Methods for Constructing Disease Transmission Prediction Models Based on Comprehensive Spatiotemporal Distance Between Cities

Abstract

This invention provides a method for constructing a disease transmission prediction model based on the comprehensive spatiotemporal distance between cities. The method includes: matching station names with cities in multiple cities at the prefecture-level using station names from train timetable data; classifying trains by type based on train number names; calculating the number of direct trains between cities based on the train operation relationships and train types; calculating the train travel time between cities based on the train operation relationships; calculating the comprehensive spatiotemporal distance between cities based on the number of direct trains and train travel time; and fitting a disease transmission speed prediction model using the comprehensive spatiotemporal distance and direct distance between cities as independent variables and the disease transmission data between cities as the dependent variable. This embodiment can accurately predict disease transmission patterns simultaneously from both temporal and spatial dimensions.

Description

Technical Field

[0001] This invention relates to the fields of geographic information processing and disease prediction technology, and in particular to a method for constructing a disease transmission prediction model based on the comprehensive spatiotemporal distance between cities. Background Technology

[0002] With the development of intercity transportation, intercity rail transit, represented by railways, has become a major route for the spread of infectious diseases. How to use traffic data to predict the speed of disease transmission, in order to rationally allocate disease control resources and implement control measures, is an urgent problem to be solved.

[0003] In the prior art, patent application CN110147419A discloses a method and system for analyzing the spread of infectious diseases based on subway space, and CN114496265A discloses a method and system for modeling the spatiotemporal spread of infectious diseases within cities. However, the subway card swiping data and mobile phone location data they utilize are both users' private data, which presents difficulties in terms of data availability and temporal sequence.

[0004] In view of this, the present invention is proposed. Summary of the Invention

[0005] This invention provides a method for constructing a disease transmission prediction model based on the comprehensive spatiotemporal distance between cities, utilizing open railway traffic data to predict disease transmission patterns.

[0006] In a first aspect, embodiments of the present invention provide a method for constructing a disease transmission prediction model based on comprehensive spatiotemporal distance between cities, including:

[0007] Based on the station names in the train timetable data, the stations are matched with cities in multiple cities at the prefecture-level, thereby constructing the train operation relationship between cities.

[0008] Trains are classified into different types based on their train number names;

[0009] Based on the train operation relationships and train types between cities, calculate the number of direct trains between each city.

[0010] Calculate the train travel time between cities based on the train operation relationships between them.

[0011] The comprehensive time and space distance between cities is calculated based on the number of direct trains and the train running time between each city.

[0012] Using the comprehensive spatiotemporal distance and direct distance between cities as independent variables and the disease transmission data between cities as dependent variables, a disease transmission speed prediction model was fitted.

[0013] In a second aspect, embodiments of the present invention provide an electronic device, the electronic device comprising:

[0014] One or more processors;

[0015] Memory, used to store one or more programs.

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for constructing a disease transmission prediction model based on the comprehensive spatiotemporal distance between cities as described in any embodiment.

[0017] In summary, this invention provides a disease medical method based on comprehensive spatiotemporal distance between cities. Based on train timetable data, it fully explores the temporal and spatial characteristics of train operations between cities, integrating information from different types of trains, including high-speed rail, bullet trains, and regular trains, to obtain the comprehensive spatiotemporal distance between cities. This distance takes into account both train travel time and spatial train frequency, reflecting the combined effects of timeliness and accessibility in actual railway transportation networks, and better reflecting the characteristics of population flow between cities. Based on these characteristics, this embodiment uses historical disease transmission data to fit the relationship between the speed of disease transmission between cities and the comprehensive spatiotemporal distance. The resulting disease prediction model can more accurately reflect the patterns of disease transmission between cities, providing data support for targeted allocation of disease control resources and the implementation of disease control measures. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a method for constructing a disease transmission prediction model based on comprehensive spatiotemporal distance between cities, provided by an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0022] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0023] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0024] Figure 1 This is a flowchart illustrating a method for constructing a disease transmission prediction model based on comprehensive spatiotemporal distance between cities, as provided in an embodiment of the present invention. This method is applicable to predicting the speed at which a disease spreads from its source city to surrounding cities and is executed by electronic equipment. Figure 1 As shown, the method specifically includes:

[0025] S110. Based on the station names in the train timetable data, match the stations with cities in multiple cities at the prefecture-level scale to construct the train operation relationship between cities.

