A calibration method for the relative risk value of resistance genes in a drinking water source in the spatio-temporal dimension

Abstract

The present invention discloses a method for calibrating the spatio-temporal dimension of the relative risk value of antibiotic resistance genes in a drinking water source, comprising: obtaining the relative risk value data of antibiotic resistance genes and the corresponding water intake time data, water intake point data and influencing variable data of the relative risk value data of antibiotic resistance genes; constructing a spatial adjacency matrix to obtain a spatial information file; constructing a calibration model for the relative risk value of antibiotic resistance genes including spatial and temporal random effects; adding the spatial information file and using the integrated nested Laplace approximation algorithm to perform Bayesian inference on the calibration model for the relative risk value of antibiotic resistance genes; determining the final calibration model for the relative risk value of antibiotic resistance genes according to the deviance information criterion; and using the final calibration model for the relative risk value of antibiotic resistance genes to calibrate the target relative risk value data of antibiotic resistance genes. The present invention can improve the calibration accuracy of the relative risk value of antibiotic resistance genes in a drinking water source.

Description

Technical Field

[0001] The present invention relates to the field of calibrating the relative risk of antibiotic resistance genes in drinking water sources, and relates to a method for calibrating the relative risk value of resistance genes in drinking water sources in the spatio-temporal dimension. Background Art

[0002] Multidimensional uncertainties existing in the water-related systems associated with Antibiotic Resistant Genes (ARGs) (uncertainties in gene abundance measurements caused by uncertainties in DNA extraction during water sample processing, uncertainties in the dynamic changes of various driving factors over time, uncertainties in the influence between driving factors and ARGs, etc.) make the relative risk values of ARGs evaluated in this system have uncertainties in the spatio-temporal dimension. In addition, most of the relationships between ARGs and environmental variables are indirect. Although the statistical significance of the correlation exists, the correlation coefficients between ARGs and driving factors are small. Characteristics such as multidimensional uncertainties and small correlations among multiple variables pose great challenges to the calibration of the relative risk value of ARGs in the spatio-temporal dimension of drinking water sources. Therefore, there is an urgent need for an effective method to calibrate the relative risk value of resistance genes in the spatio-temporal dimension of drinking water sources. Summary of the Invention

[0003] Aiming at the above deficiencies in the prior art, the present invention provides a method for calibrating the relative risk value of resistance genes in drinking water sources in the spatio-temporal dimension, which solves the problem of inaccurate measurement of antibiotic resistance genes in drinking water sources in the prior art.

[0004] In order to achieve the above invention purpose, the technical solution adopted by the present invention is: a method for calibrating the relative risk value of resistance genes in drinking water sources in the spatio-temporal dimension, including the following steps:

[0005] S1. Obtain the relative risk value data of resistance genes, as well as the water intake time data, water intake point data, and influencing variable data corresponding to the relative risk value data of resistance genes, and preprocess the relative risk value data of resistance genes, water intake time data, water intake point data, and influencing variable data;

[0006] S2. According to the preprocessed water intake point data, construct a spatial adjacency matrix to obtain a spatial information file;

[0007] S3. Based on the Poisson distribution, construct a calibration model for the relative risk value of resistance genes that includes spatial and temporal random effects;

[0008] S4. Based on the preprocessed relative risk value data of resistance genes, water intake time data, water intake point data, influencing variable data, and spatial information file, use the integrated nested Laplace approximation algorithm to perform Bayesian inference on the calibration model for the relative risk value of resistance genes to obtain a trained calibration model for the relative risk value of resistance genes;

[0009] S5. Determine the calibration model of the final relative risk value of resistance genes according to the deviation information quantity criterion;

[0010] S6. Calibrate the relative risk value data of the target resistance genes using the calibration model of the final relative risk value of resistance genes, and visualize the calibrated relative risk value data of the target resistance genes.

[0011] The beneficial effects of the above solution are as follows:

[0012] (1) Through the Bayesian spatio-temporal Poisson regression ARGs risk value calibration model, the present invention combines multi-dimensional influencing variables such as easily measurable land use types and common types of bacteria, significantly improving the spatial and temporal resolution of the calibration results and well explaining the spatio-temporal uncertainty of the relative risk values of ARGs. This enables the water source management department to obtain more accurate relative risk values of ARGs and take corresponding control measures in a timely manner, thereby better protecting public health.

[0013] (2) The present invention fully considers the selection of model influencing variable combinations and spatio-temporal interaction effects, and proposes a model comprehensive selection method based on multi-stage screening and multi-dimensional weighted indicators. This method is different from traditional single-index selection. For the specific Bayesian spatio-temporal Poisson regression model selection scenario, it can not only improve the efficiency of model selection, but also avoid missing the best model under a single index, enhancing the reliability of the calibration results of the relative risk values of ARGs. This method is not only applicable to the above model selection, but also can be extended to similar model selection processes, with high reference value.

