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The WHO and the FAO have listed the various types of data that can be used in hazard identification (FAO and WHO 2020), along with their benefits and drawbacks. Their main conclusions are summed up in Table 1.1.

The data cited by the WHO and FAO can be categorized according to their origin (whether they have been obtained from the scientific literature or from surveillance) and nature (epidemiological studies, prevalence and concentrations, early warning, toxi-infection, public health).

Food safety agencies provide professionals in the agrifood sector with factsheets describing biological hazards transmissible through food. These factsheets are updated regularly and are available online; they can also provide useful information for hazard identification. By way of example:

 – ANSES’ factsheets are available in French at https://www.anses.fr/fr/content/fiches-de-dangers-biologiques-transmissibles-par-les-aliments;

 – Canadian factsheets are available at https://www.canada.ca/fr/sante-publique/services/biosecurite-biosurete-laboratoire/fiches-techniques-sante-securite-agents-pathogenes-evaluation-risques.html.

Table 1.1. The various types of data that can be used in hazard identification (FAO and WHO 2020)

Type of data	Description	Benefits	Drawbacks
Literature data: epidemiological studies	Epidemiological investigations relate to studies that have been commissioned to specifically study the cause and effect relationship between the appearance of foodborne diseases and exposure to certain microbiological risks through the consumption of food.	Epidemiological studies are very specific and provide a vast amount of detailed information about the hazard and the group of consumers studied.	The data are often generated for a relatively small number of consumers and therefore are not representative of larger consumer groups.
Literature data: prevalence and concentration data	Studies identifying the prevalence and count/concentration of target microorganisms at various stages of production/distribution and studies identifying their evolution, such as the effectiveness of a transformation procedure.	These studies are particularly useful for exposure assessment, but can also be used for hazard identification.	The diversity of detection and/or counting methods makes it difficult to compare final estimates. Internationally validated microbiological methods to facilitate this comparison should be preferred.
Surveillance data: early warning systems	A food-safety early warning system allows national authorities to share information on measures taken in response to serious risks detected in relation to food and can thus provide useful information for hazard identification. The European RASFF system is an example of a food-safety early warning system that provides valuable information on product withdrawals and recalls.	An early warning system enables the sharing of data between geographically linked parties in an efficient manner. The data are accessible in a digital format, for example, in the Excel file format in RASFF.	The system is only as good as its least active participant. If one country does not have the resources or the expertise to easily contribute to the data, then the resulting dataset is limited or biased in favor of the other countries in the system. Emerging hazards that are not actively investigated, or that do not need to be reported through a national health system, are less likely to be captured in an early warning system.
Surveillance data: collective food toxi-infection data	Appearance of at least two similar cases of a symptomatology, generally gastrointestinal, the cause of which can be attributed to the same food origin. When an infection occurs, an epidemiological investigation is usually undertaken.	Obtaining very specific details about the food source and its preparation in the context of the outbreak is often possible, because of the emphasis on a single food or meal.	There are limitations to using collective food toxi-infection data in preparing dose–response modeling but not really for hazard identification.
Surveillance data: public health data	Countries and several international organizations compile health statistics on infectious/zoonotic diseases. An example of a public health data compilation is Pulsenet.	The data are often very specific, with fairly detailed descriptions of the food (e.g. type, quantity, composition), the pathogen (reliably identified, often subtyped) and the consumer (e.g. age, sex, state of health).	These data are highly dependent on the sophistication of the surveillance system used to collect the information.The data relate only to a limited range of microbiological hazards and do not (or only slightly) reflect sporadic cases.
Surveillance data: food contamination	Governments often have proactive food and water sampling programs in place to detect microbiological hazards, which can be expressed as the percentage of contaminated samples (the prevalence) and/or as concentration in the food. In addition, government agencies (inspection and control departments or designated laboratories) carry out routine surveillance monitoring. These data can be useful for hazard identification and also for exposure or risk assessment.	These activities generate significant amounts of data, in the form of information both on prevalence and on the level of contamination. To permit optimal evaluation of the data on prevalence and level of contamination, the associated meta data should be provided: year, season, geographical location, country, etc.	It may be that these data are not random or fully representative. They are generated in the framework of official control systems that often take resource limitations into account by targeting foods that are known to be problematic.

In parallel with composing hazard factsheets, ANSES has begun detailed work on source attribution (ANSES 2017a, 2018), which can also be used as a documentary source for hazard identification. Source attribution involves quantifying the relative share of various sources in the burden of infectious diseases transmitted through food (ANSES 2017a). It seeks to allocate the number of cases of infectious diseases transmitted by food to the various sources. Sources include:

 – Reservoirs: any living being (human, animal or plant), soil or combination of these upon which a pathogen is primarily dependent for its survival, multiplication and/or reproduction. The reservoir of a pathogen can therefore be animal (e.g. for Campylobacter or Salmonella), human (e.g. for norovirus or hepatitis A virus), environmental (e.g. for Bacillus cereus) or mixed (e.g. for Listeria monocytogenes or hepatitis E virus).

 – Vehicles: any object or substance which acts as a medium in the transmission of a pathogen from the reservoir to its introduction into a receptive host. In the case of foodborne infections, vehicles are foods contaminated with biohazards (in other words, pathogens or their toxic metabolites). Vehicles can also be environmental (e.g. recreational waters).

A report by ANSES (2018) makes it possible to identify which hazards are linked to which categories of food and/or which sectors. The foods under consideration are milk and dairy products, eggs and egg-based preparations, fishery products, meats, vegetables and composite dishes. For meat, it is possible to narrow it down to the sector level: bovine, porcine, small ruminants, poultry or laying hens. For the fishing industry, a distinction can be made between the fish sector and the mollusk and crustacean sector. This work has been published (Augustin et al. 2020) and its chief findings are set out in the Appendices.

Another rich source of information, which is regularly updated, is the documentary collection of the International Commission on Microbiological Specifications for Foods (ICMSF; https://www.icmsf.org/). The sixth book in this series, entitled Microbial Ecology of Food Commodities, provides a great deal of information on microbial hazards that can be very useful in a hazard analysis. The book is structured into product categories including meat and meat products, milk and dairy products, poultry products, eggs and egg products, fish and fishery products, cereals and cereal products, vegetables and vegetable products, etc.

With regard to microorganisms and their ability to multiply, their resistance to stress, their survival and their inactivation, the information resulting from validated forecasting models can and must be taken into account. In particular, these models make it possible to quickly assess the potential barrier effects. The scientific literature is full of model and application developments. We recommend the reader look first at summary works (McKellar and Lu 2004; Membré and Valdramidis 2016; Valdramidis et al. 2017), starting with Chapter 3 of this book (Exposure Assessment of Microbial Pathogens). In section 1.5, the cook/chill/pH barrier measure against non-proteolytic Clostridium botulinum is presented. It is based on the development of a forecasting model applicable to refrigerated, vacuum-packed foods that have received heat treatment sufficient to give them a shelf life (SL) of four weeks or more, with no added preservatives.

Data that are useful for hazard identification, or at least some of these data and assuming the associated metadata (e.g. year, season, geographical location, country) are accessible, can be used in the other stages of microbial risk assessment. As an example, the work carried out by Membré et al. (2015) in their study on the risk linked to foie gras is presented in the next section.