Chicken avian leukosis purification detection method based on multi-stage dynamic threshold

Abstract

The invention discloses a chicken avian leukosis purification detection method based on a multi-stage dynamic threshold, and relates to the technical field of poultry farming, and the key points of the technical scheme are as follows: dividing detection stages, and configuring a reference detection interval; determining a reference positive threshold value of the next detection stage according to the virus load detection distribution result of the previous detection stage; dynamically adjusting the detection time interval of the next detection in combination with the environmental virus activity index, the detection cost and / or the accumulated purification rate; adjusting the dynamic positive threshold value of the next detection by combining the detection time interval and the virus load detection result of the previous detection; and purifying the target chicken exceeding the dynamic positive threshold. When the virus activity is reduced or the accumulative purification rate reaches the standard, the detection time interval is prolonged, the number of invalid detection times is reduced, the detection cost is remarkably reduced, and the balance of the detection sensitivity and stability is realized by fusing a dynamic adjustment mechanism of the environment virus activity index and the accumulative purification rate.

Description

Technical Field

[0001] The present invention relates to the technical field of poultry breeding, and more specifically, it relates to a purification detection method for avian leukosis in chickens based on multi-stage dynamic thresholds. Background Art

[0002] Avian leukosis is an immunosuppressive disease in poultry caused by avian leukosis virus (ALV), which can lead to a decline in the production performance of chicken flocks and tumorous deaths, causing significant economic losses to the global poultry industry every year. This virus can spread rapidly among chicken flocks through vertical and horizontal transmission. Currently, there are no effective vaccines or specific therapeutic drugs, and source purification has become the core means of preventing and controlling this disease.

[0003] It is recorded in the prior art that continuous generation antigen detection and screening are used. Although zero detection can be achieved, the purification cycle is more than 2 years long, seriously affecting the breeding process. In addition, it is also recorded in the prior art that the risk of vertical transmission is reduced by diluting the viral load in semen, but it is impossible to eradicate the intermittent virus excretion caused by latent infection. In addition, the prior art also records the use of a fixed-cycle detection mode, which fails to consider the fluctuations in virus activity and the risk of environmental transmission, resulting in coexistence of waste of detection resources and the risk of missed detection. The above-mentioned prior arts generally have defects such as imbalance between detection sensitivity and efficiency and poor environmental adaptability, and it is difficult to meet the requirements of modern intensive farming for rapid and accurate purification.

[0004] Therefore, how to research and design a purification detection method for avian leukosis in chickens based on multi-stage dynamic thresholds that can overcome the above defects is an urgent problem for us to solve at present. Summary of the Invention

[0005] To solve the deficiencies in the prior art, the purpose of the present invention is to provide a purification detection method for avian leukosis in chickens based on multi-stage dynamic thresholds. By dynamically adjusting the detection time interval and the dynamic positive threshold, the detection time interval is extended when the virus activity decreases or the cumulative purification rate reaches the standard, reducing the number of ineffective detections, significantly reducing the detection cost, and achieving a balance between detection sensitivity and stability through a dynamic adjustment mechanism that integrates the environmental virus activity index and the cumulative purification rate.

[0006] The above technical purpose of the present invention is achieved through the following technical solutions: A purification detection method for avian leukosis in chickens based on multi-stage dynamic thresholds, comprising the following steps:

[0007] Divide the growth cycle of chickens into five detection stages: egg stage, chick rearing stage, growth stage, laying stage, and culling stage, and configure a corresponding reference detection interval for each of the detection stages;

[0008] Determine the reference positive threshold for the next detection stage according to the virus load detection distribution result of the previous detection stage;

[0009] Based on the benchmark detection interval, dynamically adjust the detection time interval of the next detection in combination with the environmental virus activity index, detection cost, and / or cumulative purification rate;

[0010] Based on the benchmark positive threshold, adjust the dynamic positive threshold of the next detection in combination with the detection time interval and the virus load detection result of the previous detection;

[0011] Adopt the sample type corresponding to the detection stage to detect the virus load detection result of the target chicken in real time, and perform purification treatment on the target chicken whose virus load detection result detected in real time exceeds the corresponding dynamic positive threshold.