[0014] (3) By constructing a spatial adjacency matrix and using the INLA algorithm, the present invention can efficiently process large-scale spatio-temporal data, improving the calculation speed and the applicability of the model. This method is not only applicable to the above calibration of the relative risk values of ARGs, but also can be extended to the environmental pollution risk management with spatio-temporal uncertainty, with broad application prospects.

[0015] Further, step S1 specifically includes:

[0016] S11. Obtain the relative risk value data of resistance genes, water intake time data, water intake point data, and influencing variable data from the relative risk value database of resistance genes;

[0017] S12. Convert the relative risk value data of resistance genes into an integer form to obtain the preprocessed relative risk value data of resistance genes;

[0018] S13. Convert the water intake time data and water intake point data into numerical data to obtain the preprocessed water intake time data and the preprocessed water intake point data;

[0019] S14. Use the K-nearest neighbor algorithm to perform weighted averaging on the impact variable data to obtain the preprocessed impact variable data.

[0020] Further, step S2 specifically includes:

[0021] S21. Mark the preprocessed water intake point data on the water intake area map, and obtain the adjacent relationship between the corresponding water bodies of the preprocessed water intake point data according to the relevant positions of the preprocessed water intake point data in the water intake area map;

[0022] S22. Construct a spatial adjacency matrix based on the adjacent relationship;

[0023] S23. Create an ASCII file corresponding to the spatial adjacency matrix according to the spatial adjacency matrix to obtain a spatial information file.

[0024] The beneficial effects of the above further solution are as follows: It helps to reduce noise and errors in the modeling process, ensure the quality and accuracy of the model's basic data, and facilitate subsequent model construction and model inference. Capturing the spatial associations between different water bodies effectively enhances the model's ability to grasp the spatial characteristics of drinking water source water bodies.

[0025] Further, step S3 specifically includes:

[0026] S31. Use the intrinsic conditional autoregressive specification to model the spatial structured effect. The model of the spatial structured effect is:

[0027]

[0028] where u i represents the spatial structured effect, u -i represents the set of spatial structured effects except for region i, n represents the number of regions, μ i represents the mean of region i, μ j represents the mean of region j, N i represents the number of neighbors of region i, is the variance of the same region, is the variance parameter, a ij means that if region i and region j are neighbors, the value of a ij is assigned 1, otherwise assigned 0;

[0029] S32. Perform independent and identically distributed Gaussian prior modeling on the spatial unstructured effect. The model of the spatial unstructured effect is:

[0030]

[0031] where v iRepresents the spatial unstructured effect, represents the variance parameter;

[0032] S33. Model the temporal structured effect using first-order random walk and second-order random walk respectively. The first-order random walk model of the temporal structured effect is:

[0033]

[0034] where γ j represents the temporal structured effect, and γ j-1 represents the temporal structured effect at the previous moment, represents the variance of the first-order random walk process;

[0035] The second-order random walk model of the temporal structured effect is:

[0036]

[0037] where γ j represents the temporal structured effect, and γ j-1 represents the temporal structured effect at the previous moment, and γ j-2 represents the temporal structured effect at the two previous moments, represents the variance of the second-order random walk process;

[0038] S34. Model the temporally unstructured effect with an independent and identically distributed Gaussian prior. The model of the temporally unstructured effect is:

[0039]

[0040] where represents the temporally unstructured effect, represents the variance parameter;

[0041] S35. Define the spatio-temporal interaction according to the different interactions of the spatial structured effect, spatial unstructured effect, temporal structured effect, and temporally unstructured effect, and based on the first-order random walk model and second-order random walk model of the temporal structured effect.

[0042] Among them, there are 6 types of spatio-temporal interactions;

[0043] S36. Based on the models of the spatial structured effect, spatial unstructured effect, first-order random walk model of the temporal structured effect, first-order random walk model of the temporal structured effect, model of the temporally unstructured effect, and spatio-temporal interaction, and based on the Poisson distribution, establish a calibration model for the relative risk value of resistance genes. The calibration model for the relative risk value of resistance genes is:

[0044] Y ij =[γyij ~Poisson(λ ij )

[0045]

[0046] Among them, Y ij represents the relative risk value of the resistance gene at time j in region i after scaling, and y ij represents the relative risk value of the resistance gene corresponding to region i at time j. γ represents the coefficient used to scale the relative risk value, and λ ij represents the Poisson distribution parameter, b 0 represents the bias term of the regression model, u i represents the spatial structured effect, v i represents the spatial unstructured effect, γ j represents the temporal structured effect, represents the temporal unstructured effect, δ ij represents the spatio-temporal interaction.

[0047] The beneficial effects of the above further scheme are as follows: It can more comprehensively consider the influence of spatio-temporal effects on the relative risk of ARGs, further consider the uncertainties in the time and space dimensions of the relative risk of ARGs, and improve the accuracy and applicability of the model.

