Literatura científica selecionada sobre o tema "Susceptible-Infected-Susceptible (SIS) model"
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Artigos de revistas sobre o assunto "Susceptible-Infected-Susceptible (SIS) model"
SHANG, YILUN. "DISTRIBUTION DYNAMICS FOR SIS MODEL ON RANDOM NETWORKS". Journal of Biological Systems 20, n.º 02 (junho de 2012): 213–20. http://dx.doi.org/10.1142/s0218339012500076.
Texto completo da fontede La Sen, Manuel, A. Ibeas e S. Alonso-Quesada. "A SIS Epidemic Model with Eventual Impulsive Effects". Applied Mechanics and Materials 393 (setembro de 2013): 666–74. http://dx.doi.org/10.4028/www.scientific.net/amm.393.666.
Texto completo da fonteXie, Wenhao, Gongqian Liang, Wei Wang e Yanhong She. "A spatial SIS model with Holling II incidence rate". International Journal of Biomathematics 12, n.º 08 (novembro de 2019): 1950092. http://dx.doi.org/10.1142/s179352451950092x.
Texto completo da fonteCoronel, Aníbal, Fernando Huancas, Ian Hess e Alex Tello. "The diffusion identification in a SIS reaction-diffusion system". Mathematical Biosciences and Engineering 21, n.º 1 (2023): 562–81. http://dx.doi.org/10.3934/mbe.2024024.
Texto completo da fonteDe, A., K. Maity e M. Maiti. "An integrated project of fish and broiler: SIS model with optimal harvesting". International Journal of Biomathematics 09, n.º 06 (2 de agosto de 2016): 1650088. http://dx.doi.org/10.1142/s1793524516500881.
Texto completo da fonteCHAKRABORTY, ABHIJIT, e S. S. MANNA. "DISEASE SPREADING MODEL WITH PARTIAL ISOLATION". Fractals 21, n.º 03n04 (setembro de 2013): 1350015. http://dx.doi.org/10.1142/s0218348x13500151.
Texto completo da fonteDrabo, Abdoul Karim, Frédéric Bere e S. P. Clovis Nitiema. "On a Stochastic Approach to Extensions of the Susceptible-Infected-Susceptible (SIS) Model Applied to Malaria". Journal of Applied Mathematics 2024 (30 de abril de 2024): 1–16. http://dx.doi.org/10.1155/2024/7555042.
Texto completo da fonteEssouifi, Mohamed, e Abdelfattah Achahbar. "A mixed SIR-SIS model to contain a virus spreading through networks with two degrees". International Journal of Modern Physics C 28, n.º 09 (setembro de 2017): 1750114. http://dx.doi.org/10.1142/s0129183117501145.
Texto completo da fontePaoluzzi, Matteo, Marco Leoni e M. Cristina Marchetti. "Information and motility exchange in collectives of active particles". Soft Matter 16, n.º 27 (2020): 6317–27. http://dx.doi.org/10.1039/d0sm00204f.
Texto completo da fonteJi, Chunyan, e Daqing Jiang. "The asymptotic behavior of a stochastic multigroup SIS model". International Journal of Biomathematics 11, n.º 03 (abril de 2018): 1850037. http://dx.doi.org/10.1142/s1793524518500377.
Texto completo da fonteTeses / dissertações sobre o assunto "Susceptible-Infected-Susceptible (SIS) model"
Lindamulage, de Silva Olivier. "On the Efficiency of Decentralized Epidemic Management and Competitive Viral Marketing". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0145.
