A method for predicting the probability of the spread of infectious diseases using a path planning algorithm

Abstract

The application provides a method for predicting the transmission probability of infectious diseases using a path planning algorithm, comprising the following steps: collecting data information of cities where infectious diseases occur; dividing the data information corresponding to each city into several data sets; scaling each data set; constructing a city network graph between multiple cities; constructing a city network path model, optimizing the iteratively constructed city network path model using the Dijkstra algorithm, obtaining the total path closest to the city where the infectious disease broke out, and forming the transmission path of the infectious disease with the path; constructing an infectious disease transmission probability prediction model; traversing all nodes in the transmission path of the infectious disease obtained, and performing local probability analysis. The method for predicting the transmission probability of infectious diseases provided by the application can predict the probability of a target city being infected using the path planning method after an epidemic breaks out in a certain city, and can make the prediction more accurate and closer to reality.

Description

Technical Field

[0001] This invention belongs to the field of infectious disease prediction technology, specifically relating to a method for predicting the probability of infectious disease transmission using a path planning algorithm. Background Technology

[0002] To control the spread of epidemics, various methods have been tried to predict their transmission. Currently, many methods exist for predicting infectious diseases, such as using machine learning to predict the progression of the disease. This paper proposes a new prediction method that employs path planning to determine the probability of infection in a target city. While many methods predict the speed and probability of infectious disease transmission, such as building neural network models, these methods often suffer from inaccuracies and fail to accurately reflect reality. Summary of the Invention

[0003] To address the aforementioned shortcomings, this invention provides a method for predicting the probability of infectious disease transmission using a path planning algorithm. The method for predicting the probability of infectious disease transmission provided by this invention utilizes path planning to forecast the transmission probability. After an outbreak in a city, using path planning to predict the probability of infection in the target city can make the prediction more accurate and closer to reality.

[0004] This invention provides the following technical solution: a method for predicting the probability of infectious disease transmission using a path planning algorithm, comprising the following steps:

[0005] S1: Collect data on cities where infectious diseases occur;

[0006] S2: Mark the cities where infectious diseases occur, and divide the data information of each city into several data sets;

[0007] S3: Scale each of the several data sets formed in step S2.

[0008] S4: Construct a city network diagram among multiple cities;

[0009] S5: Based on the city network map constructed in step S4, construct a city network path model, and use Dijkstra's algorithm to optimize the iteratively constructed city network path model to obtain the total path closest to the city where the infectious disease outbreak occurred, forming the transmission path of the infectious disease with this path as the transmission path.

[0010] S6: Construct a probability prediction model for the spread of infectious diseases;

[0011] S7: Using the infectious disease transmission probability prediction model constructed in step S6, traverse all nodes in the transmission path of the infectious disease obtained in step S5 and perform local probability analysis.

[0012] Furthermore, the data collected in step S1 includes population data of the cities where the infectious disease occurred and distance data between the cities where the infectious disease occurred.

[0013] Furthermore, the several data sets formed in step S2 include population data information set N and distance data information set D between cities where infectious diseases occur:

[0014] N = {N1, N2, ..., N} M}, D={D ab};

[0015] Where M is the total number of cities where infectious diseases have occurred, collected in step S1; D ab Let be the distance between the a-th city where the infectious disease occurs and the b-th city where the infectious disease occurs, where a∈{1,2,...,M}, b∈{1,2,...,M}, and a≠b.

[0016] Furthermore, in step S3, the population data information set formed in step S2 is scaled by a ratio of 1:10000.

[0017] Furthermore, in step S3, the distance data information set formed in step S2 is scaled by a ratio of 1:1000m.

