Random Modelling of Contagious Diseases

Demongeot, Jacques; Hansen, Ole Paaske; Hessami, Hessam; Jannot, Anne‐Sophie; Mintsa, Julie; Rachdi, Mustapha; Taramasco, Carla

doi:10.1007/s10441-013-9176-6

Cited by 16 publications

(11 citation statements)

References 98 publications

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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

134

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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Section: Introductionmentioning

confidence: 99%

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

Demongeot

Flet-Berliac

Seligmann

2020

Biology

134

152

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“…Influence of the contact duration Let us now introduce contact duration t and a contagion coefficient b possibly depending on t. 30 It is possible to retrieve the quadratic term of interaction already present in all the classical models of contagion [30][31][32][33][34][35][36][37][38][39][40] by using a stochastic approach coming from the random chemistry of contacts [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57] for interpreting the proposed rules, more developed in. 58 We have the demographic dynamics voiding the overweight transition:…”

Section: The Dynamics Of Contactsmentioning

confidence: 99%

Discrete dynamics of contagious social diseases: Example of obesity

2015

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Modeling contagious diseases needs to incorporate information about social networks through which the disease spreads as well as data about demographic and genetic changes in the susceptible population. In this paper, we propose a theoretical framework (conceptualization and formalization) which seeks to model obesity as a process of transformation of one's own body determined by individual (physical and psychological), inter-individual (relational, i.e., relative to the relationship between the individual and others) and socio-cultural (environmental, i.e., relative to the relationship between the individual and his milieu) factors. Individual and inter-individual factors are tied to each other in a socio-cultural context whose impact is notably related to the visibility of anybody being exposed on the public stage in a non-contingent way. The question we are dealing with in this article is whether such kind of social diseases, i.e., depending upon socio-environmental exposure, can be considered as "contagious". In other words, can obesity be propagated from individual to individual or from environmental sources throughout an entire population?

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“…Professionals from different areas can be connected with different access permissions depending on their profession. The RVS project began in June 2010: 44 homes have been outfitted with the RVS system, and 132 healthcare professionals including social professionals (80), paramedics (44) and GP's (8) have been trained to use the system. The RVS project was completed in July 2013.…”

Section: Medico-social Information Processing For the Surveillance Ofmentioning

confidence: 99%

“…Several researchers studied the influence of friends and peers on the body mass index of an individual by using systems science tools that have been proposed to determine the impact of the collective behavior of the social network in the spread of obesity [36][37][38][39][40][41]. Some of these tools are based on the system dynamics approach [42,43], which is pertinent to study the transmission of obesity at the population level, while others use network analysis [33,44], as well as agent-based modeling [45] for investigating at the individual level.…”

Section: A Systematic Reviewmentioning

confidence: 99%

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Smart Homes and Sensors for Surveillance and Preventive Education at Home: Example of Obesity

et al. 2016

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Abstract:(1) Background: The aim of this paper is to show that e-health tools like smart homes allow the personalization of the surveillance and preventive education of chronic patients, such as obese persons, in order to maintain a comfortable and preventive lifestyle at home. (2) Technologies and methods: Several types of sensors allow coaching the patient at home, e.g., the sensors recording the activity and monitoring the physiology of the person. All of this information serves to personalize serious games dedicated to preventive education, for example in nutrition and vision. (3) Results: We built a system of personalized preventive education at home based on serious games, derived from the feedback information they provide through a monitoring system. Therefore, it is possible to define (after clustering and personalized calibration) from the at home surveillance of chronic patients different comfort zones where their behavior can be estimated as normal or abnormal and, then, to adapt both alarm levels for surveillance and education programs for prevention, the chosen example of application being obesity.

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Random Modelling of Contagious Diseases

Cited by 16 publications

References 98 publications

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

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

Discrete dynamics of contagious social diseases: Example of obesity

Smart Homes and Sensors for Surveillance and Preventive Education at Home: Example of Obesity

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