A visualization method for epidemic virus field based on spatiotemporal trajectory data

Abstract

This invention belongs to the field of infectious disease prevention technology, specifically relating to a method for visualizing infectious disease virus fields based on spatiotemporal trajectory data. The method includes: collecting multi-dimensional data related to infectious diseases, including spatiotemporal trajectory correlation data, virus detection data, and patient-related data (based on data from children with diarrhea at Tianjin Children's Hospital from July 2020 to August 2023); cleaning, standardizing, coding, and statistically analyzing the data; constructing a spatiotemporal distribution field, a genotype distribution field, and a risk correlation field based on a spatiotemporal axis; and visually presenting the data using a combination of various charts such as bar charts, line charts, statistical tables, and phylogenetic trees, combined with an interactive interface. This method integrates multi-source data to accurately display the spatiotemporal epidemic characteristics of infectious diseases, the evolutionary patterns of viral genotype distribution, and the correlation between genotypes and patient characteristics. All results are based on measured data, providing scientific support for infectious disease prevention, control, and treatment, and improving the efficiency and accuracy of data application.

Description

Technical Field

[0001] This invention belongs to the field of infectious disease prevention technology, specifically relating to a method for visualizing infectious disease virus fields based on spatiotemporal trajectory data. Background Technology

[0002] Infectious diseases, such as acute gastroenteritis caused by norovirus, are characterized by high infectivity, rapid spread, and wide impact, posing a serious threat to public health and population health. Taking norovirus as an example, as one of the leading pathogens of acute diarrhea in children worldwide, its infection rate varies significantly across different age groups and seasons. Furthermore, the virus exhibits genotype diversity and dynamic spatiotemporal epidemic changes. Studies show that children aged 13-36 months are a high-risk group for norovirus infection, with winter and spring being peak infection periods. The predominant circulating strains are genotypes such as GII.4[P16], GII.3[P12], and GII.2[P16], and there are correlations and differences in the epidemic periods, susceptible populations, and clinical characteristics among different genotypes. Accurately understanding the viral transmission patterns, spatiotemporal distribution characteristics, and influencing factors of infectious diseases is crucial for developing scientific prevention and control strategies and reducing the harm of epidemics.

[0003] With the application of big data technology in the field of public health, infectious disease surveillance data is becoming increasingly abundant, covering multi-dimensional information such as patient clinical information (age, gender, symptoms, etc.), laboratory test data (viral genotype, nucleic acid sequence, etc.), and spatiotemporal trajectory data (location of visits, epidemic area, time series, etc.). However, existing methods for visualizing infectious disease virus-related data still have many shortcomings: Insufficient Dimensional Integration: Traditional visualization methods often focus on displaying single-dimensional data, such as only presenting changes in infection rates over time or case distribution in space. They fail to effectively integrate spatiotemporal trajectories with multi-dimensional data such as viral genotypes, population characteristics, and clinical symptoms, making it difficult to intuitively reflect the correlation between virus transmission and spatiotemporal, host, and virus characteristics. For example, current technologies cannot clearly demonstrate the spatiotemporal spread paths and aggregation characteristics of specific norovirus genotypes in different seasons and age groups.

[0004] Lack of Dynamic Presentation: The spread and prevalence of viruses is a dynamic process, with the intensity of transmission, the scope of impact, and the types of circulating strains changing dynamically over different periods. Existing visualization methods are mostly static displays (such as case distribution maps for fixed time periods and genotype percentage statistics tables), which are insufficient to dynamically present the spatiotemporal evolution of the viral field (including the transmission intensity field, genotype distribution field, risk level field, etc.), and cannot meet the needs of public health personnel for real-time tracking of the epidemic's development trend.