[0018] Furthermore, the urban network path model constructed in step S5 is as follows:

[0019] {′C n ′:{′C i ′:D ni , 'C j ′:D nj ,....,′C k ′:D nk}}

[0020] Among them, C n C represents the nth city where an infectious disease outbreak occurs. i D represents the i-th city associated with the n-th city of the said infectious disease outbreak. ni C represents the distance between the nth city and the i-th city. j D represents the j-th city associated with the n-th city of the said infectious disease outbreak. nj C represents the distance between the nth city and the jth city. k D represents the k-th city associated with the n-th city of the said infectious disease outbreak.nk Let D be the distance between the nth city and the kth city; i ≠ j ≠ k ≠ n, D ni ∈D, D nj ∈D and D nk ∈D. That is, i∈{1,2,...,M}, j∈{1,2,...,M}, k∈{1,2,...,M}.

[0021] Furthermore, the infectious disease transmission probability prediction model constructed in step S6 is as follows:

[0022] P = (1 - K1 * pow(R0, -N)) s ))×f(D nx )*K2;

[0023] Where P is the predicted probability of infectious disease transmission, K1 is the first weighting coefficient for the influence of population factors on the prediction of transmission probability, and N s Let the x-th city, which is predicted to be associated with the n-th city of the infectious disease outbreak, be the city node and the average population of the cities with the infectious disease outbreak, f(D) nx D is the distance influence function of associated cities as city nodes. nx K1 represents the distance between the predicted nth city and the xth city associated with the outbreak of infectious disease, where x represents the xth city associated with the nth city of the outbreak of infectious disease, x = i, j, ..., k; K2 is the second weighting coefficient that influences the distance factor in predicting the transmission probability; K1 + K2 = 1, K1 ∈ (0, 1), K2 ∈ (0, 1); R0 is the infectious disease transmission base number;

[0024] pow(R0, -N) s To obtain the infectious disease transmission base R0, -N s The power function of the power of the power;

[0025] Furthermore, the formula for calculating the average population of the predicted x-th city associated with the n-th city of the infectious disease outbreak, as a city node, and the city of the infectious disease outbreak is as follows:

[0026]

[0027] Where, N n Let N be the population of the nth city where the infectious disease outbreak occurred. x Let x be the population of the x-th city.

[0028] Furthermore, the distance influence function f(D) of the associated cities as city nodes nx The formula for calculating f(D) is as follows: nx ) = 1 / D nx .

[0029] The beneficial effects of this invention are as follows:

[0030] 1. This invention uses a path planning algorithm to predict the global propagation probability: By using path planning to predict the global propagation probability, the actual situation is more completely restored based on factors affecting the spread of infectious diseases (city population, level of transportation development, distance between cities) for prediction.

[0031] 2. Local prediction of propagation probability for a single node: When making global predictions, we use path planning to find the shortest path from the outbreak city to the city we are predicting. This path will pass through some city nodes, but when looking for the shortest distance, we often ignore many surrounding cities. So we make a local prediction based on (population, distance) to predict the propagation probability of each city node that passes through the path planning to its neighboring cities.

[0032] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0033] The invention will now be described in more detail with reference to embodiments and the accompanying drawings.

[0034] Figure 1 The method provided by this invention uses a path planning algorithm to obtain a city network construction graph;

[0035] Figure 2 This is a schematic diagram of the shortest path found after path planning in the method provided by the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] This invention provides a method for predicting the probability of infectious disease transmission using a path planning algorithm, comprising the following steps:

[0038] S1: Collect data on cities where infectious diseases occur;

[0039] S2: Mark the cities where infectious diseases occur, and divide the data information of each city into several data sets;

[0040] S3: Scale each of the several data sets formed in step S2.

[0041] S4: Construct a city network diagram among multiple cities;

[0042] S5: Based on the city network map constructed in step S4, construct a city network path model, and use Dijkstra's algorithm to optimize the iteratively constructed city network path model to obtain the total path closest to the city where the infectious disease outbreak occurred, forming the transmission path of the infectious disease with this path as the transmission path.

[0043] S6: Construct a probability prediction model for the spread of infectious diseases;

[0044] S7: Using the infectious disease transmission probability prediction model constructed in step S6, traverse all nodes in the transmission path of the infectious disease obtained in step S5 and perform local probability analysis.

[0045] Preferably, the data collected in step S1 includes population data of the cities where the infectious disease occurred and distance data between the cities where the infectious disease occurred.

