Systems and methods for assessing the risk of zoonotic diseases in animal populations

Abstract

This invention provides a system and method for assessing the risk of zoonotic diseases, including Salmonella, in animal populations at production facilities. Users can provide data to multiple data input fields relating at least partially to procedures, operations, and / or equipment at the production facility. Each data input field can be assigned a value to the data entered by the user. The value can correspond to a predetermined value of a predetermined response identified as corresponding to the user-entered data. The assigned value can be adjusted using weighting coefficients, which may correspond to the predicted potential impact of the actions or information reflected by the user-entered data on the prevention of zoonotic diseases. The assigned and adjusted values ​​can be collected to generate a risk assessment score, which can be used to determine a food safety index score and a food safety plan.

Claims

1. A system for evaluating the risk of zoonotic diseases in animal populations at production facilities, wherein the system is One or more databases having multiple data input fields, each including at least one data input field related to procedures, operations, or equipment in the aforementioned production facility, At least one processor, A memory device coupled to at least one processor, wherein the memory device, when executed by the processor, is configured in the system The user of the system is informed of the multiple data input fields. For each of the plurality of data input fields, a signal corresponding to the data entered by the user is received. For each of the plurality of data input fields, a value is assigned to the data entered by the user. The system determines whether or not to generate a weighted value by changing the value of any of the data entered by the user using a weighting coefficient. A risk assessment score is generated using the value or weighted value associated with the data entered for each of the plurality of data input fields. The memory device includes an instruction to perform the action, and the risk assessment score corresponds to the estimation of the risk of the zoonotic disease in the animal population at the production facility. A system equipped with these features.

2. The system according to claim 1, wherein the one or more databases further include a plurality of predetermined responses for each of the plurality of data input fields, and each of the predetermined responses is assigned a predetermined value.

3. When the memory device is executed by the at least one processor, it is used by the system. For each data input field, the data entered by the user is compared with the plurality of predetermined responses. From the comparison, identify the predetermined response among the plurality of predetermined responses that corresponds to the data entered by the user. The command includes an instruction to cause further action, wherein the value assigned to the data entered by the user in the data input field is the predetermined value of the predetermined response identified from the plurality of predetermined responses. The system according to claim 2.

4. The system according to any one of claims 1 to 3, wherein the risk assessment score is a food safety index score, and the memory device includes instructions, when executed by the at least one processor, that cause the system to further identify a risk level from a plurality of risk levels of the food safety index corresponding to the food safety index score.

5. The system according to any one of claims 1 to 4, wherein the one or more databases further include a plurality of mitigation strategies, and the memory device includes instructions, when executed by the at least one processor, that cause the system to further select one or more mitigation strategies from the plurality of mitigation strategies for a food safety plan for the production facility.

6. The system according to claim 5, wherein one or more mitigation strategies are selected using (a) the data entered by the user for the plurality of data input fields, and / or (b) at least one of the risk assessment scores.

7. The system according to any one of claims 1 to 6, wherein the zoonotic disease is Salmonella, and the animal population is a poultry population.

8. The system according to any one of claims 1 to 7, wherein the one or more databases include a data input field database including the plurality of data input fields, a weighting coefficient database including the plurality of weighting coefficients, and a mitigation strategy database.

9. The system according to any one of claims 1 to 8, wherein the weighting coefficient is adjustable in accordance with the data entered by the user for one or more of the plurality of data input fields.

10. The system according to any one of claims 1 to 9, wherein the memory device includes an instruction, when executed by the at least one processor, that causes the system to further select a value for the weighting coefficient from a predetermined range of values.

11. The system according to any one of claims 1 to 10, wherein the memory device includes an instruction, when executed by the at least one processor, causing the system to further receive a signal indicating one or more user input parameters, the one or more user input parameters providing identification of one or more of the following: animal species, production facility type, and specific zoonotic disease.