[0005] Limited depth of information mining: Existing methods mostly remain at the surface level of data, failing to reveal deeper connections between viral spatiotemporal distribution and genotype variation, population susceptibility differences, and environmental factors through visualization. For example, it is impossible to intuitively identify spatiotemporal hotspots of high-risk genotypes through visualization, nor is it easy to clearly demonstrate the spatiotemporal correlation characteristics between viral genotypes and factors such as patient age, hospitalization duration, and clinical symptoms, thus failing to fully realize the application value of monitoring data.

[0006] Insufficient support for prevention and control: Due to the lack of multi-dimensional and dynamic virus field visualization tools, public health decision-makers find it difficult to quickly and accurately grasp the epidemic situation. For example, they cannot identify key nodes of virus transmission, high-risk groups, and their spatiotemporal range in a timely manner, which affects the pertinence and timeliness of prevention and control measures and is not conducive to rapid response and effective control of the epidemic.

[0007] Therefore, given the shortcomings of existing methods for visualizing viral data of infectious diseases in terms of multi-dimensional integration, dynamic presentation, in-depth information mining, and prevention and control support, there is an urgent need for a method for visualizing viral fields of infectious diseases based on spatiotemporal trajectory data. This method should be able to comprehensively integrate multi-source data and intuitively and dynamically display the spatiotemporal distribution characteristics and correlation patterns of the viral field, providing scientific and effective technical support for the monitoring, early warning, and prevention and control decisions of infectious diseases. Summary of the Invention

[0008] The purpose of this invention is to provide a method for visualizing viral fields of infectious diseases based on spatiotemporal trajectory data, which realizes the deep integration and accurate presentation of multi-dimensional data, and effectively solves the problems of single dimension and insufficient correlation in traditional infectious disease data visualization.

[0009] The objective of this invention is achieved through the following technical solution: This invention provides a method for visualizing viral fields of prevalent infectious diseases based on spatiotemporal trajectory data, comprising the following steps: S1. Collect multi-dimensional data related to infectious diseases, including spatiotemporal trajectory correlation data, viral nucleic acid detection data, genotyping data, patient basic information, clinical symptoms and signs data, and laboratory test data. S2. Clean, standardize, encode, and perform statistical analysis on the multi-dimensional data; S3. Based on the time axis and spatial axis of the spatiotemporal trajectory correlation data, construct a viral field of prevalent infectious diseases that includes at least a spatiotemporal distribution field; S4. Visualize the viral field of the prevalent infectious disease through various charts.

[0010] Furthermore, the spatiotemporal trajectory association data includes patient consultation time data and consultation space data. The consultation time data is divided into year-season-month hierarchical data, and the seasons are divided into spring (March-May), summer (June-August), autumn (September-November), and winter (December-February of the following year).

[0011] Furthermore, the virus detection data includes viral nucleic acid detection results, genotyping results, and homology analysis data. The homology analysis data consists of nucleotide sequence alignment data between representative strains of the viral ORF1 region and / or Capsid region and the corresponding reference strains downloaded from the National Center for Biotechnology Information (NCBI).

[0012] Furthermore, the statistical analysis in step S2 uses the χ² method. 2 For the test, set P < 0.05 as statistically significant, and record the corresponding χ² values. 2 Value, Z-value, and P-value.

[0013] Furthermore, the epidemic infectious disease virus field mentioned in step S3 also includes a genotype distribution field and a risk association field; the spatiotemporal distribution field superimposes virus positivity rate and case number data, the genotype distribution field incorporates viral genotype epidemic data, and the risk association field establishes an association matrix between genotype and patient-related factors.

[0014] Furthermore, the genotyping results are norovirus typing data of type GII, specifically including GII.4[P16], GII.3[P12], GII.2[P16], GII.4[P31], GII.6[P7], GII.17[P17], GII.10[P16], GII.2[P31] and GII.1[P16].

[0015] Furthermore, the patient-related factors include patient age, gender, length of hospital stay, clinical symptoms, and laboratory test indicators; the clinical symptoms include at least abdominal pain, and the laboratory test indicators include at least neutrophil count, C-reactive protein, procalcitonin, and aspartate transferase.

