1. SAMSI 9/16-19/2007 Workshop (RTP, NC) (C) Jacobson 20071 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

2. SAMSI 9/16-19/2007 Workshop (RTP, NC) (C) Jacobson 20072 Lianne Sheppard Environmental 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.

3. SAMSI 9/16-19/2007 Workshop (RTP, NC) (C) Jacobson 20073 Anne Smith Environmental 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.

4. SAMSI 9/16-19/2007 Workshop (RTP, NC) (C) Jacobson 20074 A Simple Schematic Environmental Risks (Natural, Man-made) Health (Human, Animal, Birds, Insects) Black Box Geologic Industrial Mortality Morbidity (Chronic, Acute)

5. SAMSI 9/16-19/2007 Workshop (RTP, NC) (C) Jacobson 20075 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 The Analysis Process

6. SAMSI 9/16-19/2007 Workshop (RTP, NC) (C) Jacobson 20076 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? Observations and Food for Thought

7. SAMSI 9/16-19/2007 Workshop (RTP, NC) (C) Jacobson 20077 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. Observations and Food for Thought

8. SAMSI 9/16-19/2007 Workshop (RTP, NC) (C) Jacobson 20078 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? Observations and Food for Thought

9. SAMSI 9/16-19/2007 Workshop (RTP, NC) (C) Jacobson 20079 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? Observations and Food for Thought

10. SAMSI 9/16-19/2007 Workshop (RTP, NC) (C) Jacobson 200710 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? Contemporary Issues

11. SAMSI 9/16-19/2007 Workshop (RTP, NC) (C) Jacobson 200711 There are many more questions than answers. Key Observation ?