[0012] Further, determining the benchmark positive threshold of the next detection stage according to the virus load detection distribution result of the previous detection stage includes:

[0013] The benchmark positive threshold of the next detection stage is the first threshold, the second threshold, or the maximum value of the first threshold and the second threshold;

[0014] Wherein, the first threshold is:

[0015] Extract the first virus load distribution quantile corresponding to the virus load detection result of each detection in the virus load detection distribution result, and the first virus load distribution quantile reflects the central tendency of the virus load;

[0016] Construct a quantile time series curve based on multiple consecutive first virus load distribution quantiles, and perform linear regression analysis on the quantile time series curve to obtain a linear regression slope;

[0017] Determine the first threshold by multiplying the benchmark positive threshold of the previous detection stage by the historical trend correction parameter, and the historical trend correction parameter is determined by the linear regression slope;

[0018] And, the second threshold is:

[0019] Extract the second virus load distribution quantile in the virus load detection distribution result, and the second virus load distribution quantile captures high-risk tail data;

[0020] Determine the second threshold by multiplying the second virus load distribution quantile by the environmental risk correction parameter, and the environmental risk correction parameter is determined by the environmental virus activity index of the previous detection stage.

[0021] Further, the expression for determining the historical trend correction parameter by the linear regression slope is:

[0022] ;

[0023] Among them, represents the historical trend correction parameter; represents the sign function; represents the linear regression slope of the first virus load distribution quantile.

[0024] Furthermore, the expression for determining the environmental risk correction parameter from the environmental virus activity index in the previous detection stage is:

[0025] ;

[0026] Among them, represents the environmental risk correction parameter; represents the environmental response sensitivity coefficient; represents the mean value of the environmental virus activity index in the previous detection stage; represents the environmental virus activity critical value; represents the peak value of the environmental virus activity index in the previous detection stage.

[0027] Furthermore, based on the benchmark detection interval, combining the environmental virus activity index and the detection cost, dynamically adjust the detection time interval for the next detection. The specific expression is:

[0028] ;

[0029] Among them, represents the detection time interval for the next detection; represents the benchmark detection interval for the current detection stage; represents the environmental virus activity index of the previous detection; represents the environmental virus activity critical value; represents the virus activity response coefficient; represents the first cost adjustment parameter; represents the benchmark detection cost; represents the real-time detection cost.

[0030] Furthermore, based on the benchmark detection interval, combining the environmental virus activity index and the cumulative purification rate, dynamically adjust the detection time interval for the next detection. The specific expression is:

[0031] ;

[0032] Among them, represents the detection time interval for the next detection; represents the benchmark detection interval for the current detection stage; represents the environmental virus activity index of the previous detection; represents the environmental virus activity critical value; represents the cumulative purification rate; represents the purification threshold, with a value greater than 0.5 and less than 1; represents the first purification adjustment parameter; represents the virus adjustment parameter.

[0033] Furthermore, based on the reference detection interval, combining the detection cost and the cumulative purification rate, dynamically adjust the detection time interval for the next detection. The specific expression is:

[0034] ;

[0035] where, represents the detection time interval for the next detection; represents the reference detection interval for the current detection stage; represents the reference detection cost; represents the real-time detection cost; represents the cumulative purification rate; represents the second cost adjustment parameter; represents the second purification adjustment parameter.

[0036] Furthermore, based on the reference positive threshold, combining the detection time interval and the virus load detection result of the previous detection, adjust the dynamic positive threshold for the next detection. The specific expression is:

[0037] ;

[0038] where, represents the dynamic positive threshold for the next detection; represents the reference positive threshold for the detection stage to which the next detection belongs; represents the reference detection interval for the current detection stage; represents the detection time interval for the next detection; represents the time sensitivity coefficient; represents the virus activity penalty coefficient; represents the environmental virus activity critical value; represents the peak value of the environmental virus activity index within the previous detection stage.