[0046] More preferably, the several data sets formed in step S2 include population data information set N and distance data information set D between cities where infectious diseases occur:

[0047] N = {N1, N2, ..., N} M}, D={D ab};

[0048] Where M is the total number of cities where infectious diseases have occurred, collected in step S1; D ab Let be the distance between the a-th city where the infectious disease occurs and the b-th city where the infectious disease occurs, where a∈{1,2,...,M}, b∈{1,2,...,M}, and a≠b.

[0049] More preferably, the scaling ratio of the population data information set formed in step S2 in step S3 is 1:10000.

[0050] More preferably, in step S3, the scaling ratio of the distance data information set formed in step S2 is 1:1000m.

[0051] More preferably, the urban network path model constructed in step S5 is as follows:

[0052] {′C n′:{′C i ′:D ni , 'C j ′:D nj ,....,′C k ′:D nk}}

[0053] Among them, C n C represents the nth city where an infectious disease outbreak occurs. i D represents the i-th city associated with the n-th city of the said infectious disease outbreak. ni C represents the distance between the nth city and the i-th city. j D represents the j-th city associated with the n-th city of the said infectious disease outbreak. nj C represents the distance between the nth city and the jth city. k D represents the k-th city associated with the n-th city of the said infectious disease outbreak. nk Let D be the distance between the nth city and the kth city; i ≠ j ≠ k ≠ n, D ni ∈D, D ni ∈D and D nk ∈D. That is, i∈{1,2,...,M}, j∈{1,2,...,M}, k∈{1,2,...,M}.

[0054] More preferably, the infectious disease transmission probability prediction model constructed in step S6 is as follows:

[0055] P = (1 - K1 * pow(R0, -N)) s ))×f(D nx )*K2;

[0056] Where P is the predicted probability of infectious disease transmission, K1 is the first weighting coefficient for the influence of population factors on the prediction of transmission probability, and N s Let the x-th city, which is predicted to be associated with the n-th city of the infectious disease outbreak, be the city node and the average population of the cities with the infectious disease outbreak, f(D) nx D is the distance influence function of associated cities as city nodes. nx K1 represents the distance between the predicted nth city and the xth city associated with the outbreak of infectious disease, where x represents the xth city associated with the nth city of the outbreak of infectious disease, x = i, j, ..., k; K2 is the second weighting coefficient that influences the distance factor in predicting the transmission probability; K1 + K2 = 1, K1 ∈ (0, 1), K2 ∈ (0, 1); R0 is the infectious disease transmission base number;

[0057] pow(R0, -N) s To obtain the infectious disease transmission base R0, -N sThe power function of the power of the power;

[0058] 1-K1*pow(R0, -N) s This is because the base number of infectious disease transmission R0 and the population size N are... s Both factors influence the expression for probability P. When the base number of transmissions is constant, the larger the population, the greater the probability of transmission. This is because the number of people who can be infected depends on the base number of transmissions (R0) and the population (N). s The relationship is that the number of people who can be infected, R0, is N. s Since the population size and the base number of infectious disease transmissions are directly proportional to the probability of transmission, we construct (1-K1*pow(R0, -N)). s ));

[0059] More preferably, the formula for calculating the average population of the predicted x-th city associated with the n-th city of the infectious disease outbreak, as a city node, and the city of the infectious disease outbreak is as follows:

[0060]

[0061] Where, N n Let N be the population of the nth city where the infectious disease outbreak occurred. x Let x be the population of the x-th city.

[0062] More preferably, the distance influence function f(D) of the associated cities as city nodes nx The formula for calculating f(D) is as follows: nx ) = 1 / D nx .