[0048] Furthermore, in step S4, the integrated nested Laplace approximation algorithm is used to perform Bayesian inference on the calibration model of the relative risk value of the resistance gene, and the calibrated model of the relative risk value of the resistance gene after training is obtained, which specifically includes:

[0049] S41. Combine the model of the spatial structured effect and the model of the spatial unstructured effect to construct a Besag-York-Mollié model;

[0050] S42. Based on the Besag-York-Mollié model, the second-order random walk model of the temporal structured effect, and the model of the temporal unstructured effect, use the integrated nested Laplace approximation algorithm to perform Bayesian inference on the calibration model of the relative risk value of the resistance gene, and obtain the calibrated model of the relative risk value of the resistance gene after training.

[0051] Furthermore, after step S4, the method further includes:

[0052] Infer the model of the spatial structured effect, the model of the spatial unstructured effect, the first-order random walk model of the temporal structured effect, the first-order random walk model of the temporal structured effect, the model of the temporal unstructured effect, and the spatio-temporal interaction. Based on the spatio-temporal interaction, respectively obtain the bias information quantity of the calibrated model of the relative risk value of the resistance gene corresponding to the spatio-temporal interaction.

[0053] The beneficial effects of the above further solution are as follows: When dealing with complex models and facing computational difficulties, the integrated nested Laplace approximation inference is used to efficiently estimate the regression model parameters, which is convenient for obtaining the best model subsequently; further, six types of spatio-temporal interactions are considered and modeled for inference, which is convenient for subsequent model selection and makes the final model more reliable and robust.

[0054] Further, step S5 specifically includes:

[0055] S51. Use Pearson correlation analysis to perform correlation analysis on the influencing variable data, and screen the results of the correlation analysis according to a preset correlation threshold to obtain an initial correlation combination;

[0056] S52. Add the initial correlation combination to the models of spatial structured effects, spatial unstructured effects, the first-order random walk model of temporal structured effects, the first-order random walk model of temporal structured effects, the model of temporal unstructured effects, and spatio-temporal interactions respectively to obtain a set of alternative calibration models for the relative risk value of resistance genes;

[0057] S53. Use the deviance information criterion to screen the set of alternative calibration models for the relative risk value of resistance genes;

[0058] S54. Use the first weighted score to perform a second screening on the set of alternative calibration models for the relative risk value of resistance genes after screening;

[0059] S55. Use the second weighted score to perform a sensitivity analysis on the set of alternative calibration models for the relative risk value of resistance genes after the second screening and perform a third screening;

[0060] S56. According to the preset screening rules, perform a fourth screening on the set of alternative calibration models for the relative risk value of resistance genes after the third screening, and determine the final set of calibration models for the relative risk value of resistance genes;

[0061] S57. According to the final set of calibration models for the relative risk value of resistance genes, determine the final calibration model for the relative risk value of resistance genes.

[0062] Further, in step S57, the final calibration model for the relative risk value of resistance genes is:

[0063] Y ij =[γy ij ~Poisson(λ ij )

[0064]

[0065] where Y ij represents the relative risk value of resistance genes at time j in region i after scaling, yij represents the relative risk value of the resistance gene corresponding to region \(i\) and time \(j\), \(\gamma\) represents the coefficient used to scale the relative risk value, and \(\lambda\) ij represents the Poisson distribution parameter, and \(b\) 0 represents the bias term of the regression model, and \(u\) i represents the spatial structuring effect, and \(v\) i represents the spatial unstructuring effect, and \(\gamma\) j represents the time structuring effect, represents the time unstructuring effect, and \(\delta\) ij represents the spatio-temporal interaction. Variable 1 represents the first influencing variable in the selected combination of influencing variables, and variable 2 represents the second influencing variable in the selected combination of influencing variables.

[0066] The beneficial effects of the above further solution are as follows: considering the impacts of different combinations of influencing variables and spatio-temporal interactions on the model, ensuring that the final model can best explain the multi-dimensional uncertainties, and greatly improving the reliability of calibrating the relative risk values of ARGs.

[0067] Further, step S6 specifically includes:

[0068] S61. Calibrate the relative risk value data of the target resistance gene using the final relative risk value calibration model of the resistance gene to obtain the model operation result;

[0069] S62. Extract the posterior mean in the model operation result as the relative risk calibration value of the relative risk value data of the target resistance gene;

[0070] S63. Extract the mean of the time random effect in the model operation result, draw a time series graph, and visualize the calibrated relative risk value data of the target resistance gene.

[0071] Further, after step S62, the method further includes:

[0072] Compare the relative risk calibration value with the relative risk value data of the target resistance gene to determine the calibration accuracy of the final relative risk value calibration model of the resistance gene.

[0073] The beneficial effects of the above further solution are as follows: making the model result more interpretable and practical, and the visualization result provides an intuitive environmental risk management tool for decision-makers. Brief Description of the Drawings

[0074] Figure 1 It is a schematic flowchart of a method for calibrating the spatio-temporal dimension of the relative risk value of resistance genes in a drinking water source. Detailed Embodiments

[0075] The present invention will be further described below in conjunction with the drawings and specific embodiments.

