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A Discussion: Random Thoughts and Risky Propositions

A Discussion: Random Thoughts and Risky Propositions. Sheldon H. Jacobson Director, Simulation and Optimization Laboratory Department of Computer Science University of Illinois Urbana, IL shj@uiuc.edu https://netfiles.uiuc.edu/shj/www/shj.html.

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A Discussion: Random Thoughts and Risky Propositions

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  1. A Discussion:Random Thoughts and Risky Propositions Sheldon H. Jacobson Director, Simulation and Optimization Laboratory Department of Computer Science University of Illinois Urbana, IL shj@uiuc.edu https://netfiles.uiuc.edu/shj/www/shj.html (C) Jacobson 2007

  2. Lianne SheppardEnvironmental Health Modeling Methods to measure and identify environmental effect / risk on health. Important Problem Large number and amount of substances that can be scrutinized. Important policy and economic implications. (C) Jacobson 2007

  3. Anne SmithEnvironmental Risk Assessment • Risk Assessment for Ambient Air Pollutants • Important Problem • Air quality can be measured by a large quantity of substances / toxins. • Numerous sources of uncertainty in the process. • Important policy and economic implications. (C) Jacobson 2007

  4. A Simple Schematic Black Box Health Environmental Risks (Human, Animal, Birds, Insects) (Natural, Man-made) Mortality Morbidity (Chronic, Acute) Geologic Industrial (C) Jacobson 2007

  5. The Analysis Process Models, Models, Models (Environmental Health Modeling) Disease Quantifies the true environmental exposure to the disease outcome Exposure Captures the distribution of exposure over space, time, and individuals Measurement Quantifies measured exposure to the true unknown exposure Data, Data, Data…. Quality, quantity, cleanliness Not always clear what one is getting (C) Jacobson 2007

  6. Observations and “Food for Thought” Model simplicity versus data complexity Is it better to have a complex model with little data available or a simple model with much data available? Model Validation and Verification is a challenge Invisible (environmental, personal, policy) biases can creep into the analysis. Can such biases cloud what one is trying to measure / identify? How does one separate the cause/ effect relationship from system noise? (C) Jacobson 2007

  7. Observations and “Food for Thought” Design of Experiment Numerous challenges. Input controls are not that easy to control. Fewer questions can lead to more insight Focus study on particular relationship(s). Are focused studies even possible? Breadth versus depth of analysis. (C) Jacobson 2007

  8. Observations and “Food for Thought” Static versus temporal associations Must both be addressed? Knowing “when” may be as challenging as knowing “if”. Many questions can be posed. A “substance” causes what “conditions”? A “condition” is caused by what “substances”? Knowing “If” and “how much” may both be critical. Which questions should be addressed? (C) Jacobson 2007

  9. Observations and “Food for Thought” Which error is most dangerous? Not identifying an effect that exists (false clear) or believing that an effect exists which does not (false alarm)? Policy implications may have “long legs”. Complex system implications. The goal may change. Are we looking for a “needle in a haystack”, or should we ask why needles keeps ending up in a haystack, or in a particular section of a haystack? (C) Jacobson 2007

  10. Contemporary Issues Bioterrorism agent monitoring Pandemic influenza, infectious diseases and emerging pathogens Avian flu (H5N1) Prevention, detection, treatment Disease monitoring / epidemiology Can we create models that serve as “canaries in a mine shaft?” (C) Jacobson 2007

  11. Key Observation ? There are many more questions than answers. (C) Jacobson 2007

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