12. The system according to claim 11, wherein the memory device includes instructions, when executed by the at least one processor, that cause the system to further select the plurality of data input fields from one or more data field query categories based on the identification provided by the one or more user input parameters.

13. One or more neural network databases that receive neural network training data corresponding to multiple characteristics of multiple production facilities and records of the occurrence or absence of zoonotic diseases at each of the multiple production facilities, A neural network configured to analyze neural network training data and improve the accuracy of risk assessment scores generated by the system, for the continuous learning of the neural network based on machine learning, The system according to any one of claims 1 to 12, further comprising:

14. A method for evaluating the risk of zoonotic diseases in animal populations at production facilities, wherein the method is: To communicate to a user a first plurality of data input fields of a first data field query category, wherein the first plurality of data input fields include at least one data input field relating to a procedure, operation, or equipment in the production facility. Receiving a first dataset entered by the user, wherein the first dataset includes data provided by the user for each of the first plurality of data input fields. For each of the first plurality of data input fields, a first value is assigned to the data entered by the user, To determine whether or not to generate a first weighted value by changing the first value of any of the data entered by the user using the first weighting coefficient, To generate a risk assessment score using at least one of the first values ​​or first weighting values ​​associated with the data entered for each of the first plurality of data input fields, wherein the risk assessment score corresponds to an estimate of the risk of zoonotic diseases in the animal population at the production facility. Methods that include...

15. For each of the first plurality of data input fields, the data entered by the user is compared with a plurality of predetermined responses, wherein each of the plurality of predetermined responses is assigned a first predetermined value. From the comparison, identify the predetermined response among the plurality of predetermined responses that corresponds to the data entered by the user, The assignment of the first value further includes assigning the first predetermined value to the predetermined response identified from the plurality of predetermined responses, The method according to claim 14.

16. The method according to claim 14 or 15, wherein the risk assessment score is a food safety index score, and the method further comprises identifying a risk level from a plurality of risk levels of the food safety index corresponding to the food safety index score.

17. The method according to any one of claims 14 to 16, further comprising generating a food safety plan for the production facility based at least in part on the risk assessment score and / or the first dataset, wherein the food safety plan includes one or more mitigation strategies.

18. The method according to claim 17, further comprising selecting one or more mitigation strategies from a plurality of mitigation strategies.

19. The method according to any one of claims 14 to 18, wherein the zoonotic disease is Salmonella and the animal population is a poultry population.

20. The method according to any one of claims 14 to 19, further comprising adjusting the first weighting coefficient in accordance with the information contained in the first dataset.

21. The method according to any one of claims 14 to 20, further comprising selecting a value for the first weighting coefficient from a predetermined range of values.

22. Receiving neural network training data from one or more neural network databases corresponding to multiple characteristics of multiple production facilities, and records of the occurrence or absence of zoonotic diseases at each of the multiple production facilities, To enable continuous learning of a machine learning-based neural network, the training data of the neural network is analyzed to improve the accuracy of the generated risk assessment score, The method according to any one of claims 14 to 21, further comprising:

23. To communicate to the user a second set of data input fields of a second data field query category, wherein the second set of data input fields are different from the first set of data input fields. Receiving a second dataset entered by the user, wherein the second dataset includes data provided by the user for each of the second plurality of data input fields. For each of the second plurality of data input fields, a second value is assigned to the data entered by the user, To determine whether or not to generate a second weighted value by changing the second value of any of the data in the second dataset by the second weighting coefficient, The risk assessment score is at least partially based on the second value or second weighting value associated with the data entered for each of the second plurality of data input fields. The method according to any one of claims 14 to 22.

24. The method according to claim 23, wherein the second weighting coefficient is different from the first weighting coefficient.

25. The method according to any one of claims 14 to 24, further comprising receiving one or more user input parameters that provide identification of one or more of the following: the type of animal, the type of production facility, and a specific zoonotic disease.

26. The method of claim 25, further comprising selecting the first data field query category from a plurality of data field query categories based on the identification provided by the one or more user input parameters.

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