Health state model

Figure 24.1 A health state model depicting all five transitional health states for patients undergoing renal transplant.

In October of 2001, the CDC put together a draft model law for the states, Model State Emergency Health Powers Act. The proposed model law would give state officials broad powers to close buildings, take over hospitals, and order quarantines during a biological attack. The various state legislatures would have to decide to adopt, or not adopt, the model law. A CDC conference considered a summary of public health powers necessary for adequate response to a bioterrorism incident that is reproduced below. 

A Markov process model describes several discrete health states in which a person can exist at time t, as well as the health states into which the person may move at time t +1. A person can reside in just one health state at any given time. The progression from time t to time t +1 is known as a cycle. All clinically important events are modeled as transitions in which a person moves from one health state to another. The probabilities associated with each change between health states are known as transition probabilities. Each transition probability is a function of the health state and the treatment. 

Markov models are used to describe disease as a series of probable transitions between health states. The methodology has considerable appeal for use in phar-macometrics since it offers a method to evaluate patient compliance with prescribed medication regimen, multiple health states simultaneously, and transitions between different sleep stages. An overview of the Markov model is provided together with the Markovian assumption. The most commonly used form of the Markov model, the discrete-time Markov model, is described as well as its application in the mixed effects modeling setting. The chapter concludes with a discussion of a hybrid Markov mixed effects and proportional odds model used to characterize an adverse effect that lends itself to this combination modeling approach. 

II-5] UNITED STATES NUCLEAR REGULATORY COMMISSION, Health Effects Models for Nuclear Power Plant Accidents Consequence Analysis, Rep. NUREG/CR-4214, USNRC, Washington, DC (1989). 

The FDA Food Code outlines specific rules on which state and connty health departments model their retail food regulations. Healthcare organizations can experience health inspections conducted by federal, state, or local officials. Most food inspectors possess a college degree and understand food quality standards, maintenance requirements, and food safety preparation practices (Table 10.9). The main tasks of a food inspector can include the following ... 

The objective of condition monitoring is to assess the health state of industrial components and to identify possible incipient faults (Venkatasubramanian et al. 2003a, 2003b, 2003c, Hines et al. 2007). To this aim, a model is usually built to reconstruct the values of the monitored signals expected in normal conditions (Hameed et al. 2009). During operation, observed signal measurements are compared with the reconstructions provided by the model abnormal conditions are detected when the reconstructions are remarkably different from the measurements. 

Baraldi, P., Zio, E., Mangili, E, Gola, G., Nystad, B.H., 2013b. Ensemble of Kernel Regression Models for Assessing the Health State of Choke Valves in Offshore Oil Platforms. International Journal of Computational Intelligence Systems, on-line. 

Hidden Markov Model is an extension of the Markov chain in which the state process are latent and can be only revealed through an observation process. This is where the word hidden comes from. In the deterioration modeling framework, the hidden state process represents the health states of the equipment, while the observations can be measurable signals such as the vibration signals or the features extracted from condition monitoring data. The relation between these two processes is represented by a probabilistic model. Figure 1 illustrates an example of an HMM model. 

The essence of this approach is on the use of the FS-TARMA model residual signal iv[t], and more specifically its time-dependent variance as the characteristic quantity (or feature) in the decisirni-making mechanism. The underlying thesis is that for each distinct health state (for instance. 

Models of the selected (tmder the healthy condition) strucmre are subsequently fitted (estimated) for each data record corresponding to the healthy and each damaged state of the structure (40 models per health state, each one based on a distinct data record). In Fig. 2d the sample distribution of RSS/SSS and BIC of the above selected models are presented for the various health states. The FS-TAR model structure tmiformly (for all health states and data records) achieves the lowest BIC although its RSS/SSS values are not minimal. 

Selection of pollution control methods is generally based on the need to control ambient air quaUty in order to achieve compliance with standards for critetia pollutants, or, in the case of nonregulated contaminants, to protect human health and vegetation. There are three elements to a pollution problem a source, a receptor affected by the pollutants, and the transport of pollutants from source to receptor. Modification or elimination of any one of these elements can change the nature of a pollution problem. For instance, tall stacks which disperse effluent modify the transport of pollutants and can thus reduce nearby SO2 deposition from sulfur-containing fossil fuel combustion. Although better dispersion aloft can solve a local problem, if done from numerous sources it can unfortunately cause a regional one, such as the acid rain now evident in the northeastern United States and Canada (see Atmospheric models). References 3—15 discuss atmospheric dilution as a control measure. The better approach, however, is to control emissions at the source. 

Atmospheric aerosols have a direct impact on earth s radiation balance, fog formation and cloud physics, and visibility degradation as well as human health effect[l]. Both natural and anthropogenic sources contribute to the formation of ambient aerosol, which are composed mostly of sulfates, nitrates and ammoniums in either pure or mixed forms[2]. These inorganic salt aerosols are hygroscopic by nature and exhibit the properties of deliquescence and efflorescence in humid air. That is, relative humidity(RH) history and chemical composition determine whether atmospheric aerosols are liquid or solid. Aerosol physical state affects climate and environmental phenomena such as radiative transfer, visibility, and heterogeneous chemistry. Here we present a mathematical model that considers the relative humidity history and chemical composition dependence of deliquescence and efflorescence for describing the dynamic and transport behavior of ambient aerosols[3]. 

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