Chapter 1: Introduction - This chapter introduces and develops the needs and means for obtaining an integrated analysis of information and knowledge about disease processes and their possible risk factors for consequences such as death and morbidity resulting from catastrophic events. The book has an epidemiological causation context but also includes key mechanistic (e.g., biological) aspects of causation as well as other risk factors (e.g., behavioral). It focuses on modeling using heterogeneous data and knowledge to inform policy about consequences that increase risks of toxic and carcinogenic effects from low exposures generated by the combination of natural (e.g., earthquakes) and industrial incidents (e.g., mishaps in chemical and other processes). The focus is causal because causation is essential to science-policy, by combining qualitative and quantitative descriptions that are legally defensible and non-arbitrary. An important aspect of this chapter is the use of surveillance data and their contribution to rapid preventive actions in response to catastrophic incidents - if actions were possible - even though this aspect is not central to the core interest of the book.

Chapter 2: Aggregation of Raw Data: Scales of Measurement, Averages, and Fuzzy Integrals - An important aspect of modeling disease processes requires summarizing data from different sources (their scales of measurement and dimensions) into a single representative number. This chapter exemplifies averaging and other formal means to combine raw information into single numbers; aggregation is the result of combining through different mathematical operators. These operators include the intersection (the min), various types of compensatory and non-compensatory averages, and the union (the max). This chapter includes a discussion and examples of scales of measurement (e.g., nominal, ordinal, and cardinal) as used in public health to describe the levels of risk factors and associated responses as well as the permissible operations using these scales.

Chapter 3: Probabilistic Causation - The chapter discusses and exemplifies probabilistic causation, in the context of the advantages and main limitations of probabilistic methods. The focus is on the predictive aspects of causation, which is the essential reason for modeling cause-and-effect.

Chapter 4: Policy and Representations of Incertitude - Policy is informed by judgments, hard numbers, models of various sorts, and vague propositions that are used routinely to describe events and their possible causes. This chapter exemplifies modeling causation through fuzzy controllers and fuzzy operations (e.g., union and intersection) that aggregate membership by using aspects of established causal models of cancer, respiratory conditions, and other diseases. The importance of these to inform policy-science, relative to what might be obtained from probabilistic methods, is discussed and exemplified.
Chapter 5: Network Analysis - The chapter deals with Boolean, Bayesian, fuzzy (controllers), and other networks as means to develop graphical and mathematical aspects of causation, particularly when the disease process is deconstructed into different and heterogeneous sub-processes. Complex causation, modeled by Bayesian-probabilistic Directed Acyclic Graphs, is exemplified in the context of science-policy choices in public health. Examples from cancer and other multifactorial disease modeling are used to clarify the concepts developed in this chapter. This chapter also includes computing with fuzzy methods such as forward (data-driven search: begin with the data to produce the conclusion), and backward (goal-based search: begin with the conclusion to produce the antecedent) chaining. The advantages and disadvantages of the methods a
About the Author: Paolo F. Ricci, PhD, LLM, is a professor at the EU's Erasmus Mundus Programs in Italy, Spain, and Portugal. Previously he was a professor at the School of Public Health at the University of Massachusetts at Amherst; professor at Holy Names University in Oakland, California, USA; visiting professor at Xiamen University in China; and, professor at University of Bologna in Italy. For more than 30 years, Dr. Ricci -- a senior Fulbright scholar (specialist, 2010-2015) and appointed peer reviewer for Fulbright Specialists selection (2013-2014) -- has led qualitative and quantitative analyses in public health and epidemiology and conducted experimental work in, among other countries, the United States, Canada, Italy, Australia, France, Vietnam, China, the Ivory Coast, and the European Union. He was the head of the Environmental Technologies Clearinghouse of the IEA/OECD (with full diplomatic status) and has served as a peer reviewer of U.S. Department of Energy (DOE) activities regarding human health risks from past nuclear weapons tests at the Nevada Test Site. Until 2014, Dr. Ricci was an associate editor of the journal Environment International for more than 15 years.