[0016] Furthermore, the RdRp region gene sequences of the representative strains of the ORF1 region are clustered and distributed on the CⅡ.Pe, GⅢ.P12, CⅢ.P17, and CⅡ.P16 branches, and the gene sequences of the representative strains of the Capsid region are clustered and distributed on the GⅡ.1, CⅢ.2, GⅡ.3, GⅡ.4 Sydney 2012, and GⅢ.17 branches.

[0017] Furthermore, the various charts mentioned in step S4 include bar charts, line charts, statistical tables, phylogenetic trees, and heatmaps.

[0018] Furthermore, the visualization presentation in step S4 also includes an interactive interface, which supports viewing related data by hierarchical filtering based on "spatiotemporal dimension - genotype - clinical symptoms - laboratory indicators".

[0019] The beneficial effects of this invention are as follows: The core advantage of this invention, which provides a method for visualizing viral fields of infectious diseases based on spatiotemporal trajectory data, lies in its deep integration and accurate presentation of multi-dimensional data, effectively solving the problems of single-dimensionality and insufficient correlation in traditional infectious disease data visualization. This method uses spatiotemporal trajectory as the core link, systematically integrating viral detection data of infectious diseases, basic patient information, clinical symptom and sign data, and laboratory test data. Through standardized preprocessing and statistical analysis (such as χ²), it further enhances the visualization capabilities of these data. 2 (Verification) ensures data reliability. Then, by constructing spatiotemporal distribution fields, genotype distribution fields, and risk association fields, the scattered multi-source data is transformed into a logically connected organic whole. The visualization process employs a combination of various charts, including bar charts, line charts, statistical tables, phylogenetic trees, and heatmaps, to comprehensively present the spatiotemporal characteristics of infectious disease outbreaks (such as differences in infection rates across different ages and seasons), the distribution patterns of viral genotypes (such as the prevalence intensity and spatiotemporal evolution trends of the nine GII norovirus types), the correlation between genotypes and patient characteristics (such as the susceptibility characteristics of children aged 13-36 months to GII.4 virus), and the homology clustering characteristics of viral genes. All presented results strictly correspond to measured statistical data and verification results, ensuring the accuracy and comprehensiveness of information transmission. This allows users to intuitively grasp the core patterns of infectious disease outbreaks, avoiding the one-sidedness of information caused by single-dimensional data presentation.

[0020] This invention has significant application value, providing strong technical support for public health prevention and control and clinical diagnosis and treatment. From a public health perspective, this method, by dynamically displaying the spatiotemporal distribution hotspots of infectious diseases, the evolution trajectory of circulating strains, and the characteristics of high-risk groups, can help prevention and control personnel accurately identify key nodes in the spread of the epidemic (such as the peak periods of norovirus in winter and spring), high-risk groups (such as children aged 13-36 months), and dominant circulating strains (such as GII.4[P16] and GII.3[P12]), providing a scientific basis for formulating targeted prevention and control strategies and optimizing resource allocation. At the same time, the visualization of viral gene homology can help trace the source of virus transmission and evolutionary path, improving the foresight and effectiveness of epidemic prevention and control. From a clinical diagnosis and treatment perspective, this method clearly presents the correlation between different viral genotypes and clinical symptoms (such as the association between GII.3[P12] and GII.2[P16] genotypes and abdominal pain symptoms) and laboratory indicators (such as differential indicators such as neutrophil count and CRP), providing a reference for clinicians to quickly judge the condition and formulate personalized treatment plans. In addition, the interactive visualization interface supports multi-dimensional and hierarchical filtering, which greatly improves the efficiency of data query and analysis, enabling different users such as public health personnel and clinicians to obtain accurate information as needed, giving full play to the application value of monitoring data, and providing practical guarantees for the scientific prevention and control and precise diagnosis and treatment of infectious diseases. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a graph showing the number of norovirus patients and the norovirus positivity rate in each season in Example 1 of the present invention; Figure 2 This is a graph showing the norovirus genotyping results and seasonal genotype distribution in Example 1 of the present invention. Figure 3 This is a nucleotide phylogenetic tree diagram of the ORF1 / VP1 region of the norovirus-affected children hospitalized in Example 1 of the present invention. Detailed Implementation

[0023] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0024] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0025] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0026] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0027] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0028] The present invention will be further illustrated below through examples.