[0026] This step first obtains an open-source train timetable as the data source for the entire method. Then, based on this data source, it matches stations with cities, including location matching and time matching, which together constitute the train operation relationships between cities.

[0027] Optionally, firstly, the names of the stations along the route of each train are extracted from the train timetable data; then, based on the station names, the cities where each station is located are found, and a correspondence is established between each station and its city; finally, based on the correspondence, the time for each train to pass through each city is determined; the correspondence and the time together constitute the train operation relationship between the cities.

[0028] S120. Classify trains by type according to their train number names.

[0029] In this embodiment, train types include high-speed trains, bullet trains, and regular trains. Train types can be classified using letters in the train number.

[0030] Optionally, train numbers beginning with the letter G will be classified as high-speed rail; train numbers beginning with the letter D or C will be classified as bullet trains; and train numbers beginning with other letters (including P, K, T, Z, L, Y, N, A, etc.) will be classified as regular trains.

[0031] S130. Based on the train operation relationship and train type between cities, calculate the number of direct trains between each city.

[0032] Based on the data obtained from S110 and S120, this step calculates the number of direct train services between each pair of cities.

[0033] Optionally, taking any pair of cities a and b as an example, the number of direct trains between cities a and b can be calculated using the following formula:

[0034] SF C,ab =SF G,ab +SF D,ab +SF A,ab (1)

[0035] Among them, SF G,ab SF represents the number of direct high-speed rail lines between city a and city b. D,ab SF represents the number of direct high-speed trains between city a and city b. A,ab SF represents the number of direct routes between city a and city b. C,ab This represents the total number of direct trains of the three types between city a and city b.

[0036] S140. Calculate the train running time between cities based on the train operation relationship between each city.

[0037] The train travel time between cities represents the time distance between them. The longer the distance, the easier it is for diseases to spread between cities. In one specific implementation, the train travel time can be calculated in two ways:

[0038] Scenario 1: Direct trains exist between cities. In this case, the train travel time between cities can be calculated using the following formula:

[0039] MT i,ab =Tb i,arrive -Ta i,start (2)

[0040] Among them, MT i,ab Tb represents the travel time of train i from city a to city b. i,arriveTa represents the time when train i arrives in city b. i,start This indicates the time when train i departs from city a.

[0041] Scenario 2: No direct trains exist between the cities. Considering intra-city transfers, for cities a and b, we first select transfer city c that meets the following conditions:

[0042] Condition 1: There are direct trains between city A and transit city C, and between transit city C and city A.

[0043] Condition 2: When transferring within the same station, the transfer time is greater than 30 minutes; when transferring between different stations, the transfer time is greater than 90 minutes.

[0044] Then, based on the above transit cities, the shortest travel time from city a to city b via transit is calculated using the following formula:

[0045] T i,ac =Tc i,arrive -Ta i,start (3)

[0046] T j,cb =Tb j,arrive -Tc j,start (4)

[0047] T k,ab =T i,ac +T ij +T j,cb (5)

[0048] MT ab =min{T 1,ab ,T 2,ab ,,T k,ab} (6)

[0049] Among them, T i,ac Tc represents the travel time of train i from city a to transit city c. i,arrive This represents the time it takes for train i to reach the transit city c. i,start T represents the time when train i departs from city a; j,cb Tb represents the travel time of train j from transit city c to city a. j,arrive Tc represents the time it takes for train j to reach city a. j,start Indicates the time when train j departs from transit city c; T k,ab T represents the total time taken to travel from city a to city b via transit city c according to transit plan k. ij This represents the time taken to transfer from train i to train j; MF abThis represents the shortest travel time from city a to city b via a transfer. In each transfer route k, train i, train j, and transfer city c can be different.

[0050] S150. Based on the number of direct trains and the train running time between cities, calculate the comprehensive spatiotemporal distance between cities, thereby completing the construction of a comprehensive intercity transportation network.

