System and method for identifying a food event, tracking the food product, and assessing risks and costs associated with intervention Number:7,412,461 from the United States Patent and Trademark Office (PTO) owispatent The food safety system and method of the present invention provides a comprehensive consumer risk distribution model, which can be applied to any food item. Additionally, the present invention automatically evaluates consumer risk based on how much contaminated food is at each stage of the food distribution process according to the consumer risk distribution model, allowing for quick and accurate determinations as to the efficacy of a trace recall effort. A further element of the present invention provides expert analysis of data to detect and identify food events from sporadic information. Finally, the real time detection system provides early warning data in order to intercept isolated food contamination events before the contaminated food products reach the consuming public.<H intervention, identifying, System, and, meth, 7412461.html, Patent, pto, 7412461

Title: System and method for identifying a food event, tracking the food product, and assessing risks and costs associated with intervention

Abstract: The food safety system and method of the present invention provides a comprehensive consumer risk distribution model, which can be applied to any food item. Additionally, the present invention automatically evaluates consumer risk based on how much contaminated food is at each stage of the food distribution process according to the consumer risk distribution model, allowing for quick and accurate determinations as to the efficacy of a trace recall effort. A further element of the present invention provides expert analysis of data to detect and identify food events from sporadic information. Finally, the real time detection system provides early warning data in order to intercept isolated food contamination events before the contaminated food products reach the consuming public.

Patent Number: 7,412,461 Issued on 08/12/2008 to Sholl, et al.

Inventors: Sholl; Jeffrey John (Minnetonka, MN), Jaine; Andrew Martin (Rancho Santa Fe, CA), Harlander; Susan Kay (New Brighton, MN)
Assignee: BTSafety LLC. (Eden Prairie, MN)

Appl. No.: 10/946,463

Filed: September 21, 2004

Related U.S. Patent Documents


Application Number	Filing Date	Patent Number	Issue Date
10681581	Oct., 2003	6874000
60417099	Oct., 2002
60469875	May., 2003

Current U.S. Class: 707/104.1 ; 703/11; 706/924

Current International Class: G06F 17/00 (20060101); G06F 7/00 (20060101)

References Cited [Referenced By]

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5478990	December 1995	Montanari et al.
6104966	August 2000	Haagensen
6409082	June 2002	Davis et al.
6671698	December 2003	Pickett et al.
6691135	February 2004	Pickett et al.
6995675	February 2006	Curkendall et al.
7194395	March 2007	Genovese
7249006	July 2007	Lombardo et al.
2003/0009239	January 2003	Lombardo et al.
2003/0018513	January 2003	Hoffman et al.
2003/0069774	April 2003	Hoffman et al.
2003/0074250	April 2003	Burk
2003/0083947	May 2003	Hoffman et al.
2003/0177025	September 2003	Curkendall et al.
2004/0015318	January 2004	Heller et al.
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WO 01/35376	May., 2001	DE
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001489538	Dec., 2004	EP
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WO-01/97555	Dec., 2001	WO
WO-01/97556	Dec., 2001	WO
WO-02/37375	May., 2002	WO
WO 03/069508	Aug., 2003	WO
WO 2007/016937	Feb., 2007	WO

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Primary Examiner: Wassum; Luke S
Attorney, Agent or Firm: Westman, Champlin & Kelly, P.A. Sawicki; Z. Peter

Parent Case Text

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of and claims priority from U.S. patent application Ser. No. 10/681,581, filed on Oct. 8, 2003 and entitled "SYSTEM AND METHOD FOR IDENTIFYING A FOOD EVENT, TRACK THE FOOD PRODUCT, AND ASSESSING RISKS AND COSTS ASSOCIATED WITH INTERVENTION" ("the '581 application"), which is incorporated herein by reference in its entirety. The '581 application claims priority from provisional application Ser. No. 60/417,099 filed on Oct. 8, 2002 and entitled "FOOD SAFETY SYSTEM AND METHOD" and provisional application Ser. No. 60/469,875 filed May 12, 2003 and entitled "SYSTEM AND METHOD FOR IDENTIFYING, TRACING AND RECALLING CONTAMINATED FOOD ITEMS", which are incorporated herein by reference in their entireties.