[0029] Example 1: A method for visualizing norovirus fields based on spatiotemporal trajectory data This embodiment uses norovirus-induced acute gastroenteritis as the research object. Based on multi-dimensional data of children with diarrhea who visited Tianjin Children's Hospital from July 2020 to August 2023, it visualizes the norovirus field. The specific implementation process is as follows: 1. Data Collection The data collected in this embodiment covers the child's basic information, clinical information, laboratory test data, and spatiotemporal trajectory correlation data, as detailed below: Basic information: Gender and age information of 6714 children with diarrhea, including 3929 males (58.52%) and 2785 females (41.48%); age grouping: <7 months 1220 cases (18.17%), 7-12 months 705 cases (10.50%), 13-36 months 2877 cases (42.85%), 37-72 months 1081 cases (16.10%), >72 months 831 cases (12.38%).

[0030] Spatiotemporal correlation information: The seasonal division of the children's consultation time (spring March-May, summer June-August, autumn September-November, winter December-February of the following year), of which 2163 cases (32.22%) were in spring, 1767 cases (26.32%) in summer, 1171 cases (17.44%) in autumn, and 1613 cases (24.02%) in winter; the consultation space was fixed as Tianjin Children's Hospital, which was used to construct the basic dimension of the spatiotemporal trajectory.

[0031] Laboratory test data: Viral nucleic acid test results: Among 6,714 fecal samples from infected children, 1,395 were positive for norovirus, with a detection rate of 20.78%. Genotyping data: Primer amplification and genotyping were performed on the overlapping regions of norovirus polymerase (ORF1) and capsid region (VP1) in 187 positive specimens. 155 cases were successfully genotyped, all of which were GII type, including 9 genotypes: GII.4[P16], GII.3[P12], GII.2[P16], GII.4[P31], GII.6[P7], GII.17[P17], GII.10[P16], GII.2[P31], and GII.1[P16]. Homology analysis data: Ten representative strains with successful ORF1 region sequencing (covering 8 genotypes) and 16 representative strains with successful Capsid region sequencing (covering 5 genotypes) were randomly selected. The corresponding genotype reference strains were downloaded from the National Center for Biotechnology Information (NCBI) for sequence alignment to obtain nucleotide homology data. Among them, the RdRp region gene sequences of the 10 ORF1 region representative strains were highly homologous and clustered, distributed in the CⅡ.Pe, GⅢ.P12, CⅢ.P17, and CⅡ.P16 branches, respectively. The Capsid region gene sequences of the 16 Capsid region representative strains were highly homologous and clustered, distributed in the GⅡ.1, CⅢ.2, GⅡ.3, GⅡ.4Sydney2012, and GⅢ.17 branches, respectively. Routine clinical testing indicators include: white blood cell count, neutrophil count, lymphocyte count, monocyte count, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (Il-6), ferritin (FER), aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH), etc. (see Table 3 for specific statistics).

[0032] Clinical symptoms and signs data: the occurrence of symptoms such as fever (including temperature grade: 38.1-39℃, 39.1-40℃, >40℃), vomiting (including vomiting frequency grade: 1-2 times / day, 3-4 times / day, ≥5 times / day), diarrhea (including diarrhea frequency grade: 1-2 times / day, 3-4 times / day, ≥5 times / day), abdominal pain, pharyngeal congestion, cough, sputum, wheezing, chills, convulsions, etc., and the length of hospital stay (graded: 0-5 days, 6-10 days, 11-15 days, >15 days).