[0076] As Figure 1 shown, a method for calibrating the spatio-temporal dimension of the relative risk value of resistance genes in a drinking water source includes the following steps:

[0077] S1. Obtain the relative risk value data of resistance genes, the corresponding water intake time data, water intake point data, and influencing variable data of the relative risk value data of resistance genes, and preprocess the relative risk value data of resistance genes, water intake time data, water intake point data, and influencing variable data.

[0078] In this embodiment, step S1 specifically includes:

[0079] S11. Obtain the relative risk value data of resistance genes, water intake time data, water intake point data, and influencing variable data from the relative risk value database of resistance genes;

[0080] S12. Convert the relative risk value data of resistance genes into an integer form to obtain the preprocessed relative risk value data of resistance genes;

[0081] S13. Convert the water intake time data and water intake point data into numerical data to obtain the preprocessed water intake time data and the preprocessed water intake point data;

[0082] S14. Use the K-nearest neighbor algorithm to perform weighted averaging on the influencing variable data to obtain the preprocessed influencing variable data.

[0083] Exemplarily, converting the relative risk value data of resistance genes into an integer form can be to expand the relative risk value of ARGs in the drinking water source water body by 10n times, and the minimum value of n is determined by the relative risk value of ARGs after expansion being an integer.

[0084] Optionally, the influencing variable data may further include alternative influencing variable data, and the alternative influencing variable data may include variables related to land use in the water intake area and variables related to potentially influencing bacteria in the water body, etc. The variables related to land use in the water intake area may include the proportions of residential areas (Residential), urban areas (Urban), green areas (Green), and agricultural areas (Agriculture) in the drinking water source area. The different area proportions imply the spatial information of the water intake area and the spatial correlation between water intake areas, and can explain the uncertainty in the relative risk of ARGs to a certain extent. The variables related to potentially influencing bacteria in the water body may include the contents of Escherichia coli, Enterococcus, and Pseudomonas aeruginosa in the drinking water source water body. Generally, when treating infections caused by these bacteria, more potent antibiotics or a combination of multiple antibiotics can be selected. Therefore, it can be considered that their contents in the water body can further correct the uncertainty in the relative risk of ARGs in the water body.

[0085] S2. Construct a spatial adjacency matrix based on the preprocessed water intake point data to obtain a spatial information file.

[0086] In this embodiment, step S2 specifically includes:

[0087] S21. Mark the preprocessed water intake point data on the water intake area map, and obtain the adjacent relationship between the corresponding water bodies of the preprocessed water intake point data according to the relevant positions of the preprocessed water intake point data on the water intake area map;

[0088] S22. Construct a spatial adjacency matrix according to the adjacent relationship;

[0089] S23. Create an ASCII file corresponding to the spatial adjacency matrix according to the spatial adjacency matrix to obtain a spatial information file.

[0090] Exemplarily, mark the preprocessed water intake point data on the water intake area map. For the involved water bodies (catchment areas / tributaries), obtain the adjacent relationship between two water bodies according to their distribution positions and coverage areas. For adjacent water bodies, assign 1; for non-adjacent water bodies, assign 0, and thus a spatial adjacency matrix can be constructed. The number of rows in the ASCII file is equal to the number of regions n plus 1. The first row is n, and each of the next n rows is specified as: (i) the region label with indices 1, 2,..., n, (ii) the number of its neighbors, and (iii) the labels of the neighboring regions.

[0091] S3. Based on the Poisson distribution, construct a calibration model for the relative risk value of resistance genes that includes spatial and temporal random effects.

[0092] In this embodiment, step S3 specifically includes:

[0093] S31. Use the intrinsic conditional autoregressive specification to model the spatial structured effect. The model of the spatial structured effect is:

[0094]

[0095] where u i represents the spatial structured effect, u -i represents the set of spatial structured effects except for region i, n represents the number of regions, μ i represents the mean of region i, μ j represents the mean of region j, N i represents the number of neighbors of region i, is the variance of the same region, is the variance parameter, a ij represents that if region i and region j are neighbors, assign the value of a ij to 1, otherwise assign 0;

[0096] S32. Model the spatially unstructured effect with an independent and identically distributed Gaussian prior. The model for the spatially unstructured effect is:

[0097]

[0098] where v i represents the spatially unstructured effect, and represents the variance parameter;

[0099] S33. Model the temporally structured effect using first-order random walk and second-order random walk respectively. The first-order random walk model for the temporally structured effect is:

[0100]

[0101] where γ j represents the temporally structured effect, γ j-1 represents the temporally structured effect at the previous time step, and represents the variance of the first-order random walk process;

[0102] The second-order random walk model for the temporally structured effect is:

[0103]

[0104] where γ j represents the temporally structured effect, γ j-1 represents the temporally structured effect at the previous time step, γ j-2 represents the temporally structured effect at the two previous time steps, and represents the variance of the second-order random walk process;

[0105] S34. Model the temporally unstructured effect with an independent and identically distributed Gaussian prior. The model for the temporally unstructured effect is:

[0106]

[0107] where represents the temporally unstructured effect, and represents the variance parameter;

[0108] S35. Define the spatio-temporal interaction according to the different interactions of the spatial structured effect, spatial unstructured effect, temporal structured effect, and temporal unstructured effect, and simultaneously based on the first-order random walk model and the second-order random walk model of the temporal structured effect.