Claims

The invention claimed is:

1. A system adapted to monitor contamination incidents in a product distribution chain, the system comprising: a product distribution database containing physical distribution data related to a flow of products in commerce; and a modeling system adapted to model an evolving contamination incident according to the physical distribution data in the product distribution database.

2. The system of claim 1 and further comprising: an on-call product tracking system adapted to identify a probable source of a particular contamination incident based on chronological and geographical correlations between identified contamination incidents and the physical distribution data.

3. The system of claim 1 wherein one of the products is a fresh produce item.

4. The system of claim 3 wherein the fresh produce item is selected from a group consisting of potatoes, head lettuce, and onions.

5. The system of claim 1 wherein one of the products is pasteurized milk.

6. The system of claim 1 wherein the product distribution database includes data related to a volume of product at each point within a distribution chain at each hour after the product is produced.

7. A system adapted to monitor contamination incidents in a product distribution chain, the system comprising: a product distribution database containing commercial distribution data related to a flow of products in commerce; and a modeling system adapted to model an evolving contamination incident according to the commercial distribution data in the product database.

8. The system of claim 7 further comprising: a user interface coupled to the product database and the modeling system, the user interface adapted to receive user input for selecting between products in the product database and for selecting parameters associated with the evolving contamination incident.

9. The system of claim 8 wherein the parameters are selected from a group consisting of estimated public health response time, contaminant, food, contaminant point, contaminated product quantity, and time of year.

10. The system of claim 7 further comprising: an on-call product tracking system adapted to identify a probable source of a particular contamination incident based on chronological and geographical correlations between identified contamination incidents and the product database.

11. The system of claim 7 wherein the product database contains temporal and graphical product profile associated with a particular food item.

12. The system of claim 7 further comprising: risk assessment systems adapted to estimate costs and risks associated with a selected intervention based on historical data.

13. The system of claim 7 further comprising: a user interface adapted to allow user input and to display information associated with an evolving model of a particular contamination incident based on the user input.

14. The system of claim 7 further comprising: a trace system adapted to communicate with third party databases to trace products in a stream of commerce and to identify contaminated products within a food distribution chain.

15. The system of claim 14 and further comprising: expert systems adapted to identify a contamination source based on correlations between reported illness outbreaks and food product deliveries.

16. A system adapted to model contamination incidents in a product distribution chain, the system comprising: a product database containing commercial distribution data related to a physical distribution of products in commerce; and a modeling system adapted to model an evolving contamination incident according to the commercial distribution data in the product database and based on selected parameters.

17. The system of claim 16 further comprising: a user interface coupled to the product database and the modeling system and adapted to allow a user to select parameters associated with a particular product and a particular contamination incident.

18. The system of claim 17 wherein the selected parameter comprises a public health response time representative of an estimated speed of response of public health officials to the particular contamination incident.

19. The system of claim 17 wherein the modeling system is adapted to provide intervention options to the user via the user interface and to estimate costs associated with the evolving contamination incident based on the selected intervention options.

20. The system of claim 16 further comprising: a trace recall system adapted to identify distributors of a contaminated product and to issue a recall notice for the contaminated product to identified distributors within a stream of commerce.

Description

BACKGROUND OF THE INVENTION

The present invention relates to bacterial and microbial contamination of food items. More particularly, the present invention relates to a system and method for identifying contaminated food products, tracing the contaminated food products within the food distribution chain, and facilitating actions such as recalling contaminated food items and alerting consumers through various media.

For the purpose of this invention, the term "pollutants" refers to toxins, harmful bacteria (such as e-coli, Coxiella burnetti, botulinum, thermosaccharolyticum, and the like), pathogens, contaminants, organic agents, inorganic agents, radiological agents, radiological agents or any other non-beneficial agents that find their way into food products. The term "harmful" is used herein means deleterious to human health. Such pollutants may be naturally occurring, maybe the result of a contamination event (such as introduction of the food product into a non-sterile environment), or may be the result of tampering with the food products (as when someone tampered with Tylenol brand of acetaminophen capsules in 1982).

