence disease dynamics by altering the host's dynamics. The combination of these two effects is often called eco-epidemiology, the interaction of ecology and epidemiology. In this thesis, we explore this interplay of infectious diseases and predator--prey interactions, where the predator is a specialist. We start with an introductory chapter on modelling eco-epidemiology, with a particular focus on the myriad of different possible assumptions mathematical models in eco-epidemiology can have. In Chapter 2, we consider the effect predator--prey oscillations have on the endemic criteria for an infectious disease. In Chapter 3, we find a great variety of complex dynamics like tristability between endemic and disease-free states, quasi-periodic dynamics and chaos in a predator--prey model with an infectious disease in the predator. In Chapter 4, we consider the impact an infectious disease has on a group defending prey. Here, we find that the disease not only can coexist with a predator, it can actually help the predator survive where it could not in the absence of the disease, in stark contradiction to the principle of competitive exclusion which states that two exploiters should not coexist on a single resource. Lastly, in Chapter 5, we consider a spatial predator--prey model with a disease in the prey and focus on how preytaxis (the movement of predators along prey gradients) can alter various invasion scenarios. Through all these chapters, there is a common focus on the impact (endogenous) oscillations have in eco-epidemiology.
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Booton, Ross D. "Mathematical models of stress and epidemiology." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/22549/.

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De, la Harpe Alana. "A comparative analysis of mathematical models for HIV epidemiology." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/96983.