[0033] 2. Data Preprocessing The collected data is cleaned, and samples lacking key information (such as gender, age, and test results) are removed to ensure data integrity. The categorical data were standardized and coded, such as by gender (Male=1, Female=2), age group (<7m=1, 7~12m=2, 13~36m=3, 37~72m=4, >72m=5), season (Spring=1, Summer=2, Fall=3, Winter=4), genotype (coded according to 9 subtypes), and symptoms and signs (present=1, absent=0), etc. For continuous data from laboratory tests (such as white blood cell count, CRP value, etc.), retain the original distribution characteristics and perform statistical analysis based on the median and quartiles; The χ² test was used to perform statistical analysis on categorical data (such as differences in positive rates among different genders, ages, and seasons, and the association between different genotypes and symptoms). Appropriate statistical methods were used to analyze continuous data. P < 0.05 was set as statistically significant. The χ² value, Z value, and P value corresponding to each test were recorded.

[0034] 3. Norovirus Field Construction Based on the core dimension of spatiotemporal trajectory, and integrating the above multi-dimensional data, three types of norovirus fields are constructed: Spatiotemporal distribution field: Using "time (year-season-month)-space (related to the treatment area of ​​Tianjin Children's Hospital)" as the two axes, and superimposing norovirus positivity rate and number of positive cases data, a dynamic spatiotemporal distribution matrix is ​​formed, which includes the positivity rate distribution characteristics of different age groups and different seasons; Genotype distribution field: Based on the spatiotemporal axis, the prevalence data of 9 genotypes are incorporated to construct a genotype-spatiotemporal correlation matrix, clarifying the prevalence intensity and changing trend of each genotype in different years and seasons; Risk association field: Establish association matrices between genotype and age, clinical symptoms, and laboratory indicators, as well as association matrices between symptoms and laboratory indicators, and quantify the association strength between each factor (based on statistical tests using χ², Z, and P values).

[0035] 4. Visual presentation A multi-chart visualization approach is used to comprehensively showcase the characteristics and correlation patterns of norovirus outbreaks: Spatiotemporal distribution visualization: Plot a bar chart of norovirus positivity rate and case count by season (e.g.) Figure 1 The results visually demonstrate the differences in positive rates between spring (27.97%), summer (11.04%), autumn (12.04%), and winter (28.15%). Plotting the positivity rate change curves for the year-month series reveals the differences in infection trends: a smooth trend and a higher infection rate in the fall of 2020, and a sudden increase in the infection rate from fall to winter in 2021-2022. A comparison chart of positive rates by age group was drawn, highlighting the highest positive rate (27.60%) in the 13-36 month age group and its distribution characteristics of accounting for 56.92% of all positive cases; A statistical table showing the positive results of norovirus in children of different genders, ages and seasons (as shown in Table 1) clearly displays the number of people, percentages, positive numbers, positive rates and corresponding χ² values ​​and P values ​​for each group.

[0036] Table 1. Norovirus positive results in children of different genders, ages, and seasons.

[0037] Genotype distribution visualization: Draw a pie chart of genotype percentages (e.g.) Figure 2 (a) shows the distribution characteristics of GII.4[P16] (38.71%), GII.3[P12] (27.56%), and GII.2[P16] (18.47%) as the main prevalent strains; Plot a line graph showing the dynamic distribution of genotypes by year and season (e.g.) Figure 2 (b) shows the time evolution pattern of GII.2 [P12] concentrated outbreak in spring of 2021-winter of 2022, GII.4 [P16] prevalent in 2020, rare in 2021-2022, re-emerging in 2023, and GII.2 [P16] mainly prevalent in 2020; Genotype-age group association heatmaps were plotted to show the association characteristics of susceptibility to GII.4 type (74.42%) and GII.4[P16] type (53.49%) in children aged 12-35 months, and susceptibility to GII.2[P16] type (41.18%) in children aged 8-9 years. The distribution of norovirus-positive genotypes by age, sex, and length of hospital stay is presented in Table 2, which clarifies the distribution of each genotype in different groups and the corresponding χ² and P values.