Texto completo da fonteThis thesis investigates decentralized decision-making in epidemic and viral marketing dynamics. The mathematical framework of game theory is exploited to design and assess the effectiveness of decentralized strategies. The thesis begins with a review of mathematical tools, emphasizing graph theory and game theory. Chapter 2 presents a networked epidemic game where each player (region or country) seeks to implement a tradeoff between socio-economic and health looses, incorporating constraints such as intensive care unit (ICU) availability. Nash equilibrium and Generalized Nash equilibrium are analyzed, and the influence of decentralization on global efficiency is measured using metrics like the Price of Anarchy (PoA) and the Price of Connectedness (PoC). The practical application of the game to a Covid-19 scenario is illustrated. Chapter 3 extends the analysis of Chapter 2 by incorporating opinion dynamics into the decentralized control of a networked epidemic. A new game model is introduced, where players represent geographical aera balancing socio-economic and health losses; the game is built to implement features of practical interests and to possess some mathematical properties (e.g., posynomiality) which makes its analysis tractable. The analysis focuses on the existence and uniqueness of the Generalized Nash Equilibrium (GNE), and an algorithm for computing the GNE is proposed. Numerical simulations quantify the efficiency loss induced by decentralization in the presence and absence of opinion dynamics. The results identify scenarios where decentralization is acceptable in terms of global efficiency measures and highlight the importance of opinion dynamics in decision-making processes. Chapter 4 explores a Stackelberg duopoly model in the context of viral marketing campaigns. The objective is to characterize the optimal allocation strategy of advertising budgets across regions to maximize market share. A relatively simple Equilibrium strategies are derived, and conditions for a "winner takes all" outcome are established. Theoretical findings are complemented by numerical simulations and an example illustrating equilibrium characterization.This thesis offers valuable insights into the effectiveness of decentralized decision-making in the context of epidemic and viral marketing dynamics. The findings have implications for healthcare management, business competition, and related fields
Gerardi, Davi de Oliveira. "Previsão de séries temporais epidemiológicas usando autômatos celulares e algoritmos genéticos". Universidade Presbiteriana Mackenzie, 2010. http://tede.mackenzie.br/jspui/handle/tede/1386.
Texto completo da fonteSIS (susceptible-infected-susceptible) and SIR (susceptible-infectedremoved) epidemiological models based on probabilistic cellular automaton (PCA) are used in order to simulate the temporal evolution of the number of people infected by dengue in the city of Rio de Janeiro in 2007, and to predict the cases of infection in 2008. In the PCA, three different sizes of lattices and two kinds of neighborhoods are utilized, and each time step of simulation is equivalent to one week of real time. A genetic algorithm (GA) is employed to identify the probabilities of the state transition S→I, in order to reproduce the historical series of 2007 related to this disease propagation. These probabilities depend on the number of infected neighbors. Time-varying and constant probabilities are taken into account. These models based on PCA and GA were able of satisfactorily fitting the data from 2007 and making a good prediction for 2008 (mainly about the total number of cases registered during 2008).
Usam-se modelos epidemiológicos SIS (suscetível-infectado-suscetível) e SIR (suscetível-infectado-removido) baseados em autômato celular probabilista (ACP) a fim de simular a evolução temporal do número de pessoas infectadas por dengue, na cidade do Rio de Janeiro em 2007, e de prever os casos de infecção em 2008. No ACP, utilizam-se reticulados de três tamanhos diferentes e dois tipos de vizinhanças, e cada passo de tempo da simulação equivale a uma semana de tempo real. Emprega-se um algoritmo genético (AG) para identificar os valores das probabilidades da transição de estados S→I, de modo a reproduzir a série histórica de 2007 relacionada à propagação dessa doença. Essas probabilidades dependem do número de vizinhos infectados. Probabilidades variantes e invariantes no tempo são consideradas. Esses modelos baseados em ACP e AG foram capazes de fazer um ajuste satisfatório dos dados de 2007 e de fornecerem uma boa previsão para 2008, (principalmente no que diz respeito ao número total de casos registrados em 2008).
Kandhway, Kundan. "Optimal Control of Information Epidemics in Homogeneously And Heterogeneously Mixed Populations". Thesis, 2016. http://etd.iisc.ac.in/handle/2005/2670.
Texto completo da fonteKandhway, Kundan. "Optimal Control of Information Epidemics in Homogeneously And Heterogeneously Mixed Populations". Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2670.
Texto completo da fonteLivros sobre o assunto "Susceptible-Infected-Susceptible (SIS) model"
Bianconi, Ginestra. Epidemic Spreading. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0013.
Texto completo da fonteCapítulos de livros sobre o assunto "Susceptible-Infected-Susceptible (SIS) model"
Tian, Zhuang, Yu Cao, Xuting Zheng e Jingping Zhang. "Modeling of Covid-19 Transmission Using Machine Learning". In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220054.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Susceptible-Infected-Susceptible (SIS) model"
Pinto, Luan Crisostomo, Maria Luiza Rodrigues Defante e Rodrigo Lacerda da Silva. "Epidemiology: Analysis And Construction Of A Mathematical And Computational Model In Complex Systems For The COVID-19 Pandemic". In Encontro Nacional de Computação dos Institutos Federais. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/encompif.2023.229932.
Texto completo da fonte