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Thesis (MSc)--Stellenbosch University, 2015.
ENGLISH ABSTRACT: HIV infection is one of the world’s biggest health problems, with millions of people infected worldwide. HIV infects cells in the immune system, where it primarily targets CD4+ T helper cells and without treatment, the disease leads to the collapse of the host immune system and ultimately death. Mathematical models have been used extensively to study the epidemiology of HIV/AIDS. They have proven to be effective tools in studying the transmission dynamics of HIV. These models provide predictions that can help better our understanding of the epidemiological patterns of HIV, especially the mechanism associated with the spread of the disease. In this thesis we made a functional comparison between existing epidemiological models for HIV, with the focus of the comparison on the force of infection (FOI). The spread of infection is a crucial part of any infectious disease, as the dynamics of the disease depends greatly on the rate of transmission from an infectious individual to a susceptible individual. First, a review was done to see what deterministic epidemiological models exist. We found that many manuscripts do not provide the necessary information to recreate the authors’ results and only a small amount of the models could be simulated. The reason for this is mainly due to a lack of information or due to mistakes in the article. The models were divided into four categories for the analysis. On the basis of the FOI, we distinguished between frequency- or density-dependent transmission, and as a second criterion we distinguished models on the sexual activity of the AIDS group. Subsequently, the models were compared in terms of their FOI, within and between these classes. We showed that for larger populations, frequency-dependent transmission should be used. This is the case for HIV, where the disease is mainly spread through sexual contact. Inclusion of AIDS patients in the group of infectious individuals is important for the accuracy of transmission dynamics. More than half of the studies that were selected in the review assumed that AIDS patients are too sick to engage in risky sexual behaviour. We see that including AIDS patients in the infectious individuals class has a significant effect on the FOI when the value for the probability of transmission for an individual with AIDS is bigger than that of the other classes. The analysis shows that the FOI can vary depending on the parameter values and the assumptions made. Many models compress various parameter values into one, most often the transmission probability. Not showing the parameter values separately makes it difficult to understand how the FOI works, since there are unknown factors that have an influence. Improving the accuracy of the FOI can help us to better understand what factors influence it, and also produce more realistic results. Writing the probability of transmission as a function of the viral load can help to make the FOI more accurate and also help in the understanding of the effects that viral dynamics have on the population transmission dynamics.
AFRIKAANSE OPSOMMING: MIV-infeksie is een van die wêreld se grootste gesondheidsprobleme, met miljoene mense wat wêreldwyd geïnfekteer is. MIV infekteer selle in die immuunstelsel, waar dit hoofsaaklik CD4+ T-helperselle teiken. Sonder behandeling lei die siekte tot die ineenstorting van die gasheer se immuunstelsel en uiteindelik sy dood. Wiskundige modelle word breedvoerig gebruik om die epidemiologie van MIV/vigs te bestudeer. Die modelle is doeltreffende instrumente in die studie van die oordrag-dinamika van MIV. Hulle lewer voorspellings wat kan help om ons begrip van epidemiologiese patrone van MIV, veral die meganisme wat verband hou met die verspreiding van die siekte, te verbeter. In hierdie tesis het ons ‘n funksionele vergelyking tussen bestaande epidemiologiese modelle vir MIV gedoen, met die fokus van die vergelyking op die tempo van infeksie (TVI). Die verspreiding van infeksie is ‘n belangrike deel van enige aansteeklike siekte, aangesien die dinamika van die siekte grootliks afhang van die tempo van oordrag van ‘n aansteeklike persoon na ‘n vatbare persoon. ‘n Oorsig is gedoen om te sien watter kompartementele epidemiologiese modelle alreeds bestaan. Ons het gevind dat baie van die manuskripte nie die nodige inligting voorsien wat nodig is om die resultate van die skrywers te repliseer nie, en slegs ‘n klein hoeveelheid van die modelle kon gesimuleer word. Die rede hiervoor is hoofsaaklik as gevolg van ‘n gebrek aan inligting of van foute in die artikel. Die modelle is in vier kategorieë vir die analise verdeel. Op grond van die TVI het ons tussen frekwensie- of digtheidsafhanklike oordrag onderskei, en as ‘n tweede kriterium het ons die modelle op die seksuele aktiwiteit van die vigs-groep onderskei. Daarna is die modelle binne en tussen die klasse vergelyk in terme van hul TVIs. Daar is gewys dat frekwensie-afhanklike oordrag gebruik moet word vir groter bevolkings. Dit is die geval van MIV, waar die siekte hoofsaaklik versprei word deur seksuele kontak. Die insluiting van die vigs-pasiënte in die groep van aansteeklike individue is belangrik vir die akkuraatheid van die oordrag-dinamika van MIV. Meer as helfte van die uitgesoekte studies aanvaar dat vigs-pasiënte te siek is om betrokke te raak by riskante seksuele gedrag. Ons sien dat die insluiting van vigs-pasiënte in die groep van aansteeklike individue ‘n beduidende uitwerking op die TVI het wanneer die waarde van die waarskynlikheid van oordrag van ‘n individu met vigs groter is as dié van die ander klasse. Die analise toon dat die TVI kan wissel afhangende van die parameter waardes en die aannames wat gemaak is. Baie modelle voeg verskeie parameter waardes bymekaar vir die waarskynlikheid van oordrag. Wanneer die parameter waardes nie apart gewys word nie, is dit moeilik om die werking van die TVI te verstaan, want daar is onbekende faktore wat ‘n invloed op die TVI het. Die verbetering van die akkuraatheid van die TVI kan ons help om die faktore wat dit beïnvloed beter te verstaan, en dit kan ook help om meer realistiese resultate te produseer. Om die waarskynlikheid van oordrag as ‘n funksie van die viruslading te skryf kan help om die TVI meer akkuraat te maak en dit kan ook help om die effek wat virale dinamika op die bevolkingsoordrag-dinamika het, beter te verstaan.
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Otieno, Andrew Alex Omondi. "Application of lie group analysis to mathematical models in epidemiology." Thesis, Walter Sisulu University, 2013. http://hdl.handle.net/11260/100.

