Simulation of stochastic micropopulation models--I. The SUMMERS simulation shell.

A generic, abstract model and the simulation shell based on it, both called SUMMERS, are used as a framework for the implementation of stochastic micropopulation models; in these, each individual is followed separately while moving through a sequence of states. The shell supports groups of interacting members, individual characteristics and multiple simultaneous activities. Stochastic decisions may be made using Monte Carlo rules. Keywords control the simulations and the reports generated. A sensitivity analysis utility allows assessment of the dependency of outcomes on model features. Extensive use has been made of software engineering techniques. Specializations of SUMMERS are described in subsequent papers.

[1]  E Ackerman,et al.  A stochastic model for competition between viral agents in the presence of interference. 1. Live virus vaccine in a randomly mixing population, Model 3. , 1968, American journal of epidemiology.

[2]  I. Longini,et al.  Efficacy of virucidal nasal tissues in interrupting familial transmission of respiratory agents. A field trial in Tecumseh, Michigan. , 1988, American journal of epidemiology.

[3]  E Ackerman,et al.  Parameter sensitivity of a model of viral epidemics simulated with Monte Carlo techniques. II. Durations and peaks. , 1993, International journal of bio-medical computing.

[4]  S K Seaholm,et al.  Software systems to control sensitivity studies of Monte Carlo simulation models. , 1988, Computers and biomedical research, an international journal.

[5]  A. Langworthy,et al.  An influenza simulation model for immunization studies. , 1976, American journal of epidemiology.

[6]  E Ackerman,et al.  Simulation of stochastic micropopulation models--II. VESPERS: epidemiological model implementations for spread of viral infections. , 1993, Computers in biology and medicine.

[7]  E. Ackerman,et al.  Polychotomous multivariate models for coronary heart disease simulation. II. Comparisons of risk functions. , 1991, International journal of bio-medical computing.

[8]  E Ackerman,et al.  Stochastic two-agent epidemic simulation models for a community of families. , 1971, American journal of epidemiology.

[9]  E Ackerman,et al.  Latin hypercube sampling and the sensitivity analysis of a Monte Carlo epidemic model. , 1988, International journal of bio-medical computing.

[10]  E Ackerman,et al.  Order of response surfaces for representation of a Monte Carlo epidemic model. , 1988, International journal of bio-medical computing.

[11]  L C Gatewood,et al.  Preventing heart disease: is treating the high risk sufficient? , 1988, Journal of clinical epidemiology.

[12]  E Ackerman,et al.  Polychotomous multivariate models for coronary heart disease simulation. I. Tests of a logistic model. , 1991, International journal of bio-medical computing.

[13]  L C Gatewood,et al.  A generalized stochastic model for simulation of epidemics in a heterogeneous population (model VI). , 1972, Computers in biology and medicine.

[14]  Danny Kilis,et al.  A micropopulation model adaptation for neural network studies , 1989, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society,.

[15]  W. Hauser,et al.  Complex segregation analysis of febrile convulsions. , 1987, American journal of human genetics.

[16]  M McGue,et al.  Genetic linkage in schizophrenia: perspectives from genetic epidemiology. , 1989, Schizophrenia bulletin.

[17]  I. Longini,et al.  Simulation of mechanisms of viral interference in influenza. , 1990, International journal of epidemiology.

[18]  L. Elveback,et al.  AN EXTENSION OF THE REED-FROST EPIDEMIC MODEL FOR THE STUDY OF COMPETITION BETWEEN VIRAL AGENTS IN THE PRESENCE OF INTERFERENCE. , 1964, American journal of hygiene.

[19]  T. Biggerstaff,et al.  Reusability Framework, Assessment, and Directions , 1987, IEEE Software.

[20]  E. Ackerman,et al.  Polychotomous multivariate models for coronary heart disease simulation. III. Model sensitivities and risk factor interventions. , 1991, International journal of bio-medical computing.

[21]  B. Wilson,et al.  Monte Carlo modeling of light propagation in highly scattering tissues. I. Model predictions and comparison with diffusion theory , 1989, IEEE Transactions on Biomedical Engineering.

[22]  E Ackerman,et al.  Parameter sensitivity of a model of viral epidemics simulated with Monte Carlo techniques. III. Optimization strategies. , 1993, International journal of bio-medical computing.

[23]  E Ackerman,et al.  Parameter sensitivity of a model of viral epidemics simulated with Monte Carlo techniques. I. Illness attack rates. , 1993, International journal of bio-medical computing.

[24]  E Ackerman,et al.  LINKERS: a simulation programming system for generating populations with genetic structure. , 1990, Computers in biology and medicine.

[25]  L. Gatewood,et al.  Using serum cholesterol to identify high risk and stimulate behavior change: will it work? , 1989, Annals of medicine.

[26]  B. Wilson,et al.  Monte Carlo modeling of light propagation in highly scattering tissues. II. Comparison with measurements in phantoms , 1989, IEEE Transactions on Biomedical Engineering.

[27]  Eugene Ackerman,et al.  Design considerations for the application of expert system techniques to sensitivity analyses of an epidemic model , 1990 .

[28]  E Ackerman,et al.  Simulation of stochastic micropopulation models--III. COGNET: an artificial neural network for visual recognition. , 1993, Computers in biology and medicine.