Proactive Simulation for Foodborne Outbreak Source Identification
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Solution Overview
Problem
Current methods for identifying the source of foodborne disease outbreaks are time-consuming, often taking days to weeks, leading to increased medical and economic losses, as they rely on reconstructing food distribution networks, which can delay the confirmation of contaminated products.
Innovation Solution
A proactive computer simulation system that uses geographic and temporal data analysis to predict the likelihood of product failures, creating probability density maps and simulating outbreaks to determine the number of incidents necessary to identify contaminated products with a predetermined certainty, thereby accelerating the investigation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods of reconstructing food distribution networks are used to identify the source of foodborne disease outbreaks, then the investigation process is thorough and systematic, but the time required increases to days or weeks, leading to increased medical and economic losses
Solution Approach 1:
The system performs preliminary actions by proactively simulating potential outbreak scenarios and pre-calculating the number of incidents needed to identify contaminated products before actual outbreaks occur. This allows the system to have detection thresholds and identification criteria ready in advance, eliminating the need for time-consuming reconstruction of distribution networks when outbreaks actually happen.
Solution Approach 2:
The system creates simulated copies of potential outbreak scenarios by modeling various failure modes and contamination pathways. These simulations replicate the complex distribution network analysis that would otherwise be required during actual investigations, but can be performed and stored in advance, enabling rapid comparison with real-world incident data.
2Reliability
If the number of incidents required for identification is set to a high predetermined certainty level, then the confidence in identifying the contaminated product increases, but the time to reach that certainty and the number of incidents needed increases
Solution Approach 1:
The system proactively determines and stores the number of incidents necessary to identify specific products to predetermined certainty levels before outbreaks occur. This pre-calculated information allows rapid comparison with actual incident counts, enabling quick identification decisions without waiting to accumulate sufficient incidents during an active outbreak.
Solution Approach 2:
The system allows flexible adjustment of the predetermined certainty level parameter. By changing this parameter, users can balance between requiring higher certainty (more incidents) versus faster identification (fewer incidents). The system pre-calculates the incident thresholds for different certainty levels, enabling rapid selection of the appropriate threshold based on the specific outbreak context and risk tolerance.
3Measurement precision
If comprehensive geographic and temporal data analysis is performed to create probability density maps, then the accuracy of predicting product consumption locations improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system uses simulated incident locations derived from probability density maps as a simplified representation of complex consumption patterns. Instead of tracking every individual product movement through the distribution network, the system generates synthetic incident data that copies the statistical characteristics of real consumption patterns, enabling efficient comparison with actual outbreak data without requiring complex real-time tracking infrastructure.
Data Source
AI summary
Embodiments of the present invention relate to proactive computer simulation of portable product failures, and more specifically, to determining the likely cause of an outbreak of foodborne disease or other geographically distributed symptoms of a failure or contamination of a portable product. In one embodiment, a method of and computer program product for simulating portable product failures is provided. Data regarding the locations of consumers of a portable product within a geographic region is received from a data store. A probability density map is determined from the data, indicating where the portable product is likely to be consumed within the geographical region. For each of a plurality of simulated failures of the portable product, the locations of a plurality of simulated incidents arising from the simulated failure are determined. From the locations of the plurality of simulated incidents and the probability density map, the number of incidents necessary to identify the portable product to a predetermined certainty is determined.