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Lie group analysis is arguably the most systematic vehicle for finding exact solutions of differential equations. Using this approach one has at one's disposal a variety of algorithms that make the solution process of many differential equations algorithmic. Vital properties of a given differential equation can often be inferred from the symmetries admitted by the equation. However, Lie group analysis has not enjoyed wide-spread application to systems of first-order ordinary differential equations. This is because such systems typically admit an infinite number of Lie point symmetries, and there is no systematic way to find even a single nontrivial one-dimensional Lie symmetry algebra. In the few applications available, the approach has been to circumvent the problem by transforming a given system of first-order ordinary differential equations into one in which at least one of the equations is of order two or greater. It is therefore fair to say that the full power of Lie group analysis has not been sufficiently harnessed in the solution of systems of first-order ordinary differential equations. In this dissertation we review some applications of Lie group analysis to systems of first order ordinary differential equations. We shed light on the integration procedure for first-order systems of ordinary differential equations admitting a solvable Lie algebra. We do this via instructive examples drawn from mathematical epidemiology models. In particular we revisit the work of Nucci and Torrisi [54] and improve the exposition of the Lie-symmetry-inspired solution of a mathematical model which describes a HIV transmission. To aid implementation of the integration strategy for systems of ordinary differential equations, we have developed ad-hoc routines for finding particular types of admitted symmetries and checking if a given symmetry is indeed admitted by a system of ordinary differential equations.
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Lutambi, Angelina Mageni. "Basic properties of models for the spread of HIV/AIDS." Thesis, Stellenbosch : Stellenbosch University, 2007. http://hdl.handle.net/10019.1/19641.

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Thesis (MSc)--University of Stellenbosch, 2007.
ENGLISH ABSTRACT: While research and population surveys in HIV/AIDS are well established in developed countries, Sub-Saharan Africa is still experiencing scarce HIV/AIDS information. Hence it depends on results obtained from models. Due to this dependence, it is important to understand the strengths and limitations of these models very well. In this study, a simple mathematical model is formulated and then extended to incorporate various features such as stages of HIV development, time delay in AIDS death occurrence, and risk groups. The analysis is neither purely mathematical nor does it concentrate on data but it is rather an exploratory approach, in which both mathematical methods and numerical simulations are used. It was found that the presence of stages leads to higher prevalence levels in a short term with an implication that the primary stage is the driver of the disease. Furthermore, it was found that time delay changed the mortality curves considerably, but it had less effect on the proportion of infectives. It was also shown that the characteristic behaviour of curves valid for most epidemics, namely that there is an initial increase, then a peak, and then a decrease occurs as a function of time, is possible in HIV only if low risk groups are present. It is concluded that reasonable or quality predictions from mathematical models are expected to require the inclusion of stages, risk groups, time delay, and other related properties with reasonable parameter values.
AFRIKAANSE OPSOMMING: Terwyl navorsing en bevolkingsopnames oor MIV/VIGS in ontwikkelde lande goed gevestig is, is daar in Afrika suid van die Sahara slegs beperkte inligting oor MIV/VIGS beskikbaar. Derhalwe moet daar van modelle gebruik gemaak word. Dit is weens hierdie feit noodsaaklik om die moontlikhede en beperkings van modelle goed te verstaan. In hierdie werk word ´n eenvoudige model voorgelˆe en dit word dan uitgebrei deur insluiting van aspekte soos stadiums van MIV outwikkeling, tydvertraging by VIGS-sterftes en risikogroepe in bevolkings. Die analise is beklemtoon nie die wiskundage vorme nie en ook nie die data nie. Dit is eerder ´n verkennende studie waarin beide wiskundige metodes en numeriese simula˙sie behandel word. Daar is bevind dat insluiting van stadiums op korttermyn tot ho¨er voorkoms vlakke aanleiding gee. Die gevolgtrekking is dat die primˆere stadium die siekte dryf. Verder is gevind dat die insluiting van tydvestraging wel die kurwe van sterfbegevalle sterk be¨ınvloed, maar dit het min invloed op die verhouding van aangestekte persone. Daar word getoon dat die kenmerkende gedrag van die meeste epidemi¨e, naamlik `n aanvanklike styging, `n piek en dan `n afname, in die geval van VIGS slegs voorkom as die bevolking dele bevat met lae risiko. Die algehele gevolgtrekking word gemaak dat vir goeie vooruitskattings met sinvolle parameters, op grond van wiskundige modelle, die insluiting van stadiums, risikogroepe en vertragings benodig word.
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Lloyd, Alun Lewis. "Mathematical models for spatial heterogeneity in population dynamics and epidemiology." Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337603.