Generally, much of the fresh food supply in the United States and around the world is perishable because of its moderate to high water content and because of its nutritious nature. The causes of deterioration in spoilage of food products include the growth of microorganisms (by far the most common cause), contamination (filth, absorption of odors, etc.), normal respiration (plant tissues), loss of water (sprouting), autolysis (especially fish), various chemical reactions such as oxidation, physiological disorders (such as scald of apples, cold shortening of muscle, chilling injury and anaerobic respiration of plant tissues), and mechanical damage (bruising, and the like).

Spoilage of perishable foods can be prevented only by prompt consumption, which often is not possible, or by prompt effective preservation. Effective preservation not only retards spoilage, but also helps reduce the possibility of contamination of the food product. The aim of commercial food preservation is to prevent undesirable changes in the wholesomeness, nutritive value, or sensory quality of food by economical methods which control growth of microorganisms, reduce chemical, physical, and physiological changes of an undesired nature, and obviate contamination.

Currently, preservation of food can be accomplished by chemical, biological, or physical means. Generally chemical preservation involves the addition to food of such substances as sugars, salts, or acids or exposure of food to chemicals such as smoke or fumigants. Biological preservation involves alcoholic or acidic fermentations. Physical approaches to preserving food include temporary increases in the products energy level (heating or irradiation), controlled reduction of the products temperature (chilling, freezing, and the like), controlled reduction in the products water content (concentration, air dehydration, freeze drying), and the use of productive packaging.

During preservation of moderately or highly perishable foods, the greatest concern is related to microorganisms. Physical methods of preservation result either in death of microorganisms (by temporarily increasing the energy level of a food which is suitably packaged to avoid recontamination), or suppression of their growth (by maintaining the food at sub-ambient temperatures or by removing water followed by packaging to avoid reabsorption of water).

Although certain physical methods of food preservation completely stop growth of microorganisms and greatly retard the rates of chemical reactions (and spoilage), it is important to recognize that none of these methods can completely prevent chemical and physical changes. For example, in frozen foods stored at a recommended temperature of -18 degrees Celsius, microorganisms cannot grow, but degradation of vitamin C, insolubilization of protein, oxidation of lipids, and recrystallization can occur at significant rates. Additionally, methods of preservation that successfully stop the growth of microorganisms sometimes have undesirable consequences with respect to the sensory and nutritional attributes of food. For example, thermal sterilization softens food tissues, degrades chlorophyls and anthocyanins alters flavors, and results in loss or degradation of vitamins.

One method of preservation of food products is called pasteurization. Pasteurization is a heat treatment that kills part but not all of the vegetable microorganisms present in the food, and consequently it is used for foods which are to be further handled and stored under conditions which minimize microbial growth. In many cases, the primary objective of pasteurization is to kill pathogenic microorganisms. Some vegetative spoilage organisms can survive this heat treatment, and thus more severe preservation methods are needed if microbial spoilage is to be prevented. In other cases, such as in beer, pasteurization serves primarily to kill vegetative spoilage organisms. Other preservation techniques used in conjunction with pasteurization typically include refrigeration, chemical additives, packaging, and fermentation.

Pasteurization generally involves heating the food product to a specific temperature for a period of time. The time temperature treatment used in pasteurization depends upon the heat resistance of the particular vegetative or pathogenic microorganism that the process is designed to destroy, and the sensitivity of the product quality to heat. In milk pasteurization for example, the high temperature and short time method involves a comparatively high temperature for a short period of time (e.g., 161 degrees Fahrenheit for 15 seconds for milk), whereas the low temperature and long time procedure involves relatively low temperatures for longer periods of time (e.g., 145 degrees Fahrenheit for 30 minutes for milk). Optimization of the pasteurization process depends on the relative destruction rate of various microorganisms as compared to quality factors of the food product. For market milk, pasteurization conditions are based on the thermal destruction of coxiella burnetti, the ricketsia organism responsible for Q fever. For high acid fruits such as cherries, the pasteurization process is based on successful destruction of yeast or molds. For fermented beverages such as wine or beer, the pasteurization criteria involves the destruction of wild yeasts.