[0063] To better understand the method provided by this invention, the following specific embodiment illustrates the method for predicting the probability of infectious disease transmission using a path planning algorithm:

[0064] S1: Collect data on cities where infectious diseases occur;

[0065] S2: Mark the cities where infectious diseases occur, and divide the data information of each city into several data sets;

[0066] Specifically, the process begins by selecting cities and marking them, such as... Figure 1 As shown, the selected cities are sequentially labeled C1, C2, C3, C4, C5, C6, C7, C8, C9, C 10 C 11 C 12 C 13 C 14 C 15 C 16 C17 Population data was collected for the corresponding cities: C1 10.04 million, C2 3.468 million, C3 2.566 million, C4 2.628 million, C5 0.81 million, C6 1.475 million, C7 0.962 million, C8 0.34 million, C9 10.906 million, C... 10 It is 1.557 million, C 11 It is 1.527 million, C 12 For 2.12 million, C 13 It is 1.782 million, C 14 For 1.903 million, C 15 It is 2.672 million, C 16 For 3.35 million, C 17 The figure is 658,000. Further data on the distances between adjacent cities will be collected.

[0067] The resulting dataset includes population data set N for 17 cities and distance data set D between the 17 cities where the infectious disease occurred. (For example...) Figure 1 As shown, the collected population data is placed in a set N = {1004, 346.8, 256.6, 262.8, 8.1, 147.5, 96.234, 34, 1090.6, 155.7, 152.7, 212, 178.2, 190.3, 267.2, 335, 65.8}.

[0068] S3: Scale each of the several datasets formed in step S2. Specifically, scale the population data set by a ratio of 1:10000 and the distance data set between cities where infectious diseases occur by a ratio of 1:1000m. The reason for scaling the data set collected in step S2 in step S3 is that using the original data for population and distance would result in a very small probability prediction value. Therefore, we scale the data to make the predicted probability more intuitive.

[0069] S4: The selected outbreak city is C1, and the city for prediction is C. 17 Construct a city network map between multiple cities, such as Figure 1 The diagram shows the urban network among the 17 cities that were constructed.

[0070] S5: Based on the city network map constructed in step S4, construct the city network path model: {′C n ′:{′c i ′:D ni , 'C j ′:D nj ,....,′C k ′:D nk}}, where C nC represents the nth city where an infectious disease outbreak occurs. i D represents the i-th city associated with the n-th city of the said infectious disease outbreak. ni C represents the distance between the nth city and the i-th city. j D represents the j-th city associated with the n-th city of the said infectious disease outbreak. nj C represents the distance between the nth city and the jth city. k D represents the k-th city associated with the n-th city of the said infectious disease outbreak. nk Let D be the distance between the nth city and the kth city; i ≠ j ≠ k ≠ n, D ni ∈D, D nj ∈D and D nk ∈D. That is, i∈{1,2,...,M}, j∈{1,2,...,M}, k∈{1,2,...,M}.

[0071] The urban network path model constructed by the Dijkstra algorithm is optimized and iteratively constructed to obtain the total path closest to the city where the infectious disease outbreak occurred, and the path is formed as the transmission path of the infectious disease.

[0072] Construct the following list of nodes, setting distance as the weight: g is the list name, the node before the curly braces is the parent node, the node marked with an ' before the semicolon inside the curly braces is the child node, and the node after the child node is the distance to adjacent cities (distance has been scaled):

[0073] g={'1':{'4': 40, '9': 44},

[0074] '2': {'16': 170, '7': 190},

[0075] '3': {'15': 40, '16': 140},

[0076] '4': {'1': 40, '9': 49},

[0077] '5': {'14': 37},

[0078] '6': {'12': 90, '8': 55, '11': 53},

[0079] '7': {'2': 60, '16': 190, '15': 90},

[0080] '8': {'6': 55, '12': 50, '9': 100, '11': 45},

[0081] '9': {'11': 80, '10': 130, '4': 49, '12': 150},

[0082] '10': {'9': 130, '11': 160, '15': 300},

[0083] '11': {'9': 80, '8': 45, '13': 130, '6': 53, '10': 160},

[0084] '12': {'9': 150, '8': 50, '13': 105, '6': 90},

[0085] '13': {'12': 105, '11': 130, '14': 110},

[0086] '14': {'5': 37, '13': 110, '17': 280},

[0087] '15': {'10': 300, '3': 40, '7': 90, '16': 100},

[0088] '16': {'15': 100, '3': 140, '2': 170, '7': 190},

[0089] '17': {'14': 280}

[0090] The optimized transmission routes of infectious diseases are as follows:

[0091] ['1', '9', '11', '13', '14', '17'], i.e. Figure 2 As shown, infectious diseases progress through C1, C9, and C... 11 C 13 C 14 The propagation eventually reaches city C17. This propagation path is the length of the shortest path obtained through optimization, and the shortest path length is 644.0.