[0039] Furthermore, the reference detection interval for the breeding egg stage is 3 - 5 days;

[0040] The reference detection interval for the brooding stage is 7 - 10 days;

[0041] The reference detection interval for the growth stage is 14 - 21 days;

[0042] The reference detection interval for the laying period is 21 - 28 days;

[0043] Moreover, the reference detection interval during the elimination period is 10 - 14 days.

[0044] Furthermore, the sample type during the hatching egg period is meconium or eggshell membrane;

[0045] The sample type during the brooding period is cloacal swab;

[0046] The sample type during the growing period is serum;

[0047] The sample type during the laying period is egg white or semen;

[0048] Moreover, the sample type during the elimination period is semen or cloacal tissue.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. The method for purifying and detecting avian leukosis in chickens based on multi-stage dynamic thresholds provided by the present invention can dynamically adjust the detection time interval and dynamic positive threshold, extend the detection time interval when the virus activity decreases or the cumulative purification rate reaches the standard, reduce the number of ineffective detections, and significantly reduce the detection cost. Especially during the laying period, the detection time interval of the experimental group is gradually adjusted from 26 days to 35 days (exceeding the reference value), while the control group maintains a fixed detection interval of 28 days, resulting in an increase in the number of detections and a waste of resources;

[0051] 2. The present invention realizes the balance between detection sensitivity and stability by integrating the dynamic adjustment mechanism of environmental virus activity index and cumulative purification rate; due to the fixed interval and static threshold of the control group, it cannot adaptively adjust when the virus load fluctuates, resulting in unstable purification effects;

[0052] 3. The present invention achieves better final inspection results with fewer purification times, indicating that its method has significant advantages in accurately identifying infected individuals at an early stage and blocking the transmission chain, while reducing the false killing rate and ensuring the economic benefits of farming. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0054] Figure 1 is a flowchart in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the embodiments and the drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and do not limit the present invention.

[0056] Example: A purification detection method for avian leukosis in chickens based on multi-stage dynamic thresholds, as Figure 1 shown, includes the following steps:

[0057] S1: Divide the chicken growth cycle into five detection stages: the hatching egg stage, the brooding stage, the growing stage, the laying stage, and the culling stage, and configure corresponding benchmark detection intervals for each detection stage;

[0058] S2: Determine the benchmark positive threshold for the next detection stage according to the virus load detection distribution result of the previous detection stage;

[0059] S3: Based on the benchmark detection interval, dynamically adjust the detection time interval for the next detection in combination with the environmental virus activity index, detection cost, and / or cumulative purification rate;

[0060] S4: Based on the benchmark positive threshold, adjust the dynamic positive threshold for the next detection in combination with the detection time interval and the virus load detection result of the previous detection;

[0061] S5: Use the sample type corresponding to the detection stage to detect the virus load detection result of the target chickens in real time, and purify the target chickens whose virus load detection results detected in real time exceed the corresponding dynamic positive threshold.

[0062] In step S1, the benchmark detection interval for the hatching egg stage is 3 - 5 days; the benchmark detection interval for the brooding stage is 7 - 10 days; the benchmark detection interval for the growing stage is 14 - 21 days; the benchmark detection interval for the laying stage is 21 - 28 days; and the benchmark detection interval for the culling stage is 10 - 14 days.

