A suggested outline of the format and information to be included in a hazard characterization is provided below for reference purposes. Consideration of information to include starts with a summary of the host, pathogen and food matrix factors, and how these affect the likelihood of disease. The specific information included must be tailored to the purpose of the exercise and the pathogen commodity combination under consideration. Inclusion of a fitted doseresponse curve depends on the quality of data available and the goodness of fit.
1. Description of the pathogen, host and food matrix factors and how these affect the disease outcome.
1.1 Characteristics of the pathogen.
1.1.1 Infectivity, virulence or pathogenicity, and disease mechanism.
1.1.2 Genetic factors (e.g. antimicrobial resistance and virulence factors).1.2 Characteristics of the host or host population.
1.2.1 Immunity status.
1.2.2 Age, sex and ethnic group.
1.2.3 Health behaviours.
1.2.4 Physiological status.
1.2.5 Genetic and environmental factors.1.3 Characteristics of the food matrix.
1.3.1 Fat and salt content.
1.3.2 pH and water activity.
1.3.3 Processing related to stresses on microbial populations.2. Public health outcomes.
2.1 Manifestations of disease
2.2 Rationale for the biological endpoints modelled.3. Doseresponse relationship.
3.1 Summary of available data.
3.1.1 Illness given exposure.
3.1.2 Sequelae given illness.
3.1.3 Secondary and tertiary transmission.
3.1.4 Death given illness.3.2 Doseresponse model.
3.2.1 Sources of data used.
3.2.2 Assumptions.
3.2.3 Models.
3.2.4 Goodness of fit of the distribution.
3.2.5 Uncertainty and variability in the estimates.4. Validation and peer review.
5. References.
Notes: 
(1) Sources are [numbered] where appropriate and listed at the end of the appendix. 

(2) Some definitions are general but some are relevant to one specific discipline and in another discipline may be defined differently. In some cases, definitions are associated with a particular discipline, and these are here indicated as: (MRA) for microbiological risk assessment; (disease) for the infectious disease process; and (statistics) for statistical terminology. 
 § 
Accuracy Degree of agreement between average predictions of a model or the average of measurements and the true value of the quantity being predicted or measured.
Adverse effect Change in morphology, physiology, growth, development or life span of an organism which results in impairment of functional capacity or impairment of capacity to compensate for additional stress or increase in susceptibility to the harmful effects of other environmental influences. Decisions on whether or not any effect is adverse require expert judgement. [2]
Akaikes Information Criterion (AIC) and Bayesian Information Criterion (BIC) These are criteria that are used in model selection to select the best model from a set of plausible models. One model is better than another model if it has a smaller AIC (or BIC) value. AIC is based on KullbackLeibler distance in information theory, and BIC is based on integrated likelihood in Bayesian theory. If the complexity of the true model does not increase with the size of the data set, BIC is the preferred criterion, otherwise AIC is preferred. [18]
Aleatory uncertainty Aleatory is of or pertaining to natural or accidental causes and cannot be explained with mechanistic theory. Generally interpreted to be the same as stochastic variability.
Attack rate The proportion of an exposed population at risk that becomes infected or develops clinical illness during a defined period of time. [11]
Asymptomatic Showing or causing no symptoms (a symptom is any subjective evidence of disease or of a patient's condition, i.e. such evidence as perceived by the patient; a change in a patient's condition indicative of some bodily or mental state. Note that a symptom is different from a sign, which is any objective evidence of a disease, i.e. such evidence that is perceptible to the examining physician, as opposed to the subjective sensations (symptoms) of the patient. [12]
Bayesian inference Inference is using data to learn about some uncertain quantity. Bayes' theorem describes how to update a prior distribution about the uncertain quantity using a model (expressing likelihood of observed data) to obtain a posterior distribution. Bayesian inference allows incorporation of prior beliefs and can handle problems with insufficient data for frequentist inference.
Bayesian Information Criterion (BIC) See: Akaikes Information Criterion (AIC)
Bayesian methods These are an approach  founded on Bayes' Theorem  that forms one of the two flows of statistics. Bayesian inference is very strong when only subjective data is available and is useful for using data to improve one's estimate of a parameter.