In milk for example, the low temperature long time pasteurization process is targeted toward a particular organism. However, even with such pasteurization, contamination by other pollutants may occur from time to time. Additionally, storage conditions may contaminate the stored milk or provide an ambient condition for the microorganisms to reconstitute. Consequently, contamination of food products by pollutants occurs from time to time. Typically such occurrences are evidenced by sporadic outbreaks of illness among consumers and by occasional recall efforts. Whether the contamination is caused by E coli in tainted ground beef, by ricin in potatoes, or by various other pollutants on various types of food products, it is desirable to identify food contamination events quickly, and to take steps to contain the spread of the contamination so that the consumer impact is minimized.

Generally, once a food contamination is identified, food producers have few options. The food producers can recall all of the food items, assess the risk of not recalling the contaminated items against the costs associated with the recall effort, publically announce the contamination through media outlets, and destroy remaining produce. Typically, food producers employ one or more of these option for each food contamination event. Wrong decisions not only cost money, but may also cost lives (particularly if a recall effort is not mounted quickly).

Unfortunately, it has been found that public announcements of food contamination events are generally not very effective in reaching consumers. Additionally, since food producers are independent, there is no centralized or nation wide system for handling food contamination events. In fact, at present there is no standard method for addressing food contamination events.

Additionally, before remediating the food contamination event through one of the options described above, it is important that the source of the contamination is accurately identified. A misidentification of source can be very costly to food producers and may allow more time for the contaminated food items to circulate and to be consumed before the correct identification is made. Additionally, due to concerns about competition, food processing companies are reluctant to share information about distributors, harvesters and the like. This makes it very difficulty for public health officials to trace food contamination events to the source. Thus, even when the cause of an illness is properly identified by public health officials, reaching the affected consumers, distributors and other people in the food distribution chain can be extremely difficult.

BRIEF SUMMARY OF THE INVENTION

The food safety system and method of the present invention provides a comprehensive consumer risk distribution model, which can be applied to any food item. Additionally, the present invention automatically evaluates consumer risk based on how much contaminated food is at each stage of the food distribution process according to the consumer risk distribution model, allowing for quick and accurate determinations as to the efficacy of a trace recall effort. A further element of the present invention provides expert analysis of data to detect and identify food events from sporadic information. Finally, the real time detection system provides early warning data in order to intercept isolated food contamination events before the contaminated food products reach the consuming public.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the system of the present invention.

FIG. 2 is an expanded block diagram of the components of the system of FIG. 1.

FIG. 3 is a graphical representation of a food incident profile according to the present invention.

FIG. 4 is a flow diagram of the set up process of the present invention.

FIG. 5 is a flow diagram of the basic operation of the system of the present invention during analysis of a food contamination incident.

FIG. 6 is a flow diagram of a user interaction for setting up a food incident model.

FIG. 7 is a flow diagram of a user interaction with the intervention component of the present invention.

FIG. 8 is a screen shot of a first screen of the user interface of an embodiment of the present invention.

FIG. 9 is a screen shot of the user interface for an Unintentional Food Incident set up screen of the modeling system.

FIG. 10 is a screen shot of the user interface for an Intentional Food Incident set up screen of the modeling system.

FIGS. 11-29 are screen shots of the food incident profile tab at various stages within a model of a food contamination incident, modeling how an outbreak might progress over time.

FIG. 30 is a screen shot of the consumption profile information displayed when the "consumption profile" tab is selected by a user, showing the number of times per year lettuce is consumed per capita.

FIG. 31 is a screen shot of the consumption profile information displayed when the "consumption profile" tab is selected by a user and when the "percentage of lettuce consumed by age group" sub category is selected.