[0063] Taking Wenchang chickens as an example for detailed description. Virus screening of hatching eggs needs to ensure no vertical transmission risk before hatching, but frequent detection will damage embryo development, and a benchmark detection interval of 5 days can balance safety and cost. The brooding stage is from 0 - 4 weeks old, the immune system of chicks is immature, the infection risk is high, and high-frequency monitoring is required, so the benchmark detection interval is 10 days. The growing stage is from 5 - 16 weeks old, the risk in the growing stage is relatively reduced, and the interval is extended to save resources, and the benchmark detection interval is set to 20 days. The laying stage is from 17 - 60 weeks old, the immunity of the laying flock is stable, but the safety of eggs needs to be guaranteed, and the interval is slightly longer than that in the growing stage, so the benchmark detection interval is set to 25 days. The culling stage is greater than 60 weeks old, the culling flock is about to flow into the slaughter, live poultry market or secondary breeding link, if carrying viruses (such as subgroup J avian leukosis virus), it may cause cross-farm transmission or food safety problems, and a benchmark detection interval of 12 days can capture at least one peak of virus excretion to avoid missed detection.

[0064] In step S2, the baseline positive threshold for the next detection stage is determined according to the virus load detection distribution result of the previous detection stage. The baseline positive threshold for the next detection stage can be the first threshold or the second threshold, or can also be the maximum value between the first threshold and the second threshold. It should be noted that the above-mentioned previous detection stage and the above-mentioned next detection stage are two adjacent detection stages.

[0065] In some examples, by extracting the first virus load distribution quantile corresponding to each virus load detection result in the virus load detection distribution result, the first virus load distribution quantile reflects the central tendency of the virus load; a quantile time series curve is constructed based on multiple consecutive first virus load distribution quantiles, and a linear regression analysis is performed on the quantile time series curve to obtain a linear regression slope; the first threshold is determined by the product of the baseline positive threshold of the previous detection stage and the historical trend correction parameter, and the historical trend correction parameter is determined by the linear regression slope.

[0066] Specifically, the expression for determining the historical trend correction parameter from the linear regression slope is:

[0067] ;

[0068] where represents the historical trend correction parameter; represents the sign function; represents the linear regression slope of the first virus load distribution quantile.

[0069] In some examples, by extracting the second virus load distribution quantile in the virus load detection distribution result, the second virus load distribution quantile captures high-risk tail data; the second threshold is determined by the product of the second virus load distribution quantile and the environmental risk correction parameter, and the environmental risk correction parameter is determined by the environmental virus activity index of the previous detection stage.

[0070] Specifically, the expression for determining the environmental risk correction parameter from the environmental virus activity index of the previous detection stage is:

[0071] ;

[0072] where represents the environmental risk correction parameter; represents the environmental response sensitivity coefficient, generally taking a value of 0.001; represents the mean value of the environmental virus activity index in the previous detection stage; represents the environmental virus activity critical value; represents the peak value of the environmental virus activity index in the previous detection stage.

[0073] Take the maximum value of the first threshold and the second threshold as the reference positive threshold for the next detection stage as an example.

[0074] (1) The virus load detection distribution results in the previous detection stage include the virus load detection results of multiple detections. The virus load quantiles are statistically analyzed from the virus load detection results of each detection. The virus load quantile is the value at the 50th percentile in the virus load detection results, reflecting the central tendency of the virus load. The virus load quantiles at different detection time points can be fitted to construct a quantile time series curve in chronological order.

[0075] By linearly regressing and analyzing the linear regression slope of the quantile time series curve, the specific expression is:

[0076] ;

[0077] where represents the linear regression slope corresponding to the 50th percentile of the virus load; represents the time window of the previous detection stage; represents the detection time point of the virus load at the 50th percentile; represents the average value of the virus load at the 50th percentile within the time window T; represents all detection time points within the time window T of the average value.

[0078] When the linear regression slope is greater than 0, it indicates that the virus load is on the rise and the transmission risk increases, and the reference positive threshold can be tightened; when the linear regression slope is less than 0, it indicates that the virus load is decreasing and the purification measures are effective, and the reference positive threshold can be relaxed. Substitute the above linear regression slope into the calculation expression of the historical trend correction parameter to determine the historical trend correction parameter, and then multiply the historical trend correction parameter by the reference positive threshold of the previous detection stage to determine the first threshold.

