Space-time risk assessment method for disease transmission area

Abstract

The invention relates to the technical field of data processing, and provides a space-time risk assessment method for a disease transmission area. The method comprises the following steps: calculating the total travel activity time of a travel track according to the travel track and a vehicle and a moving speed corresponding to the travel track, and obtaining a plurality of reachable areas corresponding to the travel track; determining a plurality of candidate activity areas from all the reachable areas according to the total travel activity time, and obtaining a plurality of disease transmission areas based on all the candidate activity areas and all the travel tracks; generating a space-time co-occurrence relationship between every two disease infected objects based on all the disease transmission areas; according to all the time-space co-occurrence relations, the infection probability of each disease infection object is calculated, and according to all the infection probabilities and all the disease transmission areas, the disaster-causing danger of each disease transmission area is calculated; and calculating the propagation risk according to the disaster-causing risk of the disease propagation area. According to the method, the accuracy of disease transmission risk assessment of the region can be improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method for assessing the spatiotemporal risk of disease transmission areas. Background Technology

[0002] In recent years, various respiratory infectious diseases have broken out repeatedly worldwide, threatening human life and causing huge economic losses. Infected individuals in the incubation period are infectious but do not show symptoms; their activities are unrestricted, and they are extremely difficult for susceptible individuals to detect. In an environment of diverse and interconnected transportation networks, residents' frequent travel further expands the scope of interpersonal contact, thereby accelerating the speed and spread of infectious diseases. Therefore, tracing the spatiotemporal activity areas of confirmed cases during the incubation period and assessing the likelihood of infectious disease transmission within those areas is of scientific guiding significance for residents' travel planning and for disease control departments to formulate prevention and control strategies and screen potential cases.

[0003] Existing research conducts spatiotemporal risk assessments of infectious diseases based on spatial point data reflecting the residential information of confirmed cases and trajectory data describing the historical spatiotemporal activities of cases. Spatiotemporal risk assessment methods based on spatial point data first divide the study area, identifying areas where confirmed cases reside as risk areas. Then, they couple the number of infected cases or related variables (such as infection rate), natural environmental factors, and social environmental factors to assess the risk of virus transmission within the area. This type of method is sensitive to the rules governing regional division; larger regional divisions result in coarse spatiotemporal risk granularity, easily leading to excessive resource consumption; smaller regional divisions may miss some risk areas visited by cases, resulting in under-identification of high-risk areas. Therefore, scholars have conducted numerous studies on spatiotemporal risk assessment based on trajectory data. This type of method first obtains the historical activity distribution of cases within the longest incubation period through smartphone location tracking or epidemiological surveys, identifying areas where cases have been active as risk areas, and then couples multiple factors to assess the spatiotemporal risk of infectious disease transmission. However, smartphone users cannot cover all age groups, resulting in the complete loss of spatiotemporal trajectories of some confirmed cases, further leading to missed identification of risk areas and bias in the measurement of spatiotemporal risk levels. In contrast, epidemiological investigation methods can obtain historical activity information for each confirmed case, but this process relies on the patient's memory of the areas they visited. Due to the limitations of the patient's memory, only some activity areas during the historical period provided by the patient's memory are recorded.

[0004] In summary, methods based on spatial point data struggle to provide detailed spatiotemporal risk information for infectious diseases. Trajectory data records incomplete spatiotemporal activity distribution of cases, leading to uncertainty in the spatiotemporal location of cases (i.e., whether a case has visited a particular area is uncertain). Trajectory data-based methods ignore this uncertainty, easily resulting in missed risk areas. Furthermore, the number of infected individuals is a crucial indicator for quantifying the spatiotemporal risk of infectious disease transmission. Cases have the potential to be infected every day during the incubation period, but the infection time is unknown and unobservable, leading to uncertainty in the number of infected individuals (infectiousness) within a spatiotemporal region. Existing technologies ignore this uncertainty in infection time, easily causing inaccurate assessments of spatiotemporal risk levels. Therefore, existing risk assessment methods suffer from low accuracy in assessing regional disease transmission risk. Summary of the Invention

[0005] This application provides a spatiotemporal risk assessment method for disease transmission areas, which can solve the problem of low accuracy in assessing the disease transmission risk of a region.

[0006] In a first aspect, embodiments of this application provide a method for assessing the spatiotemporal risk of a disease transmission area, the method comprising:

[0007] The system acquires multiple travel trajectories for each infected individual in the target area across multiple historical time periods, along with the mode of transportation and travel speed corresponding to each trajectories. The target area includes multiple sub-areas, and the infected individuals are those with latent symptoms of the target disease.

[0008] For each historical time period, the following steps are performed:

[0009] For each travel trajectory within a historical time period, the total travel time for the travel trajectory is calculated based on the travel trajectory itself, the corresponding mode of transportation, and the travel speed. Based on the total travel time, multiple reachable areas corresponding to the travel trajectory are obtained; each reachable area is a sub-region of the target area.

[0010] For each infected individual, multiple candidate activity areas are determined from all reachable areas based on the total travel time of all travel trajectories of the infected individual. Based on all candidate activity areas and all travel trajectories of the infected individual in the historical time period, multiple disease transmission areas of the infected individual in the historical time period are obtained. The disease transmission area is a sub-region visited by the infected individual in the historical time period.