Bias A term which refers to how far the average statistic lies from the parameter it is estimating, that is, the error which arises when estimating a quantity. It is also referred to as "systematic error". It is the difference between the mean of a model prediction or of a set of measurements and the true value of the quantity being predicted or measured. Errors from chance will cancel each other out in the long run; those from bias will not (statistics). [6]
Bootstrap A numerical method  also referred to as Bootstrap simulation  for inferring sampling distributions and confidence intervals for statistics of random variables. The methodology to estimate uncertainty involves generating subsets of the data on the basis of random sampling with replacements as the data are sampled. Such resampling means that each datum is equally represented in the randomization scheme (statistics). [7]
Case definition The case definition is a standard set of criteria for deciding whether an individual should be classified as having the health condition of interest. [15]
Confidence interval A range of values inferred or believed to enclose the actual or true value of an uncertain quantity with a specified degree of probability. Confidence intervals may be inferred based upon sampling distributions for a statistic.
Contagious distribution A probability distribution describing a stochastic process consisting of a combination of two or more processes. Also referred to as a "mixture distribution" (statistics).
Controllable variability Sources of heterogeneity of values of time, space or different members of a population that can be modified in part  in principle, at least  by intervention, such as a control strategy. For example, variability in the time and temperature history of food storage among storage devices influences variability in pathogen growth among food servings and in principle could be modified through a control strategy. For both population and individual risk, controllable variability is a component of overall variability.
Data quality objective Expectations or goals regarding the precision and accuracy of measurements, inferences from data regarding distributions for inputs, and predictions of the model.
Dose The amount of a pathogen that enters or interacts with an organism. [11]
Doseresponse assessment The determination of the relationship between the magnitude of exposure (dose) to a chemical, biological or physical agent and the severity and/or frequency of associated adverse health effects (response) (MRA). [1]
Expert judgement Judgement involves a reasoned formation of opinions. An expert is someone with special knowledge or experience in a particular problem domain. Expert judgement is documented and can be explained to satisfy outside scrutiny.
Exposure assessment The qualitative and/or quantitative evaluation of the likely intake of biological, chemical and physical agents via food, as well as exposure from other sources if relevant (MRA). [1]
Food Any substance, whether processed, semiprocessed or raw, which is intended for human consumption, and includes drink, chewing gum and any substance that has been used in the manufacture, preparation or treatment of "food", but excludes cosmetics or tobacco, or substances used only as drugs. [1]
Goodness of fit The statistical resemblance of real data to a model, expressed as a strength or degree of fit of the model (statistics). [8]
Goodnessoffit test A procedure for critiquing and evaluating the potential inadequacies of a probability distribution model with respect to its fitness to represent a particular set of observations.
Hazard A biological, chemical or physical agent in, or condition of, food with the potential to cause an adverse health effect (MRA). [1]
Hazard characterization The qualitative and/or quantitative evaluation of the nature of the adverse health effects associated with biological, chemical and physical agents that may be present in food. For chemical agents, a doseresponse assessment should be performed. For biological or physical agents, a doseresponse assessment should be performed if the data are obtainable (MRA). [1]
Hazard identification The identification of biological, chemical and physical agents capable of causing adverse health effects and that may be present in a particular food or group of foods (MRA). [1]
Illness A condition marked by pronounced deviation from the normal healthy state. [12]
Independent action The mean probability of infection per inoculated microorganism is independent of the number of organisms in the inoculum (disease). [4]
Infection The entry and development of an infectious agent in the body of man or animals (disease). [3]
Infectious pathogens, toxicoinfectious pathogens and toxinogenic pathogens Three broad classes of foodborne pathogens are differentiated  infectious, toxicoinfectious or toxigenic  based on their modes of pathogenicity. Infectious pathogens typically have a threestep process by which they elicit a disease response: ingestion of viable cells, the attachment of these cells to specific locations along the gastrointestinal tract (or some other mechanisms for avoiding being swept away due to peristalsis), and the invasion of either the epithelium (gastroenteritis) or the body proper (septicaemia). Toxicoinfectious agents follow a similar threestep process, except that instead of invading the epithelium or body, they remain in the gastrointestinal tract, where they either produce or release toxins that affect sites of the epithelium and/or within the body. Toxinogenic bacteria are differentiated on the basis that they cause disease by producing toxins in foods prior to its ingestion. [16]
Inherent randomness Random perturbations that are irreducible in principle, such as Heisenberg's Uncertainty Principle.