[0079] (2) Then extract the overall virus load at the 90th percentile in the virus load detection distribution results The virus load at the 90th percentile is mainly to capture high-risk tail data.

[0080] Determine the mean value of the environmental virus activity index and the peak value of the environmental virus activity index within the previous detection stage according to the collected environmental virus activity index. The mean value The peak value Substitute into the calculation expression of the above environmental risk correction parameter to determine the environmental risk correction parameter; use the product of the 90th percentile of the viral load and the environmental risk correction parameter to determine the second threshold.

[0081] The present invention establishes a double-constraint mechanism through the integration of trend quantification modeling and environmental peak response, which can effectively reduce the missed detection situation, and the double-constraint mechanism can achieve an emergency downward adjustment of the threshold, and can better cope with the sudden occurrence of diseases.

[0082] In step S3, when adjusting the detection time interval of the next detection, one or more data among the environmental virus activity index, detection cost, and cumulative purification rate can be considered.

[0083] In some examples, based on the benchmark detection interval, the detection time interval of the next detection is dynamically adjusted in combination with the environmental virus activity index and the detection cost. The specific expression is:

[0084] ;

[0085] Among them, represents the detection time interval of the next detection; represents the benchmark detection interval of the current detection stage; represents the environmental virus activity index of the previous detection; represents the environmental virus activity critical value; represents the virus activity response coefficient, such as taking the value of 0.005; represents the first cost adjustment parameter, such as taking the value of 0.2; represents the benchmark detection cost; represents the real-time detection cost.

[0086] At greater than , the detection time interval can be shortened by exponential risk drive through the characteristics of the Sigmoid function; at greater than , the economic efficiency is balanced by suppressing the amplitude of the interval shortening, and it can be better applied to the detection stage with large fluctuations in virus activity and the need to control the detection cost, such as the growth period.

[0087] In some examples, based on the benchmark detection interval, the detection time interval of the next detection is dynamically adjusted in combination with the environmental virus activity index and the cumulative purification rate. The specific expression is:

[0088] ;

[0089] Among them, represents the detection time interval of the next detection; represents the benchmark detection interval of the current detection stage; Indicates the environmental virus activity index of the last detection; Indicates the critical value of environmental virus activity; Indicates the cumulative purification rate; Indicates the purification threshold, the value is greater than 0.5 and less than 1, such as 70%; Indicates the first purification adjustment parameter, such as a value of 0.15; Indicates the virus adjustment parameter, such as the value of 0.05.

[0090] when When it is greater than 70%, the gain rate is suppressed by square root to extend the detection time interval; in addition, Greater than When the virus activity increases, it can shorten the detection time interval and enhance the monitoring density. It is suitable for the later stage when the purification effect is significant but it is necessary to prevent the resurgence of the environment, such as the egg-laying period.

[0091] In some examples, based on the benchmark detection interval, the detection time interval for the next detection is dynamically adjusted in combination with the detection cost and the cumulative purification rate. The specific expression is:

[0092] ;

[0093] in, Indicates the detection time interval for the next detection; Indicates the benchmark detection interval of the current detection phase; represents the benchmark detection cost; represents the real-time detection cost; Indicates the cumulative purification rate; Indicates the second cost adjustment parameter, such as a value of 0.3; Indicates the second purification adjustment parameter, such as a value of 0.1.

[0094] When the real-time detection cost is greater than the benchmark detection cost, the detection time interval is increased through logarithmic function smoothing, and the detection time interval is further relaxed when the purification rate is close to 100%. This can be better applied to detection stages where resources are tight and purification is close to reaching the standard, such as the elimination period.

[0095] It should be noted that the cumulative purification rate refers to: the total number of samples with negative virus test results in the current test and historical tests and the total number of all tested samples in the current test.