[0109] Among them, there are 6 types of spatio-temporal interactions;

[0110] S36. Based on the models of spatial structuring effect, spatial unstructuring effect, first-order random walk model of temporal structuring effect, first-order random walk model of temporal structuring effect, model of temporal unstructuring effect, and spatio-temporal interaction, and based on the Poisson distribution, establish a calibration model for the relative risk value of resistance genes. The calibration model for the relative risk value of resistance genes is as follows:

[0111] Y ij =[γy ij ~Poisson(λ ij )

[0112]

[0113] where Y ij represents the relative risk value of resistance genes at time j in region i after scaling, y ij represents the relative risk value of resistance genes corresponding to region i at time j, γ represents the coefficient used to scale the relative risk value, λ ij represents the Poisson distribution parameter, b 0 represents the bias term of the regression model, u i represents the spatial structuring effect, v i represents the spatial unstructuring effect, γ j represents the temporal structuring effect, represents the temporal unstructuring effect, δ ij represents the spatio-temporal interaction.

[0114] Exemplarily, when defining the spatio-temporal interaction, the spatio-temporal interaction can be decomposed into the Kronecker product of the spatio-temporal interactions of the corresponding main effects that interact with each other. And according to the spatial effects u i , v i and the temporal effects γ j , different interactions, four methods can be used to define the spatio-temporal interaction. The temporal structuring effect γ j uses a first-order or second-order random walk during the interaction, and finally six spatio-temporal interactions can be obtained.

[0115] S4. Based on the preprocessed data of the relative risk value of resistance genes, water intake time data, water intake point data, influencing variable data, and spatial information file, use the integrated nested Laplace approximation algorithm to perform Bayesian inference on the calibration model of the relative risk value of resistance genes, and obtain the trained calibration model of the relative risk value of resistance genes.

[0116] In this embodiment, in step S4, using the integrated nested Laplace approximation algorithm to perform Bayesian inference on the calibration model of the relative risk value of resistance genes to obtain the trained calibration model of the relative risk value of resistance genes specifically includes:

[0117] S41. Combine the model of spatial structured effect and the model of spatial unstructured effect to construct the Besag-York-Mollie model;

[0118] S42. Based on the Besag-York-Mollie model, the second-order random walk model of temporal structured effect, and the model of temporal unstructured effect, use the integrated nested Laplace approximation algorithm to perform Bayesian inference on the calibration model of the relative risk value of resistance genes, and obtain the calibrated model of the relative risk value of resistance genes after training.

[0119] Optionally, after step S4, the method further includes:

[0120] Infer the model of spatial structured effect, the model of spatial unstructured effect, the first-order random walk model of temporal structured effect, the first-order random walk model of temporal structured effect, the model of temporal unstructured effect, and the spatio-temporal interaction. Based on the spatio-temporal interaction, respectively obtain the bias information amount of the calibrated model of the relative risk value of resistance genes corresponding to the spatio-temporal interaction.

[0121] S5. Determine the final calibrated model of the relative risk value of resistance genes according to the bias information amount criterion.

[0122] In this embodiment, step S5 specifically includes:

[0123] S51. Use Pearson correlation analysis to perform correlation analysis on the influencing variable data, and screen the results of the correlation analysis according to a preset correlation threshold to obtain an initial correlation combination;

[0124] S52. Add the initial correlation combination to the model of spatial structured effect, the model of spatial unstructured effect, the first-order random walk model of temporal structured effect, the first-order random walk model of temporal structured effect, the model of temporal unstructured effect, and the spatio-temporal interaction respectively to obtain a set of alternative calibrated models of the relative risk value of resistance genes;

[0125] S53. Use the bias information amount criterion to screen the set of alternative calibrated models of the relative risk value of resistance genes;

[0126] S54. Use the first weighted score to perform a second screening on the set of alternative calibrated models of the relative risk value of resistance genes after screening;

[0127] S55. Use the second weighted score to perform sensitivity analysis on the set of alternative calibrated models of the relative risk value of resistance genes after the second screening, and perform a third screening;

[0128] S56. Perform a fourth screening on the set of alternative resistance gene relative risk value calibration models after the third screening according to the preset screening rules, and determine the final set of resistance gene relative risk value calibration models;

[0129] S57. Determine the final resistance gene relative risk value calibration model according to the final set of resistance gene relative risk value calibration models.

