EpidemicAn epidemic (from Greek ἐπί epi "upon or above" and δῆμος demos "people") is the rapid spread of disease to a large number of hosts in a given population within a short period of time. For example, in meningococcal infections, an attack rate in excess of 15 cases per 100,000 people for two consecutive weeks is considered an epidemic. Epidemics of infectious disease are generally caused by several factors including a change in the ecology of the host population (e.g.
Emerging infectious diseaseAn emerging infectious disease (EID) is an infectious disease whose incidence has increased recently (in the past 20 years), and could increase in the near future. The minority that are capable of developing efficient transmission between humans can become major public and global concerns as potential causes of epidemics or pandemics. Their many impacts can be economic and societal, as well as clinical. EIDs have been increasing steadily since at least 1940.
Principal (commercial law)In commercial law, a principal is a person, legal or natural, who authorizes an agent to act to create one or more legal relationships with a third party. This branch of law is called agency and relies on the common law proposition qui facit per alium, facit per se (from Latin: "he who acts through another, acts personally"). It is a parallel concept to vicarious liability (in which one person is held liable for the acts or omissions of another) in criminal law or torts.
Markov random fieldIn the domain of physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. In other words, a random field is said to be a Markov random field if it satisfies Markov properties. The concept originates from the Sherrington–Kirkpatrick model. A Markov network or MRF is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed and acyclic, whereas Markov networks are undirected and may be cyclic.
RandomnessIn common usage, randomness is the apparent or actual lack of definite pattern or predictability in information. A random sequence of events, symbols or steps often has no order and does not follow an intelligible pattern or combination. Individual random events are, by definition, unpredictable, but if the probability distribution is known, the frequency of different outcomes over repeated events (or "trials") is predictable. For example, when throwing two dice, the outcome of any particular roll is unpredictable, but a sum of 7 will tend to occur twice as often as 4.
Infectious diseases (medical specialty)Infectious diseases or ID, also known as infectiology, is a medical specialty dealing with the diagnosis and treatment of infections. An infectious diseases specialist's practice consists of managing nosocomial (healthcare-acquired) infections or community-acquired infections. An ID specialist investigates the cause of a disease to determine what kind of Bacteria, viruses, parasites, or fungi the disease is caused by. Once the pathogen is known, an ID specialist can then run various tests to determine the best antimicrobial drug to kill the pathogen and treat the disease.
Normal distributionIn statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter is its standard deviation. The variance of the distribution is . A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate.
Conditional random fieldConditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account. To do so, the predictions are modelled as a graphical model, which represents the presence of dependencies between the predictions. What kind of graph is used depends on the application.
Law of agencyThe law of agency is an area of commercial law dealing with a set of contractual, quasi-contractual and non-contractual fiduciary relationships that involve a person, called the agent, that is authorized to act on behalf of another (called the principal) to create legal relations with a third party. Succinctly, it may be referred to as the equal relationship between a principal and an agent whereby the principal, expressly or implicitly, authorizes the agent to work under their control and on their behalf.
Eradication of infectious diseasesThe eradication of infectious diseases is the reduction of an infectious disease's prevalence in the global host population to zero. Two infectious diseases have successfully been eradicated: smallpox in humans, and rinderpest in ruminants. There are four ongoing programs, targeting the human diseases poliomyelitis (polio), yaws, dracunculiasis (Guinea worm), and malaria. Five more infectious diseases have been identified as potentially eradicable with current technology by the Carter Center International Task Force for Disease Eradication—measles, mumps, rubella, lymphatic filariasis (elephantiasis) and cysticercosis (pork tapeworm).
Incubation periodIncubation period (also known as the latent period or latency period) is the time elapsed between exposure to a pathogenic organism, a chemical, or radiation, and when symptoms and signs are first apparent. In a typical infectious disease, the incubation period signifies the period taken by the multiplying organism to reach a threshold necessary to produce symptoms in the host. While latent or latency period may be synonymous, a distinction is sometimes made whereby the latent period is defined as the time from infection to infectiousness.
Stable distributionIn probability theory, a distribution is said to be stable if a linear combination of two independent random variables with this distribution has the same distribution, up to location and scale parameters. A random variable is said to be stable if its distribution is stable. The stable distribution family is also sometimes referred to as the Lévy alpha-stable distribution, after Paul Lévy, the first mathematician to have studied it. Of the four parameters defining the family, most attention has been focused on the stability parameter, (see panel).