Inputs That which is put in or taken in, or which is operated on or utilized by any process or system (either material or abstract), e.g. the information that is put into a model.
Interindividual variability see Variability.
Intraindividual variability see Variability.
Likelihood The probability of the observed data for various values of the unknown model parameters (statistics). [3]
Markov chain Monte Carlo A general method of sampling arbitrary highlydimensional probability distributions by taking a random walk through configuration space. One changes the state of the system randomly according to a fixed transition rule, thus generating a random walk through state space, s0,s1,s2,.... The definition of a Markov process is that the next step is chosen from a probability distribution that depends only on the present position. This makes it very easy to describe mathematically. The process is often called the drunkard's walk (statistics). [9]
Model A set of constraints restricting the possible joint values of several quantities. A hypothesis or system of belief regarding how a system works or responds to changes in its inputs. The purpose of a model is to represent a particular system of interest as accurately and precisely as necessary with respect to particular decision objectives.
Model boundaries Designated areas of competence of the model, including time, space, pathogens, pathways and exposed populations, and acceptable ranges of values for each input and jointly among all inputs for which the model meets data quality objectives.
Model detail Level of simplicity or detail associated with the functional relationships assumed in the model compared to the actual but unknown relationships in the system being modelled.
Model structure A set of assumptions and inference options upon which a model is based, including underlying theory as well as specific functional relationships.
Model uncertainty Bias or imprecision associated with compromises made or lack of adequate knowledge in specifying the structure and calibration (parameter estimation) of a model.
Outbreak (foodborne) An incident in which two or more persons experience a similar illness after ingestion of the same food, or after ingestion of water from the same source, and where epidemiological evidence implicates the food or water as the source of the illness.
Parameter A quantity used to calibrate or specify a model, such as parameters of a probability model (e.g. mean and standard deviation for a normal distribution). Parameter values are often selected by fitting a model to a calibration data set.
Poisson distribution Poisson distributions model (some) discrete random variables (i.e. variables that may take on only a countable number of distinct values, such as 0, 1, 2, 3, 4,....). Typically, a Poisson random variable is a count of the number of events that occur in a certain time interval or spatial area (statistics). [6]
Precision A measure of the reproducibility of the predictions of a model or repeated measurements, usually in terms of the standard deviation or other measures of variation among such predictions or measurements.
Probability Defined depending on philosophical perspective:
1. The frequency with which we obtain samples within a specified range or for a specified category (e.g. the probability that an average individual with a particular mean dose will develop an illness).
2. Degree of belief regarding the likelihood of a particular range or category.
Probabilistic analysis Analysis in which distributions are assigned to represent variability or uncertainty in quantities. The form of the output of a probabilistic analysis is likewise a distribution.
Probability distribution A function that for each possible value of a discrete random variable takes on the probability of that value occurring, or a curve which specifies by means of the area under the curve over an interval the probability that a continuous random variable falls within the interval (the probability density function).
Qualitative risk assessment A risk assessment based on data that, while forming an adequate basis for numerical risk estimations, nonetheless, when conditioned by prior expert knowledge and identification of attendant uncertainties, permits risk ranking or separation into descriptive categories of risk. [10]
Quantitative risk assessment A risk assessment that provides numerical expressions of risk and indication of the attendant uncertainties. [10]
Quorum sensing Quorum sensing is a form of communication between bacteria based on the use of signalling molecules that allows bacteria to coordinate their behaviour. The accumulation of signalling molecules in the environment enables a single cell to sense the number of bacteria (cell density). Behavioural responses include adaptation to availability of nutrients, defence against other microorganisms that may compete for the same nutrients, and the avoidance of toxic compounds potentially dangerous for the bacteria. For example, it is very important for pathogenic bacteria during infection of a host (e.g. humans, other animals or plants) to coordinate their virulence in order to escape the immune response of the host in order to be able to establish a successful infection.