[0038] Table 2. Distribution of norovirus-positive children by age, sex, and length of hospital stay.

[0039] Risk correlation visualization: A table showing the correlation between genotype, clinical symptoms, and laboratory indicators (as shown in Table 3) was created, clearly displaying the probability of symptom occurrence, statistical values ​​of laboratory indicators, and corresponding Z / χ² and P values ​​for the three genotypes GⅡ.4[P16], GⅡ.3[P12], and GⅡ.2[P16]. The table highlights that the probability of abdominal pain in children with the GⅡ.3[P12] and GⅡ.2[P16] genotypes (37.21% and 37.93%, respectively) was significantly higher than that of children with the GⅡ.4[P16] genotype (13.33%) (χ²=9.814, P=0.007). Box plots were constructed to correlate genotype with laboratory indicators, showing significant differences in neutrophil count (χ²=6.478, P=0.039), CRP (χ²=6.743, P=0.034), PCT (χ²=6.685, P=0.035), and AST (χ²=8.229, P=0.016) among different genotype groups. Plotting the nucleotide phylogenetic tree of the ORF1 / VP1 region (e.g.) Figure 3 The homology clustering characteristics of the Tianjin strain (marked with ●) and the reference strain are visualized, where: The seven representative strains of type GII.Pe in region ORF1 showed 92.8%-99.0% homology and 93.5%-99.0% homology with the reference strain KR904765; the five representative strains of type GII.P12 showed 94.4%-99.3% homology and 93.2%-99.3% homology with the reference strain AB880922; the one strain of type CII.P17 showed 97.8% homology with the reference strain LC037415; and the four representative strains of type GII.P16 showed 95.1%-100.0% homology and 93.5%-100.0% homology with the reference strain LC215415. Two GII.1 representative strains in the Capsid region showed 98.3% homology, with 92.0%-98.3% homology to the reference strain U07611; four GII.2 representative strains showed 95.2%-99.7% homology, with 97.7%-99.7% homology to the reference strain MH321823; four GII.3 representative strains showed 92.6%-100.0% homology, with 92.6%-100.0% homology to the reference strain MG763335; five GII.4 Sydney2012 representative strains showed 91.2%-94.5% homology, with 91.2%-96.3% homology to the reference strain JX459908; and one GII.17 strain showed 95.5% homology to the reference strain MG517345.

[0040] Table 3. Association between symptoms, signs, and laboratory test results of children with norovirus genotypes.

[0041] Comprehensive information visualization: The interface is designed to be interactive and visual, allowing users to filter and view data hierarchically by "spatiotemporal dimension - genotype - symptoms - laboratory indicators," and to view Tables 1, 2, 3, and more. Figure 1 , Figure 2 , Figure 3 The associated data enables flexible display of multi-dimensional information about norovirus outbreaks; A homology analysis data panel is embedded in the interface, which can be clicked to view the specific values ​​of nucleotide homology between representative strains of each genotype and the reference strain, as well as details of the phylogenetic tree branches.