[0051] This embodiment proposes a spatiotemporal distance between cities, comprehensively measuring the relationship between cities from both temporal and spatial dimensions. This distance reflects the ease with which diseases spread between cities, is inversely proportional to the frequency of train operations between cities, and directly proportional to the average travel time of trains between cities.

[0052] Optionally, the spatiotemporal distance between cities can be calculated separately for different types of trains. Taking the spatiotemporal distance of high-speed rail as an example, the specific calculation method is as follows:

[0053]

[0054] Among them, STD G,ab T represents the high-speed rail distance between city a and city b. G,ab SF represents the average travel time between city a and city b by high-speed rail. G,ab MT represents the number of direct high-speed train services between city a and city b. i,ab MT represents the travel time of high-speed train i between city a and city b. ab This represents the shortest transfer time between city A and city B in the high-speed rail network. The corresponding time and space distances for other types of trains are similar.

[0055] Based on the above methods, the comprehensive spatiotemporal distance between cities can be further calculated. This distance also reflects the ease with which diseases spread between cities, and it takes into account various train types. The specific calculation method is as follows:

[0056]

[0057] Among them, STD C,ab SF represents the combined spatiotemporal distance between city a and city b. C,ab SF represents the total number of direct train services between city a and city b. G,ab SF represents the number of direct high-speed train services between city a and city b. D,ab SF represents the number of direct high-speed train services between city a and city b. A,ab MT represents the number of direct trains between city a and city b. G,i,ab MT represents the travel time of high-speed train i between city a and city b. D,i,abMT represents the travel time of train number i between city a and city b. A,i,ab MT represents the travel time of train number i between city a and city b. C,ab This represents the shortest transfer time between city a and city b in the integrated transportation network. It should be noted that MT in formula (6)... ab It is a universal representation applicable to high-speed rail networks, bullet train networks, regular train networks, and integrated spatiotemporal distance networks; that is, in high-speed rail networks, MT ab This indicates the shortest transfer time using high-speed trains; in the EMU network, MT ab The shortest transfer time using high-speed trains; in the regular train network, MT ab Indicates the shortest transfer time using regular trains; in the integrated spatiotemporal distance network, MT ab This indicates the shortest transfer time using any type of train. In contrast, MT... C,ab Specifically refers to the shortest transfer time within a comprehensive transportation network, used only in comprehensive spatiotemporal distance networks, equivalent to the MT (Transfer Time) in comprehensive spatiotemporal distance networks. ab .

[0058] Furthermore, to demonstrate the effectiveness of the comprehensive spatiotemporal distance between cities constructed in this embodiment in measuring population flow, this embodiment uses train timetable data from 2007 to 2024 as the data source, executes steps S110 to S150 above, and calculates the daily population migration index between each city based on the daily population outflow index and daily outflow population percentage data from October 1 to December 31, 2021. Through correlation analysis between the comprehensive spatiotemporal distance between cities and the population migration index between cities, the advantages of the comprehensive spatiotemporal distance between cities in this embodiment compared to other indicators are verified. Optionally, the specific calculation method for the population migration index between cities is as follows:

[0059] MI i,j =M_out i ×M_pecent i,j (9)

[0060] Among them, MI i,j M_out represents the population migration index from city i to city j. i M_pecent represents the size of the outflowing population of city i. i,j This represents the proportion of population migration from city i to city j relative to the total outflow population from city i. The average population migration index for the period from October 1 to December 31, 2021, can be obtained by averaging the daily population migration index.

[0061] Then, the correlation between the intercity population migration index and the spatiotemporal distance index, the time distance index, and the frequency of operation index was calculated. The results showed that the comprehensive spatiotemporal distance index between cities constructed in this embodiment had the strongest correlation with the actual intercity population migration (correlation coefficient r = -0.76, significance level p < 0.01), thus proving the superiority of the comprehensive spatiotemporal distance constructed in this embodiment in reflecting the intensity of intercity population flow.

[0062] Table 1. Correlation between intercity population migration index and various network indicators

[0063]

[0064] S160. Using the comprehensive spatiotemporal distance and direct distance between cities as independent variables and the disease transmission data between cities as dependent variables, fit a disease transmission speed prediction model.