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McLean, A. R. "Mathematical models of the epidemiology of measles in developing countries." Thesis, Imperial College London, 1987. http://hdl.handle.net/10044/1/47259.

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Tosun, Kursad. "QUALITATIVE AND QUANTITATIVE ANALYSIS OF STOCHASTIC MODELS IN MATHEMATICAL EPIDEMIOLOGY." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/dissertations/732.

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We introduce random fluctuations on contact and recovery rates in three basic deterministic models in mathematical epidemiology and obtain stochastic counterparts. This paper addresses qualitative and quantitative analysis of stochastic SIS model with disease deaths and demographic effects, and stochastic SIR models with/without disease deaths and demographic effects. We prove the global existence of a unique strong solution and discuss stochastic asymptotic stability of disease free and endemic equilibria. We also investigate numerical properties of these models and prove the convergence of the Balanced Implicit Method approximation to the analytic solution. We simulate the models with fairly realistic parameters to visualize our conclusions.
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Thompson, Brett Morinaga. "Development, Implementation, and Analysis of a Contact Model for an Infectious Disease." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc9824/.

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With a growing concern of an infectious diseases spreading in a population, epidemiology is becoming more important for the future of public health. In the past epidemiologist used existing data of an outbreak to help them determine how an infectious disease might spread in the future. Now with computational models, they able to analysis data produced by these models to help with prevention and intervention plans. This paper looks at the design, implementation, and analysis of a computational model based on the interactions of the population between individuals. The design of the working contact model looks closely at the SEIR model used as the foundation and the two timelines of a disease. The implementation of the contact model is reviewed while looking closely at data structures. The analysis of the experiments provide evidence this contact model can be used to help epidemiologist study the spread of an infectious disease based on the contact rate of individuals.
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Griette, Quentin. "Mathematical and numerical analysis of propagation models arising in evolutionary epidemiology." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS051/document.

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Cette thèse porte sur différents modèles de propagation en épidémiologie évolutive. L'objectif est d'en faire une analyse mathématique rigoureuse puis d'en tirer des enseignements biologiques. Dans un premier temps nous envisageons le cas d'une population d'hôtes répartis de manière homogène dans un espace linéaire, dans laquelle se propage un pathogène pouvant muter entre deux phénotypes plus ou moins virulents. Ce phénomène de mutation est à l'origine d'une interaction entre les dynamiques évolutive et épidémiologique du pathogène. Nous étudions la vitesse de propagation de l'épidémie et l'existence de fronts progressifs, ainsi que l'influence sur la vitesse de différents facteurs biologiques, comme des effets stochastiques liés à la taille de la population d'hôtes (explorations numériques). Dans un deuxième temps nous envisageons une hétérogénéité spatiale périodique dans la population d'hôtes, et l'existence de fronts pulsatoires pour le système de réaction-diffusion (non-coopératif) associé. Enfin nous considérons un pathogène pouvant muter vers un grand nombre de phénotypes différents et étudions l'existence de fronts potentiellement singuliers, modélisant ainsi une concentration sur un trait optimal
In this thesis we consider several models of propagation arising in evolutionary epidemiology. We aim at performing a rigorous mathematical analysis leading to new biological insights. At first we investigate the spread of an epidemic in a population of homogeneously distributed hosts on a straight line. An underlying mutation process can shift the virulence of the pathogen between two values, causing an interaction between epidemiology and evolution. We study the propagation speed of the epidemic and the influence of some biologically relevant quantities, like the effects of stochasticity caused by the hosts' finite population size (numerical explorations), on this speed. In a second part we take into account a periodic heterogeneity in the hosts' population and study the propagation speed and the existence of pulsating fronts for the associated (non-cooperative) reaction-diffusion system. Finally, we consider a model in which the pathogen is allowed to shift between a large number of different phenotypes, and construct possibly singular traveling waves for the associated nonlocal equation, thus modelling concentration on an optimal trait
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Books on the topic "Epidemiology – Mathematical models"