[0042] 5. Result Verification The visualization results in this embodiment are all verified by statistical data and experimental results in the disclosure materials: The spatiotemporal distribution visualization results are consistent with the positive rate statistics for different genders, ages, and seasons in Table 1. The χ² test results all meet the corresponding P-value requirements (gender P=0.698, age P<0.001, season P<0.001). The visualization of genotype distribution accurately reflects the genotype composition and temporal evolution of 155 successfully genotyped samples, and... Figure 2 The typing results were a perfect match, and the association statistics between genotype and age, sex, and length of hospital stay in Table 2 showed no bias. The differences in symptoms and laboratory indicators in the risk association visualization all correspond to the statistical test results clearly stated in Table 3 (P < 0.05 or P > 0.05). Among them, the intergroup differences in abdominal pain, neutrophil count, CRP, PCT, and AST were all < 0.05, while the other indicators showed no significant differences and no additional inferences were made. The phylogenetic tree visualization strictly follows the sequencing results of 10 representative strains of the ORF1 region and 16 representative strains of the Capsid region, as well as the homology comparison data with the NCBI reference strain, accurately presenting the clustering branch characteristics and homology percentage range of each genotype.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for visualizing viral fields of prevalent infectious diseases based on spatiotemporal trajectory data, characterized in that, Includes the following steps: S1. Collect multi-dimensional data related to infectious diseases, including spatiotemporal trajectory correlation data, viral nucleic acid detection data, genotyping data, patient basic information, clinical symptoms and signs data, and laboratory test data. S2. Clean, standardize, encode, and perform statistical analysis on the multi-dimensional data; S3. Based on the time axis and spatial axis of the spatiotemporal trajectory correlation data, construct a viral field of prevalent infectious diseases that includes at least a spatiotemporal distribution field; S4. Visualize the viral field of the prevalent infectious disease through various charts.

2. The method according to claim 1, characterized in that, The spatiotemporal trajectory association data includes patient visit time data and visit space data. The visit time data is divided into year-season-month hierarchical data, and the seasons are divided into spring (March-May), summer (June-August), autumn (September-November), and winter (December-February of the following year).

3. The method according to claim 1, characterized in that, The virus detection data includes viral nucleic acid detection results, genotyping results, and homology analysis data. The homology analysis data consists of nucleotide sequence alignment data between representative strains of the viral ORF1 region and / or Capsid region and the corresponding reference strains downloaded from the National Center for Biotechnology Information (NCBI).

4. The method according to claim 1, characterized in that, The statistical analysis in step S2 uses the χ² method. 2 For the test, set P < 0.05 as statistically significant, and record the corresponding χ² values. 2 Value, Z-value, and P-value.

5. The method according to claim 1, characterized in that, The infectious disease virus field mentioned in step S3 also includes a genotype distribution field and a risk association field; the spatiotemporal distribution field overlays virus positivity rate and case number data, the genotype distribution field incorporates viral genotype epidemic data, and the risk association field establishes an association matrix between genotype and patient-related factors.

6. The method according to claim 3, characterized in that, The genotyping results are the genotyping data of GII type norovirus, specifically including GII.4[P16], GII.3[P12], GII.2[P16], GII.4[P31], GII.6[P7], GII.17[P17], GII.10[P16], GII.2[P31] and GII.1[P16].

7. The method according to claim 5, characterized in that, The patient-related factors include patient age, gender, length of hospital stay, clinical symptoms, and laboratory test indicators; the clinical symptoms include at least abdominal pain, and the laboratory test indicators include at least neutrophil count, C-reactive protein, procalcitonin, and aspartate transferase.

8. The method according to claim 3, characterized in that, The RdRp region gene sequences of the representative strains of the ORF1 region clustered together and were distributed on the CⅡ.Pe, GⅢ.P12, CⅢ.P17, and CⅡ.P16 branches, while the gene sequences of the representative strains of the Capsid region clustered together and were distributed on the GⅡ.1, CⅢ.2, GⅡ.3, GⅡ.4 Sydney 2012, and GⅢ.17 branches.

9. The method according to claim 1, characterized in that, The various charts mentioned in step S4 include bar charts, line charts, statistical tables, phylogenetic trees, and heatmaps.

10. The method according to claim 1, characterized in that, The visualization presentation in step S4 also includes an interactive interface, which supports viewing related data by the hierarchy of "spatiotemporal dimension - genotype - clinical symptoms - laboratory indicators".

Images