[0065] In one specific implementation, the first step is to extract the dates of the first reported cases in multiple cities surrounding the source city from the historical transmission data of a disease. The source city refers to the city that first reported the case; for ease of distinction and description, all cities surrounding the source city will subsequently be referred to as target cities.

[0066] Then, based on the time periods described for each target city, the spread rate of the disease to each target city is determined. For example, taking the infection case data reported by surrounding target cities within 14 days after an outbreak of a certain infectious disease in a source city as an example, these 14 days can be divided into multiple consecutive time periods. The disease spread rate of target cities with cases appearing in the first time period (e.g., the first 3 days) is marked as very fast, the disease spread rate of target cities with cases appearing in the second time period (e.g., days 4 to 6) is marked as fast, the disease spread rate of target cities with cases appearing in the third time period (e.g., days 7 to 9) is marked as slow, and the disease spread rate of target cities with cases appearing in the fourth time period (e.g., days 10 to 14) is marked as very slow, thus obtaining the spread rate corresponding to each target city. Optionally, these spread rate levels can also be quantified using different numbers.

[0067] Based on the above data, the speed at which the disease spreads to each target city can be used as the dependent variable, with the comprehensive spatiotemporal distance, direct distance, comprehensive temporal distance, and comprehensive train frequency between cities as independent variables. A random forest method can be used to fit a model of the relationship between the disease transmission speeds between cities. When the independent and dependent variable data are known, random forest regression modeling can be implemented using the `RandomForestRegressor` function from the `sklearn` package in Python.

[0068] The constructed model takes the comprehensive spatiotemporal distance, direct distance, comprehensive temporal distance, and comprehensive train frequency between two cities as input, and the speed at which a disease spreads from one city to another as output. In practical applications, when a disease breaks out in a source city, the comprehensive spatiotemporal distance, direct distance, comprehensive transit distance, and comprehensive train frequency between each city within a certain distance and the source city can be input into the model to obtain the predicted speed of disease spread to each city, providing data support for the allocation of disease control resources and the implementation of disease control measures.

[0069] In summary, this embodiment presents a disease prediction method based on comprehensive spatiotemporal distance between cities. Utilizing train timetable data, it fully leverages the temporal and spatial characteristics of train operations between cities, integrating information from different types of trains, including high-speed rail, bullet trains, and regular trains, to obtain the comprehensive spatiotemporal distance between cities. This distance considers both train travel time and spatial frequency, reflecting the combined effects of timeliness and accessibility in actual railway transportation networks, and better reflects the characteristics of population flow between cities. Furthermore, this embodiment uses historical disease transmission data to fit the relationship between disease transmission speed and comprehensive spatiotemporal distance between cities. The resulting disease prediction model can more accurately reflect the patterns of disease transmission between cities, providing data support for targeted allocation of disease control resources and the implementation of disease control measures.

[0070] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 2 As shown, the device includes a processor 60, a memory 61, an input device 62, and an output device 63; the number of processors 60 in the device can be one or more. Figure 2 Taking a processor 60 as an example; the processor 60, memory 61, input device 62, and output device 63 in the device can be connected via a bus or other means. Figure 2 The bus connection is taken as an example.

[0071] The memory 61, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the method for constructing a disease transmission prediction model based on comprehensive inter-city spatiotemporal distance in this embodiment of the invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 61, thereby realizing the aforementioned method for constructing a disease transmission prediction model based on comprehensive inter-city spatiotemporal distance.

[0072] The memory 61 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 61 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include memory remotely located relative to the processor 60, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0073] Input device 62 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 63 may include display devices such as a display screen.

[0074] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for constructing a disease transmission prediction model based on comprehensive spatiotemporal distance between cities, as described in any embodiment.

