Publicação
Computational implementation of epidemic models
| Resumo: | The main objective is to implement computationally several epidemiological models. The main purpose is to simulate the spread of an infection person to person. In the first approach deterministic methods will be used. A deterministic algorithm is an algorithm which, given a particular input, will always produce the same output. In a second part we will focus on stochastic methods. In probability theory, a stochastic process is a collection of random variables, representing the evolution of some system of random values over time. This is the probabilistic counterpart to a deterministic process. Instead of describing a process which can only evolve in one way, in a stochastic process there is some indeterminacy: even if the initial condition is known, there are several directions in which the process may evolve. Even when trying to include as many realistic features in a model as possible there is a limit to how close a model can get to reality, and models can never completely predict what will happen in a given situation. It’s for example impossible to predict how people will adapt and change behavior as a disease starts spreading. Models are very useful as guidance for health professionals when deciding about preventive measures aiming at reducing the spread of the disease. |
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| Autores principais: | Ronco, Nicolas |
| Outros Autores: | Balsa, Carlos |
| Assunto: | Epidemic Mathematical model Eterministic Stochastic |
| Ano: | 2016 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | The main objective is to implement computationally several epidemiological models. The main purpose is to simulate the spread of an infection person to person. In the first approach deterministic methods will be used. A deterministic algorithm is an algorithm which, given a particular input, will always produce the same output. In a second part we will focus on stochastic methods. In probability theory, a stochastic process is a collection of random variables, representing the evolution of some system of random values over time. This is the probabilistic counterpart to a deterministic process. Instead of describing a process which can only evolve in one way, in a stochastic process there is some indeterminacy: even if the initial condition is known, there are several directions in which the process may evolve. Even when trying to include as many realistic features in a model as possible there is a limit to how close a model can get to reality, and models can never completely predict what will happen in a given situation. It’s for example impossible to predict how people will adapt and change behavior as a disease starts spreading. Models are very useful as guidance for health professionals when deciding about preventive measures aiming at reducing the spread of the disease. |
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