Random error Unexplainable but characterizable variations in repeated measurements of a fixed true value resulting from processes that are random or statistically independent of each other, such as imperfections in measurement techniques. Some random errors could be reduced by developing improved techniques.
Refined method This method is intended to provide accurate exposure and risk using appropriately rigorous and scientifically credible methods. The purpose of such methods, models or techniques is to produce an accurate and precise estimate of exposure or risk, or both, consistent with data quality objectives or best practice, or both.
Representativeness The property of a sample (set of observations) that they are characteristic of the system from which they are a sample or which they are intended to represent, and thus appropriate to use as the basis for making inferences. A representative sample is one that is free of unacceptably large bias with respect to a particular data quality objective.
Risk A function of the probability of an adverse health effect and the severity of that effect, consequential to a hazard(s) in food. [1]
Risk analysis A process consisting of three components: risk assessment, risk management and risk communication. [1]
Risk assessment A scientificallybased process consisting of the following steps: (i) hazard identification, (ii) hazard characterization, (iii) exposure assessment, and (iv) risk characterization. [1]
Risk characterization The qualitative and/or quantitative estimation, including attendant uncertainties, of the probability of occurrence and severity of known or potential adverse health effects in a given population based on hazard identification, hazard characterization and exposure assessment (MRA). [1]
Risk communication The interactive exchange of information and opinions throughout the risk analysis process, concerning hazards and risks, riskrelated factors and risk perceptions, among risk assessors, risk managers, consumers, industry, academic community and other interested parties, including the explanation of risk assessment findings and the basis for risk management decisions. [1]
Risk estimate The output of risk characterization. [10]
Risk management The process, distinct from risk assessment, of weighting policy alternatives, in consultation with all interested parties, considering risk assessment and other factors relevant for the health protection of consumers and the promotion of fair trade practices, and, if needed, selecting appropriate prevention and control options. [1]
Sampling distribution A probability distribution for a statistic.
Scenario A construct characterizing the likely pathway affecting the safety of the food product. This may include consideration of processing, inspection, storage, distribution and consumer practices. Probability and severity values are applied to each scenario. [17]
Sensitivity analysis A method used to examine the behaviour of a model by measuring the variation in its outputs resulting from changes in its inputs. [10]
Screening method This method is intended to provide conservative overestimates of exposure and risk using relatively simple and quick calculation methods and with relatively low data input requirements. The purpose of such methods, models or techniques is to eliminate the need for further, more detailed modelling for scenarios that do not cause or contribute to high enough levels of exposure or risk to be of potential concern. If a screening method indicates that levels of exposure or risk are low, then there should be high confidence that actual exposures or risk levels are low. Conversely, if a screening method indicates that estimated exposure or risk levels are high, then a more refined method should be applied since the screening method is intentionally biased. See Refined method.
Statistic A function of a random sample of data (e.g. mean, standard deviation, distribution parameters).
Stochastic uncertainty Also referred to as random error, q.v.
Stochastic variability Sources of heterogeneity of values associated with members of a population that are a fundamental property of a natural system and that in practical terms cannot be modified, stratified or reduced by any intervention. For example, variation in human susceptibility to illness for a given dose for which there is no predictive capability to distinguish the response of a specific individual from that of another. Stochastic variability contributes to overall variability for measures of individual risk and for population risk.
Subjective probability distribution A probability distribution that represents an individual's or group's belief about the range and likelihood of values for a quantity, based upon that person's or group's expert judgement, q.v.
Surrogate data Substitute data or measurements on one quantity used to estimate analogous or corresponding values for another quantity.
Systematic error see bias.