[0130] In step S57 of this embodiment, the final resistance gene relative risk value calibration model is:

[0131] Y ij =[γy ij ~Poisson(λ ij )

[0132]

[0133] where Y ij represents the relative risk value of the resistance gene at time j in region i after scaling, y ij represents the relative risk value of the resistance gene corresponding to region i at time j, γ represents the coefficient used to scale the relative risk value, λ ij represents the Poisson distribution parameter, b 0 represents the bias term of the regression model, u i represents the spatial structured effect, v i represents the spatial unstructured effect, γ j represents the temporal structured effect, represents the temporal unstructured effect, δ ij represents the spatio-temporal interaction, variable 1 represents the first influencing variable in the selected combination of influencing variables, and variable 2 represents the second influencing variable in the selected combination of influencing variables.

[0134] Exemplarily, the influencing variable data may include 7 influencing variables, and the preset correlation threshold may be 0.4. Pearson correlation analysis is used to check the correlation between the 7 influencing variables pairwise. Strong correlation may lead to the problem of multicollinearity, affecting the calibration ability of the model. Therefore, the combination where the correlation coefficient of two variables is greater than 0.4 is not selected. In the total variable combinations, the combinations with correlation coefficients not meeting the requirements are screened out, and the remaining m 1 combinations are obtained, that is, the initial correlation combinations.

[0135] For the remaining m 1A variety of available combinations of influencing variables are added to the model for running (each combination combined with 6 interactions will generate 6 different models). The Deviance Information Criterion (DIC) is used for model screening: for the 6 models generated by each variable combination, select the one with the smallest DIC value. Screen out the models with DIC values greater than the original model, and select m models with smaller DIC values (ranked in the top 50%) from the remaining models. 2 models.

[0136] For the remaining m 2 models, the top 1 / 3 of the models are selected using the first weighted scoring: The models can be secondarily screened according to five indicators: the DIC value, the DIC saturation value, the number of effective parameters of the model, the marginal log-likelihood value, and the total model running time. The weight assignment and scoring rules can be as follows: 3 The DIC value (ω

[0137] = 0.5), the total model running time (ω DIC = 0.15), and the number of effective parameters of the model (ω Time = 0.1) are scored from low to high, and the marginal log-likelihood value (ω p.eff = 0.15) is scored from high to low. The scores are from high to low as m likelihood , m 2 , m 2 -1,..., 1; DIC saturation value: If the DIC value / DIC saturation value is greater than 0.5, the score is otherwise it is 1. The model score is p k is the score of the kth indicator.

[0138] The remaining m 3 models are subjected to sensitivity analysis using the second weighted scoring for the third screening. The scoring rules can be the same as above, and the index weight change rules can be: (i) the DIC weight range is [0.2, 0.5], and the DIC saturation value weight is fixed at 0.2; (ii) the other weight ranges are [0.1, 0.4]; (iii) all weight definitions and changes are in units of 0.1.

[0139] Exemplarily, the preset screening rule can be that the score ranking is on average in the top 50%.

[0140] For example, select m 4 models with better stability to weight changes (score ranking on average in the top 50%). Finally, considering the DIC improvement degree and the introduction of more variable quantities and types, the final model is selected from the m 4 models, and the final calibration model of the relative risk value of the resistance gene is further determined.

[0141] For example, in one embodiment of the present invention, the relative risk values of ARGs in 8 adjacent natural water basins after evaluation are extracted, the real data of the actual drinking water environment is obtained, and the relative risk values of ARGs are accurately calibrated. The final calibration model of the relative risk value of resistance genes is as follows:

[0142] Y ij = [γy ij ~ Poisson(λ ij )

[0143]

[0144] S6. Calibrate the relative risk value data of the target resistance gene using the final calibration model of the relative risk value of resistance genes, and visualize the calibrated relative risk value data of the target resistance gene.

[0145] In this embodiment, step S6 specifically includes:

[0146] S61. Calibrate the relative risk value data of the target resistance gene using the final calibration model of the relative risk value of resistance genes to obtain the model operation result;

[0147] S62. Extract the posterior mean in the model operation result as the relative risk calibration value of the relative risk value data of the target resistance gene;

[0148] S63. Extract the mean of the time random effect in the model operation result, draw a time series diagram, and visualize the calibrated relative risk value data of the target resistance gene.

[0149] Optionally, after step S62, the method further includes:

[0150] Compare the relative risk calibration value with the relative risk value data of the target resistance gene to determine the calibration accuracy of the final calibration model of the relative risk value of resistance genes.

[0151] Exemplarily, the mean of the time random effect extracted from the model operation result can be μ j , draw a time series diagram, the abscissa can be the time index 1, 2,..., T, and the ordinate can be the risk estimate value R j of the adjusted time j and the actual risk value E j :

[0152] R j = exp(μ j ) × S j

[0153]

[0154] where Sj represents the calculation result of the exponential expected value of the random effect in the model; RR raw.ji represents the relative risk value of the original ARGs at time j in region i.

