Demography and Diffusion in Epidemics: Malaria and Black Death Spread

Gaudart, Jean; Ghassani, Mohamad; Mintsa, Julie; Rachdi, Mustapha; Waku, Jules; Demongeot, Jacques

doi:10.1007/s10441-010-9103-z

Cited by 31 publications

(29 citation statements)

References 55 publications

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“…In another study the authors have augmented the model by age classes and with a diffusion term to account for spatial effects in order to approximate the epidemic front wave dynamics of the Black Death between 1348 and 1350. 106 In Demongeot et al (2012) the Ross and McKendrik SIR model has been revised to incorporate demographic and spatial dynamics introducing continuous age classes and diffusion of both human and vectors species subpopulations within the infected zones. 107 The model has been used to simulated the spread of malaria in Bancoumana, Mali.…”

Section: Mathematical/mechanistic State-space Modelsmentioning

confidence: 99%

Mathematical modeling of infectious disease dynamics

Siettos

Russo²

2013

Virulence

357

282

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Over the last years, an intensive worldwide effort is speeding up the developments in the establishment of a global surveillance network for combating pandemics of emergent and re-emergent infectious diseases. Scientists from different fields extending from medicine and molecular biology to computer science and applied mathematics have teamed up for rapid assessment of potentially urgent situations. Toward this aim mathematical modeling plays an important role in efforts that focus on predicting, assessing, and controlling potential outbreaks. To better understand and model the contagious dynamics the impact of numerous variables ranging from the micro host–pathogen level to host-to-host interactions, as well as prevailing ecological, social, economic, and demographic factors across the globe have to be analyzed and thoroughly studied. Here, we present and discuss the main approaches that are used for the surveillance and modeling of infectious disease dynamics. We present the basic concepts underpinning their implementation and practice and for each category we give an annotated list of representative works.

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Section: Mathematical/mechanistic State-space Modelsmentioning

confidence: 99%

Mathematical modeling of infectious disease dynamics

Siettos

Russo²

2013

Virulence

357

282

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“…We can introduce a last formalism for being able to define n-uples interactions and simulate as in a Markov or spatial renewal context [88][89][90][91][92][93][94][95].…”

Section: Non-linear Interactionsmentioning

confidence: 99%

Random Modelling of Contagious (Social and Infectious) Diseases: Examples of Obesity and HIV and Perspectives Using Social Networks

Demongeot

Hansen

Jannot

et al. 2012

2012 26th International Conference on Advanced Information Networking and Applications Workshops

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Modelling contagious diseases needs to incorporate inthe models information about social networks through which the disease spreads out as well as data about demographic and genetic changes in the susceptible population, and also to include mechanistic knowledge about contacts between hosts and pathogens. We will introduce all these elements in two examples of contagious diseases, the obesity, a social pathology partly caused by behaviour mimicking some dominant habits of nutrition and HIV transmitted through social networks. Obesity spread modelling will use the notion of homophilic graphs and we will show that a micro-simulation of IBM type (Individual Based Modelling) can reproduce the current stable incidence of the HIV epidemic in a population of HIV-positive MSM (Men having Sex with Men).

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“…by considering morphogen, cell, pathogen, host and vector populations whose global size changes during morphogenetic, epidemic and endemic histories, as well as spatial aspects about their diffusion, spread or genetic changes [Gaudart et al, 2007[Gaudart et al, , 2009[Gaudart et al, , 2010a[Gaudart et al, , 2010bGlade et al, 2007;Abbas et al, 2009;Horie et al, 2010;Demongeot et al, to appear].…”

Section: Introductionmentioning

confidence: 99%

Zero-Diffusion Domains in Reaction–diffusion Morphogenetic and Epidemiologic Processes

Demongeot

Gaudart

Lontos

et al. 2012

Int. J. Bifurcation Chaos

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Classical models of morphogenesis by Murray and Meinhardt and of epidemics by Ross andMcKendrick can be revisited in order to consider the colocalizations favoring interaction between morphogens and cells or between pathogens and hosts. The classical epidemic models suppose, for example, that the populations in interaction have a constant size and are spatially fixed during the epidemic waves, but the presently observed pandemics show that the long duration of their spread during months or years imposes to take into account the pathogens, hosts and vectors migration in epidemics, as well as the morphogens and cells diffusion in morphogenesis. That leads naturally to study the occurrence of complex spatio-temporal behaviors in dynamics of population sizes and also to consider preferential zones of interaction, i.e. the zero-diffusion sets, for respectively building anatomic frontiers and confining contagion domains. Three examples of application will be presented, the first proposing a model of Black Death spread in Europe (1348-1350), and the last ones related to two morphogenetic processes, feather morphogenesis in chicken and gastrulation in Drosophila.

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Demography and Diffusion in Epidemics: Malaria and Black Death Spread

Cited by 31 publications

References 55 publications

Mathematical modeling of infectious disease dynamics

Mathematical modeling of infectious disease dynamics

Random Modelling of Contagious (Social and Infectious) Diseases: Examples of Obesity and HIV and Perspectives Using Social Networks

Zero-Diffusion Domains in Reaction–diffusion Morphogenetic and Epidemiologic Processes

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