Children Summary Germs, or microbes, are found everywhere - in the air, soil, and water. There are also germs on your skin and in your body. Many of them are harmless, and some can even be helpful. But some of them can make you sick.
This can often lead to interesting work being ignored, or, more significantly, the uncritical acceptance of the results of models. In the presentation of mathematical models a balance is required so that sufficient and comprehensible descriptions can move the subject forward under the critical scrutiny of the scientific community.
This paper aims to provide an overview of the terms and methods used in mathematical models and a brief illustration of the use of models in sexually transmitted disease STD epidemiology.
Thus, the central role of creating and analysing mathematical models is to develop our understanding of a system. Once the transmission dynamics of an infectious disease are appropriately described by a model it is possible to evaluate the potential impact of proposed interventions.
Models should assist in the identification of successful interventions, their necessary scale, and the role or ability of new technologies to deliver public health benefits. Often mathematical models are called upon to forecast the future. This has been particularly true for HIV where many models have been used with varying success.
In sexually transmitted disease epidemiology mathematical models can describe the position of individuals within the network of sexual partnerships via which infections spread allowing a more complete identification of risks for acquiring and transmitting infection.
Models can be categorised by a number of key criteria. Compartmental versus distributional There are different ways of representing the modelled variables.
A compartmental model of gonorrhoea might divide the population into those susceptible, symptomatically and asymptomatically infected, whereas a distributional model could describe gradations of symptoms, perhaps related to the size of the gonococcal colony or the level of immune response.
Discrete versus continuous Change in the model population can take place either as a smooth continuous process or in discrete steps. In the former differential calculus developed to explore changes in one variable with a diminishingly small change in another variable is used, whereas in discrete models, difference equations, which reflect the change over the whole time step are used.
For example, in demography because of the availability of fertility and mortality rates in 5 year age groups the United Nations uses 5 year time steps to project population growth.
Deterministic versus stochastic In a deterministic model events are not subject to chance and two realisations of a model using the same parameters and exact starting conditions will give exactly the same results. However, results can diverge in the case of deterministic chaos because it is impossible to exactly specify starting conditions and the value of variables.
There is debate over the role of chaos in measles where small differences in the fraction infected during troughs in incidence grow into larger differences as infection spreads. In stochastic models chance is taken into account. There are a number of ways to allow the events in a model to be influenced by chance, but the most common and rigorous method is Monte Carlo simulation, where the set of possible next events is defined with a probability attached to each.
A random number generator is then used to calculate when the next event will occur and which of the range of possible events it will be. With recent increases in the speed of computers stochastic models have become more convenient and should have an important role in understanding the persistence and control of the bacterial STDs when they are at a low prevalence.
Population averages versus individual based simulations An important distinction in the modelling of sexually transmitted infections is whether the population is represented in groups with average rates of contact, risk of transmission, and progress through stages of infection or whether individuals and their contacts are explicitly simulated.
The latter level of detail provides a powerful tool as it allows microstructures in the dynamic network of sexual partnerships to be included.
In between the extremes of a homogeneous population and individual based simulation it is possible to divide the population into groups with similar characteristics—for example, of a similar age, sex, or patterns of sexual activity.
Thus, the observed heterogeneity in the population can be captured by stratifying the model population.
Much of our initial understanding of the epidemiological consequences of the natural history of STDs has been based on studies using population averages, 3 understanding more detailed sexual network data has required individual based simulation models.
For infectious diseases the transmission term is assumed to be non-linear as it is a function of the interaction between infectious and susceptible individuals.
Many health economic models make linear assumptions—that is, treating one more individual reduces the number of cases by one.
However, there are knock on effects which depend upon the epidemiological context.Apr 30, · Sexually Transmitted Diseases Essays (Examples) Filter results by: Author not Available. "An Introduction to Sexually Transmitted Infections." National Institute of Allergy and Infectious caninariojana.com July 25 July In order to protect against unwanted pregnancies and the spread of sexually transmitted diseases and infections.
Introduction. Jessica Evert, MD This is not the case. STDs are biological infections not unlike the common cold How to Avoid Getting Sexually Transmitted Diseases.
Introduction. The epidemics of HIV and sexually transmitted infections (STIs) are characterised by high incidence and high prevalence in many populations.1, 2 It is also apparent that STIs facilitate the transmission of HIV. 3 In recent years the rapid spread of HIV among women has underscored their risk of infection.
Women are vulnerable to HIV for a number of reasons. Sexually transmitted diseases in children: introduction. S Estreich and G E Forster Mulcahy FM, Lacey CJ. Sexually transmitted infections in adolescent girls. Genitourin Med. McGrath PJ, Pless RP, Corsini L, Bennett S.
Is childhood sexual abuse really increasing in prevalence? An analysis of the evidence. Pediatrics. Jul.
Introduction Sexually Transmitted Diseases are a substantialpublic health problem and remain a critical public health concern in America. This is despite it being largely preventable. According to Healthy People , Sexually Transmitted Diseases refer to at least 25 infectious organisms which a.
Infectious diseases can cause many different symptoms. Some are so mild that you may not even notice any symptoms, while others can be life-threatening. There are treatments for some infectious diseases, but for others, such as some viruses, you can only treat your symptoms.