While the above-identified illustrations set forth preferred embodiments, other embodiments of the present invention are also contemplated, some of which are noted in the discussion. In all cases, this disclosure presents the illustrated embodiments of the present invention by way of representation and not limitation. Numerous other minor modifications and embodiments can be devised by those skilled in the art which fall within the scope and spirit of the principles of this invention.

DETAILED DESCRIPTION

Fresh food products have a very short usable life-cycle from packaging to spoilage. Typically, fresh foods become unusable in a matter of days or weeks. By contrast, pharmaceuticals, canned goods and other consumables typically enjoy a much longer shelf life, and the time from packaging to consumption of those goods may be many months.

Because of this short "life-cycle", fresh foods are packaged, sold, and consumed more rapidly than most other products. The rapidity of the consumption of fresh foods makes it difficult to identify, trace and recall food products before people are affected. Specifically, it takes several days for infected consumers to seek medical attention for contaminated food-related health problems. During that time, contaminated food has been purchased and consumed by many other individuals. By the time the contaminant is identified and the contaminated food is traced to the source, it is often times too late to effectively recall a product, in part, because the vast majority of the contaminated product has already been consumed or thrown away.

The present invention is a system and method for responding to a food contamination incident. Specifically, the present system combines product distribution profiles for each individual food product with food consumption and demographic data and with contaminant profiles. The system is used to effectively model a food contamination event by tracing food production from harvest through consumption in order to accurately estimate the consumer exposure to the food event. The system models consumer illness reporting and public health response times associated with food contamination event, either based on user input information or available historical data. The system provides context specific remediation options (such as public announcements, trace recall, and other interventions) and evaluates related costs directly attributable to the food event and to selected interventions. Finally, the system interfaces with trade associations, fresh food producers, and other players in the food distribution chain in order to trace contaminated produce backward and forward for effective containment and recall efforts. In essence, the system provides a "one-stop" user interface for tracking, assessing, and remediating food contamination events.

While the system and method discussed herein is directed to an overall strategy for food tracking, for simplicity, the majority of the discussion is directed to an embodiment of the invention, including screen shots from within a particular software implementation. The present invention is intended to be used by food suppliers, for the purpose of effective and quantifiably justified remediation decisions, as well as by public health officials for the purpose of determining the efficacy of particular intervention strategies, when a food contamination event has been detected.

Generally, the present invention serves as an analytical predictive modeling tool, coupled with the data required to support its predictive abilities. The modeling tool is designed to facilitate a qualitative analysis of product contamination events, based on seasonal food distribution profiles, statistical data, and collected demographic information. In particular, the present tool allows for iterative predictive modeling of probable outcomes and costs associated with different control and intervention approaches to food contamination events. The modeling tool accommodates the incorporation of a variety of assumptions about the nature of the threat and the effectiveness of the control and intervention strategies.

Specifically, the analytical predictive modeling tool is capable of generating hypothetical permutations to food contamination events. The modeling tool can project outcomes and their probabilities in terms of the likely distribution of human illness or death and in terms of economic consequences, based on assumptions about the underlying food contamination event, even before the source and nature of the contamination is known. Finally, the analytical tool incorporates different methods, time and types of intervention, depending on the particular point or points in the food production and distribution chain at which the intervention is applied.

FIG. 1 shows a block diagram of the system 10 of the present invention. The system 10 includes an analytical and predictive modeling tool (APMT) 12 and database(s) 14 for storing the data necessary for predictive modeling, which are accessible by the APMT 12. The APMT 12 may be connected to a network 16. The network 16 can be any type of network, including a local area network, a wide-area network, a telephone network, the Internet, or any other type of network (wired or wireless). The APMT 12 can be accessed by one or more authorized users 18 over the network 16. Finally, the APMT 12 can interact with third party data 20 and other third party systems 22, either over the network 16 or via direct connections (shown in phantom) in order to supplement the capabilities of the APMT 12.

In particular, to the extend that third parties and/or food distributors and producers maintain data related to food consumption, food distribution, health data or other relevant information, the APMT 12 can interact with their systems. The APMT 12 can query third party databases 20 or interact with third party systems 22 via direct connections, virtual private network (VPN) connections, or any secure connection means or directly such as via a direct modem connection.

