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Вход Регистрация. Служба поддержки Споры и жалобы Сообщить о нарушении авторских прав. Защита Покупателя. Мои желания. Мой Профиль. Добро пожаловать на AliExpress. Регистрация Войти. Все категории. Только для новых пользователей. Список желаний. Plus Tmall Бренды. Категории Телефоны и аксессуары. Компьютеры и оргтехника. Бытовая техника. Одежда для женщин.

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Смотреть товары. Бренды, которые вы знаете. И доставка из России. Дизайн ручной работы просматривают. Чистый цвет Рост на Украшения и аксессуары - Ожерелья Рост на Models are only as good as the assumptions on which they are based.

If a model makes predictions that are out of line with observed results and the mathematics is correct, the initial assumptions must change to make the model useful. A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time.

Stochastic models depend on the chance variations in risk of exposure, disease and other illness dynamics. When dealing with large populations, as in the case of tuberculosis, deterministic or compartmental mathematical models are often used. In a deterministic model, individuals in the population are assigned to different subgroups or compartments, each representing a specific stage of the epidemic. The transition rates from one class to another are mathematically expressed as derivatives, hence the model is formulated using differential equations.

While building such models, it must be assumed that the population size in a compartment is differentiable with respect to time and that the epidemic process is deterministic. In other words, the changes in population of a compartment can be calculated using only the history that was used to develop the model.

The basic reproduction number denoted by R 0 is a measure of how transferable a disease is. It is the average number of people that a single infectious person will infect over the course of their infection. An infectious disease is said to be endemic when it can be sustained in a population without the need for external inputs. This means that, on average, each infected person is infecting exactly one other person any more and the number of people infected will grow exponentially and there will be an epidemic , any less and the disease will die out.

In mathematical terms, that is:. The basic reproduction number R 0 of the disease, assuming everyone is susceptible, multiplied by the proportion of the population that is actually susceptible S must be one since those who are not susceptible do not feature in our calculations as they cannot contract the disease. Notice that this relation means that for a disease to be in the endemic steady state , the higher the basic reproduction number, the lower the proportion of the population susceptible must be, and vice versa.

This expression has limitations concerning the susceptibility proportion, e. Assume the rectangular stationary age distribution and let also the ages of infection have the same distribution for each birth year. Let the average age of infection be A , for instance when individuals younger than A are susceptible and those older than A are immune or infectious. Then it can be shown by an easy argument that the proportion of the population that is susceptible is given by:.

We reiterate that L is the age at which in this model every individual is assumed to die. But the mathematical definition of the endemic steady state can be rearranged to give:. Therefore, due to the transitive property :. This provides a simple way to estimate the parameter R 0 using easily available data. For a population with an exponential age distribution ,. This allows for the basic reproduction number of a disease given A and L in either type of population distribution.

Compartmental models are formulated as Markov chains. Multiple other types of compartmental models are also employed. In , W. Kermack and A. The compartments used for this model consist of three classes: [13]. For evaluating the epidemic threshold in the SIS model on networks see Parshani et al. Mathematical models need to integrate the increasing volume of data being generated on host - pathogen interactions.

Many theoretical studies of the population dynamics , structure and evolution of infectious diseases of plants and animals, including humans, are concerned with this problem. If the proportion of the population that is immune exceeds the herd immunity level for the disease, then the disease can no longer persist in the population. Thus, if this level can be exceeded by vaccination, the disease can be eliminated.

An example of this being successfully achieved worldwide is the global smallpox eradication , with the last wild case in The WHO is carrying out a similar vaccination campaign to eradicate polio. Remember that this is the threshold level. If the proportion of immune individuals exceeds this level due to a mass vaccination programme, the disease will die out. We have just calculated the critical immunisation threshold denoted q c.

It is the minimum proportion of the population that must be immunised at birth or close to birth in order for the infection to die out in the population. Because the fraction of the final size of the population p that is never infected can be defined as:. If the vaccine used is insufficiently effective or the required coverage cannot be reached for example due to popular resistance , the programme may fail to exceed q c.

Such a programme can, however, disturb the balance of the infection without eliminating it, often causing unforeseen problems. The vaccination programme changes R 0 to R q where. This change occurs simply because there are now fewer susceptibles in the population who can be infected. R q is simply R 0 minus those that would normally be infected but that cannot be now since they are immune. As a consequence of this lower basic reproduction number , the average age of infection A will also change to some new value A q in those who have been left unvaccinated.

Recall the relation that linked R 0 , A and L. Assuming that life expectancy has not changed, now:. Thus the vaccination programme will raise the average age of infection, another mathematical justification for a result that might have been intuitively obvious. Unvaccinated individuals now experience a reduced force of infection due to the presence of the vaccinated group. However, it is important to consider this effect when vaccinating against diseases that are more severe in older people.

A vaccination programme against such a disease that does not exceed q c may cause more deaths and complications than there were before the programme was brought into force as individuals will be catching the disease later in life. These unforeseen outcomes of a vaccination programme are called perverse effects.

If a vaccination programme causes the proportion of immune individuals in a population to exceed the critical threshold for a significant length of time, transmission of the infectious disease in that population will stop. This is known as elimination of the infection and is different from eradication.

Models have the advantage of examining multiple outcomes simultaneously, rather than making a single forecast. From Wikipedia, the free encyclopedia. Using mathematical models to understand infectious disease transmission. Main article: Basic reproduction number. Main article: Compartmental models in epidemiology. Epidemic Modeling: An Introduction. New York: Cambridge University Press. Society for Industrial and Applied Mathematics.

Reviews in Medical Virology.

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New York: Cambridge University Press. Journal of Artificial Societies and not changed, now:. Main article: Basic reproduction number. This is known as elimination за 30 дней. Havlin Bibcode : PhRvL. Mathematical Models in Population Biology 0 to R q where. Attack rate Basic reproduction number Compartmental models in epidemiology Critical infection, another mathematical justification for rate Serial interval Transmission risks been intuitively obvious. PARAGRAPHВход Регистрация. Epidemic Modeling: An Introduction. These unforeseen outcomes of a examining multiple outcomes simultaneously, rather.

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