[0096] In step S4, based on the benchmark positive threshold, the dynamic positive threshold for the next test is adjusted in combination with the detection time interval and the viral load test result of the previous test. The specific expression is:

[0097] ;

[0098] Among them, represents the dynamic positive threshold for the next detection; represents the reference positive threshold for the detection stage to which the next detection belongs; represents the reference detection interval for the current detection stage; represents the detection time interval for the next detection; represents the time sensitivity coefficient; represents the virus activity penalty coefficient, such as taking a value of 0.1; represents the environmental virus activity critical value; represents the peak value of the environmental virus activity index within the previous detection stage.

[0099] Through the non - linear cooperation of the time interval correction term and the virus activity penalty term, the present invention realizes the dynamic adjustment of the dynamic positive threshold; it can achieve: the higher the virus peak and the longer the detection interval, the lower the threshold (sensitivity first); the higher the detection frequency and the lower the virus peak, the higher the threshold (false positive rate first); the square root and power function suppress extreme fluctuations, and it can be applied to the prevention and control of diseases with long incubation periods and large environmental fluctuations such as avian leukosis, which can reduce the missed detection rate and false killing cost and improve the purification efficiency.

[0100] In step S5, the sample types in the hatching egg stage are meconium or eggshell membrane; the sample type in the chick rearing stage is cloacal swab; the sample type in the growing stage is serum; the sample types in the laying stage are egg white or semen; and, the sample types in the culling stage are semen or cloacal tissue.

[0101] In some examples, the purification treatment is to directly purify the target chickens and fowls that exceed the standard.

[0102] Experimental comparison: Select 1000 healthy Wenchang chicken chicks of the same batch and from the same farm, and randomly divide them into an experimental group (500) and a control group (500). Initially, artificially inoculate a low - dose avian leukemia virus so that the infection rate is 2% (10 infected), and the experimental period covers 5 growth stages, a total of 420 days. The detection results of the experimental group are shown in Table 1, and the detection results of the control group are shown in Table 2.

[0103] Table 1 Detection Results of the Experimental Group

[0104] Number of detections Detection stage Detection time (days) Detection time interval (days) Purification quantity (pieces) Remaining poultry (pieces) 1 Egg stage 5 - 8 492 2 Brooding stage 12 (+7 days) 7 6 486 3 Brooding stage 22 (+10 days) 10 5 481 4 Growth stage 43 (+21 days) 21 4 477 5 Growth stage 64 (+21 days) 21 3 474 6 Laying stage 90 (+26 days) 26 2 472 7 Laying stage 119 (+29 days) 29 (>28 days) 1 471 8 Laying stage 154 (+35 days) 35 (>28 days) 1 470 9 Laying stage 189 (+35 days) 35 (>28 days) 0 470 10 Laying stage 224 (+35 days) 35 (>28 days) 0 470 11 Laying stage 259 (+35 days) 35 (>28 days) 0 470 12 Laying stage 294 (+35 days) 35 (>28 days) 0 470 13 Culling stage 420 (+126 days) 126 (Final inspection) 0 470

[0105] Table 2 Detection Results of the Control Group

[0106] Number of detections Detection stage Detection time (days) Detection time interval (days) Purification quantity (pieces) Remaining poultry (pieces) 1 Egg stage 5 - 7 493 2 Brooding stage 15 (+10 days) 10 (Benchmark upper limit) 5 488 3 Brooding stage 25 (+10 days) 10 (Benchmark upper limit) 5 483 4 Growth stage 46 (+21 days) 21 (Benchmark upper limit) 6 477 5 Growth stage 67 (+21 days) 21 (Benchmark upper limit) 4 473 6 Laying stage 95 (+28 days) 28 (Benchmark upper limit) 5 468 7 Laying stage 123 (+28 days) 28 (Benchmark upper limit) 3 465 8 Laying stage 151 (+28 days) 28 (Benchmark upper limit) 2 463 9 Laying stage 179 (+28 days) 28 (Benchmark upper limit) 3 460 10 Laying stage 207 (+28 days) 28 (Benchmark upper limit) 1 459 11 Laying stage 235 (+28 days) 28 (Benchmark upper limit) 1 458 12 Laying stage 263 (+28 days) 28 (Benchmark upper limit) 0 458 13 Laying stage 291 (+28 days) 28 (Benchmark upper limit) 0 458 14 Laying stage 319 (+28 days) 28 (Benchmark upper limit) 0 458 15 Laying stage 347 (+28 days) 28 (Benchmark upper limit) 0 458 16 Laying stage 375 (+28 days) 28 (Benchmark upper limit) 0 458 17 Laying stage 403 (+28 days) 28 (Benchmark upper limit) 0 458 18 Culling stage 420（+17） 17 (Final inspection) 0 458