As shown, a user 18 can interact with the tool 12 over the network 16 in order to perform a qualitative analysis of product contamination events, based on food distribution profiles, statistical data, and collected demographic information stored in the databases 14. In particular, the tool 12 allows for iterative predictive modeling of probable outcomes and costs associated with different control and intervention approaches to food contamination events. The tool 12 accommodates the incorporation of a variety of assumptions about the nature of the threat and the effectiveness of the control and intervention strategies via a user interface (discussed with respect to later figures).

Specifically, the tool 12 is capable of generating hypothetical permutations to food contamination events. The tool 12 can project outcomes and their probabilities in terms of the likely distribution of human illness or death and in terms of economic consequences, based on assumptions about the underlying food contamination event, even before the source and nature of the contamination is known. Finally, the tool 12 incorporates different methods, time and types of interventions, depending on the particular state in the food production and distribution chain at which the intervention is applied.

In general, by interfacing with existing data systems and by providing a simple and accessible user interface, the system 10 provides a framework based on a range of simple principles for facilitating the smooth and efficient transfer of information relating to each stage of the food chain. More importantly, the system 10 provides a targeted and statistically verifiable model of food as it passes through the food distribution network to the consumer. The system 10 provides a practical framework for evaluating an evolving food event, for tracing contaminated food throughout the distribution chain, and for evaluating the human and economic costs of various intervention strategies.

FIG. 2 illustrates a block diagram of various elements of the system 10. As shown, the system 10 includes a contamination event tracking system 24, risk assessment system 26, contamination event response system 28, trace recall systems 30, and (optionally) early contamination detection systems 32. The contamination event tracking system 24, the risk assessment system 26 and the contamination event response system 28 utilize data stored in multiple databases, each of which may be multidimensional databases. As shown, the APMT 12 interacts with the food distribution profiles database 14A, the food consumption profiles database 14B, the historical food event profile database 14C, and a contaminant profile database 14D. Additionally, the trace recall systems 30 interact with multiple third party databases 22, such as various trade associations and/or distributor databases for the purpose of tracing contaminated food products within the distribution system.

Optionally, the system 10 may incorporate an early contamination detection system 32, which interacts with hospitals and public health officials located in areas at or near agricultural areas. For example, since a large percentage of lettuce produce is harvested from a small geographic area, health officials and hospitals in those areas can be monitored and/or plugged into the system 10 so that high incidence of illness from field workers and/or packing plant employees can provide a red flag for potential contamination. Alternatively, if such systems are established, the tool 12 can make use of such systems to fine tune its internal metrics for modeling food events and to triangulate against available food tracking information to determine likely sources of illness.

A user interface 34 is provided to allow one or more authorized users to access the tool 12 in order to model various events and/or to strategize as to how to respond to an evolving event. The user interface 34 may be a standalone program, a client run-time, a web interface, or any other user friendly interface. In a preferred embodiment, the interface 34 is a web-based user interface, which allows an authorized user to access the system 10 using any security enabled web browser, either over an internal network, a wireless network, or the Internet.

In general, each database 14A, 14B, 14C and 14D stores vectors of information. As previously mentioned, each database may be multidimensional, meaning that a data fact is viewed as a mapping from a point in a space of dimensions into one or more spaces of measures. For example, within the food distribution profiles database, there are a number of different kinds of measures, such as number of heads of lettuce harvested, number of heads of lettuce sold, number of heads of lettuce consumed, and the like. Each of these can be analyzed in terms of dimensions. Number of heads of lettuce sold, for example, can be analyzed in terms of customer type, distributor, volume per sale, date of sale, geographic region, and the like. Dimensions can be further organized into hierarchical levels, such that the geographic region might be part of a larger region, and so on.

Data for the food distribution profiles database 14A is derived by tracking food from harvest to consumption for each specific food product over a period of years. As shown in FIG. 3, the compiled data can be illustrated as a series of layered graphs.