[0075] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0076] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0077] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0078] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as C or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0079] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a disease transmission prediction model based on comprehensive spatiotemporal distance between cities, characterized in that, include: Based on the station names in the train timetable data, the stations are matched with cities in multiple cities at the prefecture-level, thereby constructing the train operation relationship between cities. Trains are categorized according to their train number type; Based on the train operation relationships and train type classifications between cities, calculate the number of direct trains between each city. Calculate the train travel time between cities based on the train operation relationships between them. The comprehensive spatial and temporal distance between cities is calculated based on the number of direct train services and train travel time between each city; specifically, ,in, Represents city With the city The combined spatiotemporal distance between them Represents city With the city The total number of direct trains between them Represents city With the city Number of direct high-speed train services between the two locations Represents city With the city Number of direct high-speed trains between them Represents city With the city Number of direct trains between the two places Indicates high-speed train number In the city With the city Runtime between Indicates the train number In the city With the city Runtime between Indicates the train number In the city With the city Runtime between Represents city With the city Shortest transit time between locations; Using the comprehensive spatiotemporal distance and direct distance between cities as independent variables, and the disease transmission data between cities as dependent variables, a disease transmission speed prediction model is fitted. Specifically, from the historical transmission data of a certain disease, the time when the first infection cases appeared in multiple target cities around the source city is extracted; based on the time of each target city, the speed at which the disease spreads from the source city to each target city is determined; using the speed of each target city as the dependent variable, and the comprehensive spatiotemporal distance, direct distance, comprehensive time distance, and comprehensive train frequency between cities as independent variables, a disease transmission speed prediction model between cities is fitted.

2. The method according to claim 1, characterized in that, The process of matching station names from train timetable data with cities across multiple cities at the prefecture-level scale to construct train operation relationships between cities includes: Extract the names of the stations along the route of each train from the train timetable data; Find the city where each station is located based on its name, and establish a correspondence between each station and its city; Based on the aforementioned correspondence, the travel time of each train through each city is determined; The aforementioned correspondence and time together constitute the train operation relationships between cities.

3. The method according to claim 1, characterized in that, The classification of trains based on train number type includes: will be in letters Train numbers starting with "high-speed rail" are classified as such. will be in letters Train numbers starting with "-" are classified as high-speed trains. Train numbers that begin with other letters will be classified as regular trains.

4. The method according to claim 1, characterized in that, The calculation of the number of direct train services between cities is based on the train operation relationships and train types, including: Calculate the number of direct train services between each city using the following formula: in, Represents city and city The number of direct high-speed rail connections between them Represents city and city The number of direct high-speed trains between them Represents city and city The number of direct flights between them is [number of flights]. Represents city and city The total number of direct trains of the three types.

5. The method according to claim 1, wherein The calculation of train travel time between cities based on the train operation relationships between cities includes: When there are direct trains between cities, the train travel time between the cities is calculated using the following formula: in, Indicates train From the city to the city runtime, Indicates train Arrival in the city Time, Indicates train From the city Departure time.

6. The method according to claim 1, characterized in that, The calculation of train travel time between cities based on the train operation relationships between cities includes: When there are no direct trains between cities, for cities and city Choose a transit city that meets the following conditions. :City transit city Between, transit cities to the city There are direct trains between them; when transferring at the same station, the transfer time is greater than the first threshold; when transferring at different stations, the transfer time is greater than the second threshold, wherein the first threshold is less than the second threshold. Calculate the shortest transit time between cities using the following formula: ， ， ， ， in, Indicates train From the city transit city runtime, Indicates train Arrive at transit city Time, Indicates train From the city Departure time; Indicates train transit city to the city runtime, Indicates train Reach the city Time, Indicates train transit city Departure time; This indicates that the transit plan is being followed. From the city transit cities Reach the city Total duration Indicates from the train Transfer to train Duration of consumption; Indicates from the city Arriving in the city via transit The shortest duration.

7. The method according to claim 1, characterized in that, The calculation of the comprehensive spatiotemporal distance between cities based on the number of direct train services and train travel time includes: Calculate the high-speed rail travel time between cities using the following formula: ， in, Represents city With the city The high-speed rail travel time between them Represents city With the city The average travel time between high-speed rail stations is [not specified]. Represents city With the city Number of direct high-speed train services between the two locations Indicates high-speed train number In the city With the city Runtime between Represents city With the city The shortest transit time between them.

8. An electronic device, characterized in that, include: one or more processors; Memory, used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for constructing a disease transmission prediction model based on comprehensive inter-city spatiotemporal distance as described in any one of claims 1-7.

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