Population modeling planning

SAVING TIME IN A POPULATION MODELING PROJECT 11.2.1 Planning... 

Planning is crucial if one is to complete a population modeling project as efficiently as possible (16). A population modeling project can be characterized by its goal and the various tasks involved in the project, including their duration. Figure 11.1 shows the time schedule allocated to a project, starting with the definition of the... 

FIGURE 11.2. Revised time plan for a population modeling project. The box with dashed lines indicates tasks performed intermittently during this time period. The solid black line indicates the time period referred to as the modeling time. 

In the first stages of the development of an Action plan all control options are considered. In the case of lakes, this process is aided by a PC-based expert system , PACGAP, which looks at the physical and chemical characteristics of the lake to determine the most likely option for control. Once further, more detailed information has been collected on the lake s nutrient inputs and other controlling factors, amore complex interactive model can be used (Phytoplankton Response To Environmental CHange, PROTECH-2) to define the efficacy of proposed control options more accurately. This model is able to predict the development of phytoplankton species populations under different nutrient and stratification regimes. 

For a limited number of exposure pathways (primarily inhalation of air in the vicinity of sources), pollutant fate and distribution models have been adapted to estimate population exposure. Examples of such models include the SAI and SRI methodologies developed for EPA s Office of Air Quality Planning and Standards (1,2), the NAAQS Exposure Model (3), and the GEMS approach developed for EPA s Office of Toxic Substances (4). In most cases, however, fate model output will serve as an independent input to an exposure estimate. 

Risk assessment is an essential feature of disaster planning and is in essence a calculation or model of risk, in which a comprehensive inventory is created including all existing and potential dangers, the population most likely to be affected by each danger, and a prediction of the health consequences. Risk analysis uses the elements of hazard analysis and vulnerability... 

The model assumes that the interindividual variabilities in the two populations are the same, which was implicit in the model building plan because no variability differences in the subject populations were to be tested. The plan was written based on the assumption that any such differences would have limited influence on the population average parameter estimates. This assumption is certainly debatable, however, in some sense similar to whether such differences should be assumed in formulations with standard BE assessments. 

The covariate distribution models, which describe the characteristics of the population (weight, height, sex, race, etc.), must be determined and used for the creation of the study population. The virtual subjects are drawn from a probability distribution that can be one of many types (normal, lognormal, binomial, uniform) but that needs to be described in the study plan. For assignments to sex one must account for what proportion of patients will be female versus male. Furthermore, when creating this population the joint distribution of variables such as height and weight or sex and size must be accounted for. This then leads to the execution model. 

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