Generally, the Food Incident Profiles 12 represent a day-by-day statistical analysis of the quantity of product at each identified stage of the distribution chain for each product. In other words, lettuce, strawberries, corn, and potatoes each have their own distribution profiles. Depending on the particular product and the statistical variance in the distribution data for each product, the profile for any particular product may be relatively static over long periods of time or may vary with the season. Each product can be handled differently.

A food distribution profile can be developed for each fresh produce item. Then, the distribution profile can be coupled with profiles for various harmful pollutants and naturally occurring pathogens to form a food incident profile. In general, the food distribution profile is produced for any food item by collecting and cataloging distribution data for the food item over a period of time. The system was developed for fresh produce, but can be applied to virtually any food product and any contamination agent.

In general, the food distribution profiles illustrate how broadly fresh produce items are distributed across the country and the velocity at which the selected items move through the distribution system. However, the profiles are utilized by the APMT 12 to model the extent of consumer exposure to a food incident, to locate product within the distribution chain, to identify where to direct containment efforts, and to determine the likely efficacy of available intervention measures.

Generally, each profile is created by tracking the movement of a specific product temporally and geographically through each link in the distribution chain. Over time, the profiles become increasingly accurate and may be used to track product distribution.

As shown in FIG. 3, the food distribution profile information and the Agent/contaminant profile information can be combined to provide a graphical illustration of the progress of contaminated food product from harvest to mortality. In particular, the y-axis of the graph represents a statistical distribution of the percentage of product and/or percentage of infected consumers. The y-axis intersects the x-axis of the graph at day zero (0), which represents the day the food product is harvested, in the case of fresh produce for example. At day 1, the harvested and contaminated food product begins to arrive at retail locations. The contaminated food product (in this instance) typically arrives at the retail outlets within a day or two of harvest, and may remain on the shelves at the retail location for 1 to 10 days.

Once the contaminated item reaches retail shelves, it begins to be sold and taken to consumer's homes. Beginning at 1 and a half days (1.5 days), consumers begin purchasing the food product and taking it home. Typically, consumers can store such produce from half a day to a week or so before consuming the product. As shown, the time at the consumer's home may be half a day to six days (1.5 to 16 days from the day the food product was harvested). The profile shows that the food was consumed between half a day and 10 days after purchase (meaning 2 days to 26 days after harvest).

After the food is consumed, it may take several days for symptoms to begin to appear. After symptoms appear, it may take several more days for a consumer to seek medical attention. In extreme circumstances, consumers may die from contaminated food, and it can be several days after seeking medical attention before the consumer dies (7 days to 39 days after harvest).

In general, the food profiles are based on product-specific information. For Lettuce for example, the time from harvest to processing at the distribution center is approximately two hours, based on distance evaluations and trucker interviews. Between time in the distribution center and time in shipping, all of the harvested lettuce reaches the stores within about thirty-six hours. Retailers estimate that received products are placed on the shelves within twenty-four hours of receipt, and the products are typically sold within one to three days of its arrival at the store.

At this point, it is estimated that the first onset of symptoms from illness caused by contaminated lettuce would be noticed within forty-eight hours. Since most people do not immediately attribute illness to food items, medical literature suggests that most infected individuals will seek medical attention within an additional seventy-two hours. Identification of the illness and its potential source may take some time, but would probably occur within ninety-six hours, and the decision whether to recall the product would be made at that time.

Generally, the development of each food profile requires a source profile (detailed breakdown of tonnage of production at all geographic locations at various times of the year). The food profile also requires a distribution profile (location and quantities of products at each identified stage of the distribution chain from harvest through purchase), and a consumption profile (product storage and usage by consumers). As previously mentioned, this information is derived from interviewing producers, truckers, and others.

The contaminant/agent profile requires a clinical disease progression profile (pollutant/agent specific disease symptoms, progression and outcomes), and public health response profiles (public health response times--best, most likely, and worst case scenarios). Both the progression profile and the public health profile can be derived from existing public health data.