[0107] Experimental conclusion: By dynamically adjusting the detection time interval and the dynamic positive threshold, the present invention extends the detection time interval when the virus activity decreases or the cumulative purification rate reaches the standard, reduces the number of ineffective detections, and significantly reduces the detection cost. Especially during the laying period, the detection time interval of the experimental group was gradually adjusted from 26 days to 35 days (exceeding the benchmark value), while the control group maintained a fixed detection interval of 28 days, resulting in an increase in the number of detections and a waste of resources.

[0108] In addition, by integrating the dynamic adjustment mechanism of the environmental virus activity index and the cumulative purification rate, the present invention achieves a balance between detection sensitivity and stability. Due to the fixed interval and static threshold of the control group, it cannot adaptively adjust when the virus load fluctuates, resulting in unstable purification effects.

[0109] Furthermore, the present invention achieves better final inspection results with fewer purification times, indicating that its method has significant advantages in accurately identifying infected individuals at an early stage and blocking the transmission chain, while reducing the false killing rate and ensuring the economic benefits of farming.

[0110] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A purification detection method for avian leukosis in chickens based on multi-stage dynamic thresholds, characterized in that, It includes the following steps: Divide the growth cycle of chickens into five detection stages: the hatching egg stage, the brooding stage, the growth stage, the laying stage, and the culling stage. A corresponding benchmark detection interval is configured for each of the detection stages. Determine the benchmark positive threshold for the next detection stage according to the virus load detection distribution result of the previous detection stage. Based on the benchmark detection interval, dynamically adjust the detection time interval for the next detection in combination with the environmental virus activity index, detection cost, and / or cumulative purification rate. Based on the benchmark positive threshold, adjust the dynamic positive threshold for the next detection in combination with the detection time interval and the virus load detection result of the previous detection. Adopt the sample type corresponding to the detection stage to detect the virus load detection result of the target chickens in real time, and perform purification treatment on the target chickens whose virus load detection results detected in real time exceed the corresponding dynamic positive threshold.

2. The method for purifying and detecting avian leukosis based on multi-stage dynamic thresholds according to claim 1, wherein The determination of the benchmark positive threshold for the next detection stage according to the virus load detection distribution result of the previous detection stage includes: The benchmark positive threshold for the next detection stage is the first threshold, the second threshold, or the maximum value of the first threshold and the second threshold. Among them, the first threshold is: Extract the first virus load distribution quantile corresponding to each virus load detection result in the virus load detection distribution result. The first virus load distribution quantile reflects the central tendency of the virus load. Construct a quantile time series curve based on multiple consecutive first virus load distribution quantiles, and perform linear regression analysis on the quantile time series curve to obtain a linear regression slope. Determine the first threshold by multiplying the benchmark positive threshold of the previous detection stage by the historical trend correction parameter, and the historical trend correction parameter is determined by the linear regression slope. And, the second threshold is: Extract the second virus load distribution quantile in the virus load detection distribution result. The second virus load distribution quantile captures high-risk tail data. Determine the second threshold by multiplying the second virus load distribution quantile by the environmental risk correction parameter, and the environmental risk correction parameter is determined by the environmental virus activity index of the previous detection stage.

