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

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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

[email protected]

https://netfiles.uiuc.edu/shj/www/shj.html

(C) Jacobson 2007


Lianne sheppard environmental health modeling

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


Anne smith environmental risk assessment

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


A Simple Schematic

Black

Box

Health

Environmental

Risks

(Human, Animal,

Birds, Insects)

(Natural,

Man-made)

Mortality

Morbidity

(Chronic, Acute)

Geologic

Industrial

(C) Jacobson 2007


The analysis process

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


Observations and food for thought

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


Observations and food for thought1

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


Observations and food for thought2

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


Observations and food for thought3

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


Contemporary issues

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


Key observation

Key Observation

?

There are many more questions than answers.

(C) Jacobson 2007


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