Demography in epidemics modelling

Demongeot, Jacques; Gaudart, Jean; Mintsa, Julie; Rachdi, Mustapha

doi:10.3934/cpaa.2012.11.61

Cited by 14 publications

(8 citation statements)

References 34 publications

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“…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

View full text Add to dashboard Cite

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“…These seasonal changes could occur in exactly the same way as for other pathogens, like the common cold or influenza [9][10][11][12]. This phenomenon can be modelled and the deterministic as well as stochastic models [2,[13][14][15][16][17][18][19][20][21][22][23][24][25] include potentially temperature-dependent parameters, like the contagion coefficient increasing with cold, dry weather because of faster evaporation of aerosol droplets. The present paper aims to identify such parameters from the covid-19 spread dynamics.…”

Section: Introductionmentioning

confidence: 99%

Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics

Demongeot

Flet-Berliac

Seligmann

2020

Biology

Self Cite

137

152

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1) Background: The virulence of coronavirus diseases due to viruses like SARS-CoV or MERS-CoV decreases in humid and hot weather. The putative temperature dependence of infectivity by the new coronavirus SARS-CoV-2 or covid-19 has a high predictive medical interest. (2) Methods: External temperature and new covid-19 cases in 21 countries and in the French administrative regions were collected from public data. Associations between epidemiological parameters of the new case dynamics and temperature were examined using an ARIMA model. (3) Results: We show that, in the first stages of the epidemic, the velocity of contagion decreases with country-or region-wise temperature. (4) Conclusions: Results indicate that high temperatures diminish initial contagion rates, but seasonal temperature effects at later stages of the epidemy remain questionable. Confinement policies and other eviction rules should account for climatological heterogeneities, in order to adapt the public health decisions to possible geographic or seasonal gradients.

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“…The natural death rate was parameterized with µ , and the number of births per unit time was parametrized with Λ. Combining [7] and [31] helped us check whether the number of unreported cases influenced the severity of the pandemic, and to what extent public health measures such as quarantine contribute to containing the pandemic. The modified model can be described as follows:…”

Section: Parameters Of the Modelmentioning

confidence: 99%

Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

Sallahi

Park

El-Mellouhi

et al. 2021

Biology

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Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.

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Demography in epidemics modelling

Cited by 14 publications

References 34 publications

Mathematical modeling of infectious disease dynamics

Mathematical modeling of infectious disease dynamics

Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics

Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

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