[0092] S6: Construct a model for predicting the probability of infectious disease transmission:

[0093] P = (1 - K1 * pow(R0, -N)) s ))×f(D nx )*K2;

[0094] Where P is the predicted probability of infectious disease transmission, K1 is the first weighting coefficient for the influence of population factors on the prediction of transmission probability, and N s Let the x-th city, which is predicted to be associated with the n-th city of the infectious disease outbreak, be the city node and the average population of the cities with the infectious disease outbreak, f(D) nx D is the distance influence function of associated cities as city nodes. nxK1 represents the distance between the predicted nth city and the xth city associated with the outbreak of infectious disease, where x represents the xth city associated with the nth city of the outbreak of infectious disease, x = i, j, ..., k; K2 is the second weighting coefficient that influences the distance factor in predicting the transmission probability; K1 + K2 = 1, K1 ∈ (0, 1), K2 ∈ (0, 1); R0 is the infectious disease transmission base. K1 and K2 are user-defined weighting coefficients representing the influence of population size and distance from neighboring cities on the probability of infectious disease transmission P. Changing K1 and K2 can alter the weighting of distance in predicting the transmission probability. When using these coefficients, it's important to note the user-defined method (e.g., if the outbreak city A and the predicted city B are far apart but city B has a large population, then the distance weight K2 should be set smaller to reduce the influence of distance between neighboring cities on the calculated result P, and K1 should be set larger to increase the influence of population on the calculated result P). K1 and K2 are set to assign weights to population and distance factors in the probability prediction based on actual conditions, and both K1 and K2 are real numbers between 0 and 1. The collected data is then substituted into the formula to predict the transmission probability of the city.

[0095] pow(R0, -N) s To obtain the infectious disease transmission base R0, -N s The power function of the power of the power;

[0096] The formula for calculating the average population of the city predicted to be associated with the nth city of the infectious disease outbreak, and the city with the infectious disease outbreak, is as follows:

[0097]

[0098] Where, N n Let N be the population of the nth city where the infectious disease outbreak occurred. x Let x be the population of the x-th city.

[0099] The distance influence function f(D) of associated cities as city nodes nx The formula for calculating f(D) is as follows: nx ) = 1 / D nx By setting the distance influence function of the constructed associated cities as city nodes, the calculated propagation probability P can be affected, due to the distance D between the associated city nodes x and the nth city where the infectious disease outbreak occurred. nx The farther away, the lower the probability P of propagation, i.e., the distance D. nx It is inversely proportional to the probability P of propagation.

[0100] S7: The infectious disease transmission probability prediction model constructed using step S6 traverses all nodes in the transmission path of the infectious disease obtained in step S5 and performs local probability analysis.

[0101] Perform local probability analysis based on the nodes: First, based on the constructed nodes, such as... Figure 1 The city network diagram shown allows for probability prediction of neighboring cities for each traversed child node after path planning. For the outbreak city node C1, it has two child nodes C4 and C9. Our path planning only traverses node C9, not node C4. However, in reality, nodes C4 and C9 will both be infected. We perform local probability prediction by substituting the city population of node C4 (2.628 million) and the distance between nodes C4 and C1 (40 km) into the formula for estimation. For the traversed child nodes C1, C9, and C9... 11 C 13 C 14 C 17 Such operations are performed to make probability predictions more accurate and the predictability of cities more complete.

[0102] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0103] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0104] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the function specified in one or more processes of the method provided in this invention.