[0155] In the present invention, the relative risk value of ARGs in the evaluated drinking water source water body is first extracted, and the relative risk value of ARGs, water intake time, water intake point information, and influencing variable data are preprocessed. Then, an adjacency matrix is constructed according to the adjacent relationship of each water body to further obtain a spatial information file. Next, a Bayesian spatio-temporal Poisson regression ARGs risk value calibration model including spatial and temporal random effects is established. Then, the spatial information file is added, and the INLA method is used for Bayesian inference. Further, according to the DIC criterion, etc., the influencing variable combination and spatio-temporal interaction are selected to obtain the final model. Finally, the final model is run to calibrate the relative risk value of ARGs in the spatio-temporal dimension under the consideration of uncertainty and visualize it, so as to achieve the precise calibration of the relative risk value of antibiotic resistance genes in the drinking water source.

[0156] Those of ordinary skill in the art will realize that the embodiments described herein are for helping the reader understand the principles of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations without departing from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the invention.

Claims

1. A method for calibrating the spatiotemporal dimension of the relative risk value of resistance genes in drinking water sources, characterized in that: The method comprises: S1. Obtaining the relative risk value data of resistance genes and the water intake time data, water intake point data and influencing variable data corresponding to the relative risk value data of resistance genes, and preprocessing the relative risk value data of resistance genes, the water intake time data, the water intake point data and the influencing variable data; the influencing variable data include variables related to land use in the water intake area and variables related to potential bacteria affecting the water body; S2. Construct a spatial adjacency matrix based on the preprocessed water intake point data to obtain a spatial information file; S3. Based on the model of spatial structured effect, the model of spatial unstructured effect, the first-order random walk model of temporal structured effect, the first-order random walk model of temporal structured effect, the model of temporal unstructured effect and the spatial-temporal interaction, and based on the Poisson distribution, a relative risk value calibration model of resistance genes including spatial and temporal random effects was constructed; S4, based on the pre-treated resistance gene relative risk value data, water intake time data, water intake point data, influencing variable data and the spatial information file, using an integrated nested Laplace approximation algorithm to perform Bayesian inference on the resistance gene relative risk value calibration model to obtain a trained resistance gene relative risk value calibration model; S5. According to the deviation information criterion, determine the final resistance gene relative risk value calibration model: Y ij =[γy ij ]~Poisson(λ ij ) Among them, Y ij represents the relative risk value of the resistance gene in region i at time j after scaling, y ij represents the relative risk value of the resistance gene corresponding to region i at time j, γ represents the coefficient used to scale the relative risk value, and λ ij represents the Poisson distribution parameter, b0 represents the bias term of the regression model, u i represents the spatial structuring effect, v i represents the spatial unstructured effect, γ j represents the time structuring effect, represents the time unstructured effect, δ ij represents the spatial-temporal interaction, variable 1 represents the first influencing variable in the selected influencing variable combination, and variable 2 represents the second influencing variable in the selected influencing variable combination; S6. Use the final resistance gene relative risk value calibration model to calibrate the target resistance gene relative risk value data, and visualize the calibrated target resistance gene relative risk value data.

2. The method according to claim 1, characterized in that The step S1 specifically includes: S11, acquiring the resistance gene relative risk value data, the water collection time data, the water collection point data and the influencing variable data from a resistance gene relative risk value database; S12, converting the resistance gene relative risk value data into an integer form to obtain pre-processed resistance gene relative risk value data; S13, converting the water intake time data and the water intake point data into numerical data to obtain pre-processed water intake time data and pre-processed water intake point data; S14. Use a K-nearest neighbor algorithm to perform weighted averaging on the influencing variable data to obtain preprocessed influencing variable data.

3. The method according to claim 1, characterized in that The step S2 specifically includes: S21, marking the pre-processed water intake point data in a water intake area map, and obtaining the neighboring relationship between the water bodies corresponding to the pre-processed water intake point data according to the relevant positions of the pre-processed water intake point data in the water intake area map; S22, constructing a spatial adjacency matrix according to the adjacent relationship; S23. Create an ASCII file corresponding to the spatial adjacency matrix according to the spatial adjacency matrix to obtain the spatial information file.