In many cases, the food profiles 12 depend on several factors in addition to the product type (for example, iced vs. non-iced green onions). These additional factors, termed "discriminants" are identified as a part of the profile development for each food item, and a profile is built for each combination of the selected food type with the other external discriminants that have a significant effect on the profile.

The Profile for a specific food product will vary with changes in some external factors (the "discriminants"). For some products, seasonality will likely be one such discriminant--for example, it seems logical that the profile of lettuce out of Florida would look somewhat different from the profile of lettuce out of Salinas.

It is believed that there will be key discriminants for each food item, but the relative importance of each discriminant will be determined over time as the profile data for each item improves. If changing the value of a discriminant has a significant effect on a profile, then different profiles will be developed for each such value.

While growing seasons (and therefore particular growers) may vary for each food item, it is believed that competitive pressure tends to force a given product at a given time of year to move at "roughly" the same speed through all distribution channels (where "roughly" means within the limits of accuracy of the profiles).

The food incident profile of FIG. 3 can be used to illustrate the rate at which a food contamination incident is expected to evolve for a particular combination of pollutant or harmful agent for each type of food. Each profile begins with a distribution profile of the movement of the food product from the farm through all stages of production and distribution and through consumption. The profile also contains an agent-specific disease progression profile illustrating the rate at which disease symptoms resulting from the consumption of contaminated food items would be expected to be seen in affected populations. Finally, the profile contains public health response profiles illustrating the likely response by the public health system once affected consumers seek medical attention.

As shown in FIG. 4, set up for the system 10 requires compilation of food distribution information and contamination profile information. As shown, the set up requires that the system operator compile food distribution profiles (step 36). Then, the system operator stores the food distribution profiles in a database (step 38). The system operator compiles and stores contaminant/agent profile information in a contaminant database (step 40). Finally, the system operator makes the databases accessible to the system 10 (step 42) for use in analytical and predictive modeling processes.

While the general process is disclosed as being system operator driven, compilation and storage of the food and pathogen profiles can be automated. Alternatively, the data entry and storage can be performed by a third party, and the databases can be made accessible to the system 10.

FIG. 5 is a flow diagram of the operation of an embodiment of the system 10. The user identifies a food contamination event (step 44) and selects various parameters. The system 10 then performs a risk assessment of the contamination event based on market tracking and selected (or known) contaminant information (step 46). As the risk assessment progresses, if the user chooses to intervene in the food contamination event, the system 10 provides the user with intervention options depending on the available data (step 48). If the user selects an intervention option, the system performs an economic analysis based on a chosen intervention strategy (step 50). Finally, the user can instruct the system 10 to act on a selected intervention strategy (to intervene) in the contamination event based on the available information, the associated risk assessment and the economic analysis (step 52). Specifically, the system 10 may be configured to initiate selected interventions, such as notifying news outlets, initiating a recall, and the like.

FIG. 6 is a flow diagram of an embodiment of the user interface. First, the system 10 presents the user with a window for selecting a type of contaminant. The user selects type of contamination (step 54). Then, the system 10 presents the user with a window for selecting the particular contaminant or pollutant, the food type, the contamination point, the product quantity, the season and the estimated response time. The user selects the contaminant, food type, contamination point, product quantity, season, and response time estimate (step 56). Finally, the user initiates the modeling process, and the system 10 models the food incident based on the user-selected parameters (step 58).

FIG. 7 is a flow diagram of an embodiment of the operation of the system 10 with respect to intervention options. Once the APMT 12 begins modeling a food incident, the user can choose to view available intervention options, based on information related to the food event. When the user chooses to view intervention options (such as by clicking a button within the APMT 12 software, the system 10 provides one or more intervention options depending on the user-selected parameters (step 60). If the user chooses to intervene (step 62), the user selects from the one or more available intervention options (step 64), and when the user is finished, the system continues modeling the food contamination incident (step 68) based on the selected intervention option(s). Alternatively, if the user chooses not to intervene (step 62), the system returns the user to the main user interface (step 66) and continues modeling the food conta