[0106] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for predicting the probability of infectious disease transmission using a path planning algorithm, characterized in that, Includes the following steps: S1: Collect data on cities where infectious diseases occur; S2: Mark the cities where infectious diseases occur, and divide the data information of each city into several data sets; S3: Scale each of the several data sets formed in step S2. S4: Construct a city network diagram among multiple cities; S5: Based on the city network map constructed in step S4, construct a city network path model, and use Dijkstra's algorithm to optimize the iteratively constructed city network path model to obtain the total path closest to the city where the infectious disease outbreak occurred, forming the transmission path of the infectious disease with this path as the transmission path. S6: Construct a probability prediction model for the spread of infectious diseases; S7: Using the infectious disease transmission probability prediction model constructed in step S6, traverse all nodes in the transmission path of the infectious disease obtained in step S5 and perform local probability analysis. The infectious disease transmission probability prediction model constructed in step S6 is as follows: P=(1-K1*pow(R0,-N s ))×f(D nx )*K2; Where P is the predicted probability of infectious disease transmission, K1 is the first weighting coefficient for the influence of population factors on the prediction of transmission probability, and N s Let the x-th city, which is predicted to be associated with the n-th city of the infectious disease outbreak, be the city node and the average population of the cities with the infectious disease outbreak, f(D) nx D is the distance influence function of associated cities as city nodes. nx K1 represents the distance between the predicted nth city and the xth city associated with the outbreak of infectious disease, where x represents the xth city associated with the nth city of the outbreak of infectious disease, x=i, j, …, k; K2 is the second weighting coefficient that influences the distance factor in predicting the transmission probability; K1+K2=1, K1∈(0,1), K2∈(0,1); R0 is the infectious disease transmission base number; pow(R0,-N s To obtain the infectious disease transmission base R0, -N s The power function of the power of 1.

2. The method for predicting the probability of infectious disease transmission using a path planning algorithm according to claim 1, characterized in that, The data collected in step S1 includes population data of the cities where the infectious disease occurred and distance data between the cities where the infectious disease occurred.

3. The method for predicting the probability of infectious disease transmission using a path planning algorithm according to claim 2, characterized in that, The several data sets formed in step S2 include population data information set N and distance data information set D between cities where infectious diseases occur: ， ； Where M is the total number of cities where infectious diseases have occurred, as collected in step S1; Let be the distance between the city where the infectious disease occurs (a) and the city where the infectious disease occurs (b). , And a≠b.

4. The method for predicting the probability of infectious disease transmission using a path planning algorithm according to claim 2, characterized in that, In step S3, the population data information set formed in step S2 is scaled by a ratio of 1:10000.

5. A method for predicting the probability of infectious disease transmission using a path planning algorithm according to claim 2, characterized in that, In step S3, the distance data information set formed in step S2 is scaled by a ratio of 1:1000m.

6. A method for predicting the probability of infectious disease transmission using a path planning algorithm according to claim 1, characterized in that, The urban network path model constructed in step S5 is as follows: ； in, This represents the nth city where an infectious disease outbreak has occurred. This represents the i-th city associated with the n-th city of the said infectious disease outbreak. Let n be the distance between the nth city and the i-th city. This represents the j-th city associated with the n-th city of the said infectious disease outbreak. Let n be the distance between the nth city and the jth city. This represents the k-th city associated with the n-th city of the said infectious disease outbreak. Let i be the distance between the nth city and the kth city; i ≠ j ≠ k ≠ n. , and ;Right now , , .

7. A method for predicting the probability of infectious disease transmission using a path planning algorithm according to claim 1, characterized in that, The formula for calculating the average population of the xth city, which is predicted to be associated with the nth city of the infectious disease outbreak, and the city with the infectious disease outbreak is as follows: ； in, Let be the population of the nth city where the infectious disease outbreak occurred. Let x be the population of the x-th city.

8. A method for predicting the probability of infectious disease transmission using a path planning algorithm according to claim 1, characterized in that, The distance influence function f(D) of the associated cities as city nodes nx The formula for calculating f(D) is as follows: nx )=1 / D nx .

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