4. The method according to claim 1, characterized in that: The step S3 specifically includes: S31. Modeling the spatial structured effect using the intrinsic conditional autoregressive specification, the model of the spatial structured effect is: Among them, u i represents the spatial structured effect, u -i represents the set of spatial structured effects except region i, n represents the number of regions, μ i represents the mean of region i, μ j represents the mean of region j, N i represents the number of neighbors in region i, / N i is the variance of the same region, is the variance parameter, a ij If area i and area j are neighbors, a ij The value of is assigned 1, otherwise it is assigned 0; S32, performing independent and identically distributed Gaussian prior modeling on the spatial unstructured effect, the model of the spatial unstructured effect is: Among them, v i represents the spatial unstructured effect, represents the variance parameter; S33, using the first-order random walk and the second-order random walk to model the time structured effect respectively, the first-order random walk model of the time structured effect is: Among them, γ j represents the temporal structured effect, γ j-1 represents the time structured effect of the previous moment, σ1 2 represents the variance of the first-order random walk process; The second-order random walk model of the time structured effect is: Among them, γ j represents the temporal structured effect, γ j-1 represents the temporal structured effect of the previous moment, γ j-2 represents the time structured effect of the first two moments, represents the variance of the second-order random walk process; S34, performing independent and identically distributed Gaussian prior modeling on the temporal unstructured effect, wherein the model of the temporal unstructured effect is: in, represents the time unstructured effect, represents the variance parameter; S35, defining a spatiotemporal interaction according to different interactions of the spatial structured effect, the spatial unstructured effect, the temporal structured effect, and the temporal unstructured effect, and based on a first-order random walk model of the temporal structured effect and a second-order random walk model of the temporal structured effect; Among them, the space-time interactions are 6 types; S36. According to the model of the spatial structured effect, the model of the spatial unstructured effect, the first-order random walk model of the temporal structured effect, the first-order random walk model of the temporal structured effect, the model of the temporal unstructured effect and the spatiotemporal interaction, and based on Poisson distribution, a relative risk value calibration model for the resistance gene is established, and the relative risk value calibration model for the resistance gene is: Y ij =[γy ij ]~Poisson(λ ij ) Among them, Y ij represents the relative risk value of the resistance gene in region i at time j after scaling, y ij represents the relative risk value of the resistance gene corresponding to region i at time j, γ represents the coefficient used to scale the relative risk value, and λ ij represents the Poisson distribution parameter, b0 represents the bias term of the regression model, u i represents the spatial structuring effect, v i represents the spatial unstructured effect, γ j represents the time structuring effect, represents the time unstructured effect, δ ij Represents space-time interactions.

5. The method according to claim 4, characterized in that In step S4, the integrated nested Laplace approximation algorithm is used to perform Bayesian inference on the resistance gene relative risk value calibration model to obtain a trained resistance gene relative risk value calibration model, which specifically includes: S41, combining the model of the spatial structured effect and the model of the spatial unstructured effect to construct a Bessage-York-Molière model; S42. Based on the Bessage-York-Molier model, the second-order random walk model of the time structured effect and the model of the time unstructured effect, the integrated nested Laplace approximation algorithm is used to perform Bayesian inference on the resistance gene relative risk value calibration model to obtain the trained resistance gene relative risk value calibration model.

6. The method according to claim 4, characterized in that After step S4, the method further includes: The model of the spatial structured effect, the model of the spatial unstructured effect, the first-order random walk model of the temporal structured effect, the first-order random walk model of the temporal structured effect, the model of the temporal unstructured effect and the space-time interaction are inferred, and based on the space-time interaction, the deviation information amount of the trained resistance gene relative risk value calibration model corresponding to the space-time interaction is obtained respectively.

7. The method according to claim 6, characterized in that The step S5 specifically includes: S51, performing a correlation analysis on the influencing variable data using Pearson correlation analysis, and screening the results of the correlation analysis according to a preset correlation threshold to obtain an initial correlation combination; S52, respectively adding the initial correlation combination to the model of the spatial structured effect, the model of the spatial unstructured effect, the first-order random walk model of the temporal structured effect, the first-order random walk model of the temporal structured effect, the model of the temporal unstructured effect and the spatiotemporal interaction to obtain a set of relative risk value calibration models for candidate resistance genes; S53, using the deviation information criterion to screen the candidate resistance gene relative risk value calibration model set; S54, using the first weighted score to perform a second screening on the screened candidate resistance gene relative risk value calibration model set; S55, using the second weighted score to perform sensitivity analysis on the relative risk value calibration model set of candidate resistance genes after the second screening, and perform a third screening; S56, performing a fourth screening on the candidate resistance gene relative risk value calibration model set after the third screening according to a preset screening rule, and determining a final resistance gene relative risk value calibration model set; S57. Determine the final resistance gene relative risk value calibration model based on the final resistance gene relative risk value calibration model set.

8. The method according to claim 1, characterized in that The step S6 specifically includes: S61, using the final resistance gene relative risk value calibration model to calibrate the target resistance gene relative risk value data to obtain a model operation result; S62, extracting the posterior mean in the model operation result as the relative risk calibration value of the relative risk value data of the target resistance gene; S63. Extract the mean of the time random effect in the model operation results, draw a time series graph, and visualize the calibrated target resistance gene relative risk value data.

9. The method according to claim 8, characterized in that After step S62, the method further includes: The relative risk calibration value is compared with the target resistance gene relative risk value data to determine the calibration accuracy of the final resistance gene relative risk value calibration model.

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