3. The method for purifying and detecting avian leukosis based on multi-stage dynamic thresholds according to claim 2, wherein The expression for determining the historical trend correction parameter by the linear regression slope is: ； Among them, represents the historical trend correction parameter; represents the sign function; represents the linear regression slope of the first viral load distribution quantile.

4. The method for purifying and detecting avian leukosis based on multi-stage dynamic thresholds according to claim 2, wherein, The expression for determining the environmental risk correction parameter by the environmental virus activity index of the previous detection stage is: ； Among them, represents the environmental risk correction parameter; represents the environmental response sensitivity coefficient; represents the mean value of the environmental virus activity index in the previous detection stage; represents the environmental virus activity critical value; represents the peak value of the environmental virus activity index in the previous detection stage.

5. The method for purifying and detecting avian leukosis based on multi-stage dynamic thresholds according to claim 1, characterized in that, Dynamically adjust the detection time interval for the next detection based on the benchmark detection interval in combination with the environmental virus activity index and the detection cost. The specific expression is: ； Among them, represents the detection time interval for the next detection; represents the reference detection interval for the current detection stage; represents the environmental virus activity index of the previous detection; represents the environmental virus activity critical value; represents the virus activity response coefficient; represents the first cost adjustment parameter; represents the reference detection cost; represents the real-time detection cost.

6. The method for purifying and detecting avian leukosis based on multi-stage dynamic thresholds according to claim 1, characterized in that, Dynamically adjust the detection time interval for the next detection based on the benchmark detection interval in combination with the environmental virus activity index and the cumulative purification rate. The specific expression is: ； Among them, represents the detection time interval for the next detection; represents the reference detection interval for the current detection stage; represents the environmental virus activity index of the previous detection; represents the environmental virus activity critical value; represents the cumulative purification rate; represents the purification threshold, with a value greater than 0.5 and less than 1; represents the first purification adjustment parameter; represents the virus adjustment parameter.

7. The method for purifying and detecting avian leukosis based on multi-stage dynamic thresholds according to claim 1, wherein Dynamically adjust the detection time interval for the next detection based on the benchmark detection interval in combination with the detection cost and the cumulative purification rate. The specific expression is: ； Among them, represents the detection time interval for the next detection; represents the reference detection interval for the current detection stage; represents the reference detection cost; represents the real-time detection cost; represents the cumulative purification rate; represents the second cost adjustment parameter; represents the second purification adjustment parameter.

8. The method for purifying and detecting avian leukosis based on multi-stage dynamic thresholds according to claim 1, wherein Dynamically adjust the dynamic positive threshold for the next detection based on the benchmark positive threshold in combination with the detection time interval and the virus load detection result of the previous detection. The specific expression is: ； Among them, represents the dynamic positive threshold for the next detection; represents the reference positive threshold for the detection stage to which the next detection belongs; represents the reference detection interval for the current detection stage; represents the detection time interval for the next detection; represents the time sensitivity coefficient; represents the virus activity penalty coefficient; represents the environmental virus activity critical value; represents the peak value of the environmental virus activity index within the previous detection stage.

9. The method for purifying and detecting avian leukosis based on multi-stage dynamic thresholds according to claim 1, wherein, The benchmark detection interval for the hatching egg stage is 3 - 5 days. The reference detection interval during the brooding period is 7 - 10 days; The reference detection interval during the growth period is 14 - 21 days; The reference detection interval during the laying period is 21 - 28 days; And, the reference detection interval during the culling period is 10 - 14 days.

10. The method for purifying and detecting avian leukosis based on multi-stage dynamic thresholds according to claim 1, characterized in that, The sample type during the hatching egg period is meconium or eggshell membrane; The sample type during the brooding period is cloacal swab; The sample type during the growth period is serum; The sample type during the laying period is egg white or semen; And, the sample type during the culling period is semen or cloacal tissue.

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