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Brauer, Fred, Carlos Castillo-Chavez, and Zhilan Feng. Mathematical Models in Epidemiology. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9828-9.

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Brauer, Fred. Mathematical models in population biology and epidemiology. 2nd ed. New York: Springer, 2012.

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Brauer, Fred, and Carlos Castillo-Chavez. Mathematical Models in Population Biology and Epidemiology. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1686-9.

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Brauer, Fred, and Carlos Castillo-Chávez. Mathematical Models in Population Biology and Epidemiology. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3516-1.

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Capasso, Vincenzo. Mathematical structures of epidemic systems. 2nd ed. Berlin: Springer, 2008.

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Mathematical structures of epidemic systems. Berlin: Springer, 1993.

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Center for Emerging Issues (U.S.). Overview of predictive infectious-disease modeling. Washington, D.C.]: United States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, Center for Emerging Issues, 2005.

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Courant Institute of Mathematical Sciences, ed. Mathematical methods for analysis of a complex disease. New York: Courant Institute of Mathematical Sciences, 2011.

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Malchow, Horst. Spatiotemporal patterns in ecology and epidemiology: Theory, models, and simulation. Boca Raton: Chapman & Hall/CRC Press, 2008.

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Hybrid models of tropical infections. Berlin: Springer-Verlag, 1985.

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Book chapters on the topic "Epidemiology – Mathematical models"

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van den Driessche, P. "Deterministic Compartmental Models: Extensions of Basic Models." In Mathematical Epidemiology, 147–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78911-6_5.

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Brauer, Fred. "Compartmental Models in Epidemiology." In Mathematical Epidemiology, 19–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78911-6_2.

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van den Driessche, P. "Spatial Structure: Patch Models." In Mathematical Epidemiology, 179–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78911-6_7.

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Allen, Linda J. S. "An Introduction to Stochastic Epidemic Models." In Mathematical Epidemiology, 81–130. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78911-6_3.

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Wu, Jianhong. "Spatial Structure: Partial Differential Equations Models." In Mathematical Epidemiology, 191–203. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78911-6_8.

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Wonham, M. J., and M. A. Lewis. "A Comparative Analysis of Models for West Nile Virus." In Mathematical Epidemiology, 365–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78911-6_14.

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Li, Jia, and Fred Brauer. "Continuous-Time Age-Structured Models in Population Dynamics and Epidemiology." In Mathematical Epidemiology, 205–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78911-6_9.

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Bauch, Chris T. "The Role of Mathematical Models in Explaining Recurrent Outbreaks of Infectious Childhood Diseases." In Mathematical Epidemiology, 297–319. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78911-6_11.

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Nuño, M., C. Castillo-Chavez, Z. Feng, and M. Martcheva. "Mathematical Models of Influenza: The Role of Cross-Immunity, Quarantine and Age-Structure." In Mathematical Epidemiology, 349–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78911-6_13.

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Kretzschmar, Mirjam, and Jacco Wallinga. "Mathematical Models in Infectious Disease Epidemiology." In Modern Infectious Disease Epidemiology, 209–21. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-93835-6_12.

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Conference papers on the topic "Epidemiology – Mathematical models"

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Báez-Sánchez, Andrés David. "A Mathematical Model for Behavioral Epidemiology: A Numerical Approach." In CNMAC 2017 - XXXVII Congresso Nacional de Matemática Aplicada e Computacional. SBMAC, 2018. http://dx.doi.org/10.5540/03.2018.006.01.0299.

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