Transparent Characteristics of a process where the rationale, the logic of development, constraints, assumptions, value judgements, limitations and uncertainties of the expressed determination are fully and systematically stated, documented and accessible for review. [10]
Threshold Dose of a substance or exposure concentration below which a stated effect is not observed or expected to occur (disease). [5]
Toxigenic pathogens  see infectious pathogens.
Toxicoinfectious pathogens  see infectious pathogens.
Uncertainty Lack of knowledge regarding the true value of a quantity, such as a specific characteristic (e.g. mean, variance) of a distribution for variability, or regarding the appropriate and adequate inference options to use to structure a model or scenario. These are also referred to as model uncertainty and scenario uncertainty. Lack of knowledge uncertainty can be reduced by obtaining more information through research and data collection, such as through research on mechanisms, larger sample sizes or more representative samples.
Validation Comparison of predictions of a model to independently estimated or observed values of the quantity or quantities being predicted, and quantification of biases in mean prediction and precision of predictions.
Variability Observed differences attributable to true heterogeneity or diversity in a population or exposure parameter. Variability implies real differences among members of that population. For example, different individuals have different intakes and susceptibility. Differences over time for a given individual are referred to as intraindividual variability. Differences over members of a population at a given time are referred to as interindividual variability. Variability in microbial risk assessment cannot be reduced but only more precisely characterized.
 § 
Sources of definitions
[1] Codex Alimentarius Commission. 2001 Procedural manual. Twelfth edition. Rome, Food and Agriculture Organization of the United Nations and World Health Organization.
[2] WHO. 1994. Assessing human health risks of chemicals: derivation of guidance values for healthbased exposure limits. Environmental Health Criteria, No. 170.
[3] Last, J.M. (ed). 1995. A dictionary of epidemiology. 3rd ed. New York, NY: Oxford University Press.
[4] Meynell, G.G., & Stocker, B.A.D. 1957. Some hypotheses on the aetiology of fatal infections in partially resistant hosts and their application to mice challenged with Salmonella paratyphiB or Salmonella typhimurium by intraperitoneal injection. Journal of General Microbiology, 16: 3858.
[5] WHO. 1999. Risk Assessment Terminology: methodological considerations and provisional results. Report on a WHO experiment. Terminology Standardization and Harmonization, vol. 2, nos. 14.
[6] http://www.stats.gla.ac.uk/steps/glossary/sampling.html
[7] http://linkage.rockefeller.edu/wli/glossary/stat.html
[8] http://inside.uidaho.edu/tutorial/gis/engine.asp?term=goodnessoffit
[9] http://www.mcc.uiuc.edu/SummerSchool/David%20Ceperley/dmc_lec3.htm
[10] Codex Alimentarius Commission. 1999. Principles and guidelines for the conduct of microbiological risk assessment. Doc. No. CAC/GL30.
[11] ILSI [International Life Science Institute]. 2000. Revised framework for microbial risk assessment. ILSI, Washington.
[12] Dorland's illustrated medical dictionary. Twentysixth edition. 1981. Philadelphia. W.B. Saunders Company.
[13] Benenson, A.S. (ed). 1995. Control of communicable diseases manual. Sixth edition. Washington DC: American Public Health Association.
[14] Anonymous. 2003. Risk assessment of food borne bacterial pathogens: quantitative methodology relevant for human exposure assessment. Brussels, European Commission, Health & Consumer Protection DirectorateGeneral.
[15]. Gregg, M., Dicker, R.C., & Goodman, R.A. (eds). 1996. Field epidemiology. New York, NY: Oxford Press.
[16] Buchanan, R.L., Smith, J.L., & Long, W. 2000. Microbial risk assessment: doseresponse relations and risk characterization. International Journal of Food Microbiology, 58: 159172.
[17] FAO/WHO. 1995. Application of risk analysis to food standards issues. Report of a joint FAO/WHO expert consultation, Geneva, Switzerland, 1317 March 1995. WHO, Geneva.
[18] Burnham, K.P., & Anderson, D.R. 1998. Model selection and inference. Springer.