samsi 9/16-19/2007 workshop (rtp, nc) (c) jacobson 20071 a discussion: random thoughts and risky...
TRANSCRIPT
SAMSI 9/16-19/2007 Workshop (RTP, NC)
(C) Jacobson 2007 1
A Discussion:Random Thoughts and
Risky Propositions
Sheldon H. JacobsonDirector, Simulation and Optimization Laboratory
Department of Computer ScienceUniversity of Illinois
Urbana, IL [email protected]
https://netfiles.uiuc.edu/shj/www/shj.html
SAMSI 9/16-19/2007 Workshop (RTP, NC)
(C) Jacobson 2007 2
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.
SAMSI 9/16-19/2007 Workshop (RTP, NC)
(C) Jacobson 2007 3
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.
SAMSI 9/16-19/2007 Workshop (RTP, NC)
(C) Jacobson 2007 4
A Simple Schematic
EnvironmentalRisks
(Natural, Man-made)
Health
(Human, Animal,Birds, Insects)
Black Box
Geologic Industrial
MortalityMorbidity
(Chronic, Acute)
SAMSI 9/16-19/2007 Workshop (RTP, NC)
(C) Jacobson 2007 5
•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
SAMSI 9/16-19/2007 Workshop (RTP, NC)
(C) Jacobson 2007 6
•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”
SAMSI 9/16-19/2007 Workshop (RTP, NC)
(C) Jacobson 2007 7
•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”
SAMSI 9/16-19/2007 Workshop (RTP, NC)
(C) Jacobson 2007 8
•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”
SAMSI 9/16-19/2007 Workshop (RTP, NC)
(C) Jacobson 2007 9
•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”
SAMSI 9/16-19/2007 Workshop (RTP, NC)
(C) Jacobson 2007 10
•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
SAMSI 9/16-19/2007 Workshop (RTP, NC)
(C) Jacobson 2007 11
There are many more questions than answers.
Key Observation
?