[0011] Based on all disease transmission areas of all disease-infected objects, a spatiotemporal co-occurrence relationship between every two disease-infected objects is generated in a historical time period; the spatiotemporal co-occurrence relationship is used to describe the situation where two corresponding disease-infected objects appear in the same disease transmission area in a historical time period.

[0012] The probability of each infected object in a historical time period is calculated based on all spatiotemporal co-occurrence relationships. The hazard risk of each disease transmission area of ​​each infected object in a historical time period is calculated based on the probability of infection of all infected objects in a historical time period and all disease transmission areas of all infected objects. The probability of infection describes the probability that an infected object has the ability to spread infectiously in a historical time period, and the hazard risk describes the probability that the target disease will spread in the disease transmission area.

[0013] For each disease transmission area, the risk of disease transmission in that area over a historical period is calculated based on the area's hazard level.

[0014] Optionally, based on the travel trajectory and the corresponding mode of transportation and speed, the total travel time for the travel trajectory is calculated, including:

[0015] Through the formula:

[0016]

[0017] Calculate the total travel time of the s-th travel trajectory of the ith disease-infected subject in the j-th historical time period.

[0018] in, This represents the activity time of the i-th infected person at the end of the a-th travel trajectory within the j-th historical time period. Let s represent the movement speed corresponding to the a-th travel trajectory, where s, a∈n, s≠a, n represents the number of travel trajectories of the i-th infected person in the j-th historical time period, i=1,2,...,I, where I represents the number of infected persons, j=1,2,...,J, where J represents the number of historical time periods. The shortest road network distance for the a-th travel trajectory is:

[0019]

[0020] in, This represents the mode of transportation corresponding to the a-th travel trajectory. This represents the road network corresponding to the mode of transportation for the a-th travel trajectory. This represents the node in the road network corresponding to the starting point of the a-th travel trajectory. This represents the node in the road network corresponding to the destination of the a-th travel trajectory. This represents the starting point of the a-th travel trajectory. This represents the destination of the a-th travel trajectory. This indicates that, under the constraints of the road network for the mode of transportation corresponding to the a-th travel trajectory, and The shortest distance between them.

[0021] Optionally, multiple reachable areas corresponding to the travel trajectory can be obtained based on the total travel time, including:

[0022] Through the formula:

[0023]

[0024] Get the set of all reachable areas corresponding to the s-th travel trajectory.

[0025] in, Let represent the set of all travel trajectories of the i-th disease-infected individual within the j-th historical time period. This represents the starting point of the s-th travel trajectory. This represents the destination of the s-th travel trajectory. Let x represent the x-th reachable region corresponding to the s-th travel trajectory, where x = 1, 2, ..., X, and X represents the number of reachable regions corresponding to the s-th travel trajectory. This represents the travel time from the starting point of the s-th travel trajectory to the x-th reachable area. This represents the travel time from the x-th reachable area to the s-th destination of the travel trajectory:

[0026]

[0027] in, This represents the distance from the starting point of the s-th travel trajectory to the reachable area. The shortest road network distance, Indicates from the reachable area The shortest road network distance to the destination of the s-th travel trajectory.

[0028] Optionally, multiple candidate activity areas can be determined from all reachable areas corresponding to the infected individuals based on the total travel time of all travel trajectories. These include:

[0029] For each reachable area, perform the following steps:

[0030] Based on the total travel time of the travel trajectory corresponding to the reachable area, calculate the maximum estimated activity time of the infected person in the reachable area during the historical time period;

[0031] Obtain all city locations within the reachable area, and calculate the minimum activity time requirement for the reachable area based on all city locations;

[0032] If the maximum activity time budget is greater than the minimum activity time requirement, the reachable area will be considered as a candidate activity area for disease-infected individuals in the historical time period.

[0033] Optionally, based on the total travel time of the travel trajectories corresponding to the reachable areas, calculate the estimated maximum activity time of the infected individuals in the reachable areas during the historical time period, including:

[0034] Through the formula:

[0035]

[0036] Calculate the maximum activity time budget for the i-th infected subject in the x-th reachable area.

[0037] in, This represents the total travel time of the s-th travel trajectory of the ith disease-infected individual within the j-th historical time period. This represents the travel time from the starting point of the s-th travel trajectory to the x-th reachable area. Let x represent the travel time from the x-th reachable area to the destination of the s-th travel trajectory, where x = 1, 2, ..., X, X represents the number of reachable areas corresponding to the s-th travel trajectory, s ∈ n, n represents the number of travel trajectories of the i-th infected person in the j-th historical time period, i = 1, 2, ..., I, I represents the number of infected persons, j = 1, 2, ..., J, J represents the number of historical time periods;

[0038] The minimum activity time requirement for reachable areas is calculated based on all city locations, including:

[0039] Through the formula:

[0040]

[0041] Calculate the minimum activity time required for the i-th infected subject in the x-th reachable area. x ;

[0042] Among them, NP x_l NP represents the number of city locations of type l in the x-th reachable region. x Let L represent the total number of city locations in the x-th reachable region, and let L represent the number of different types of city locations. l This indicates the shortest activity duration for city venues of type l.

[0043] Optionally, based on all candidate activity areas and all travel trajectories of infected individuals during historical time periods, multiple disease transmission areas of infected individuals during historical time periods can be obtained, including:

[0044] For each candidate activity area, the access probability of the candidate activity area is calculated. If the access probability is greater than the access probability threshold, the candidate activity area is regarded as a disease transmission area.

[0045] For each travel trajectory of a person infected with the disease during a historical period, both the starting and ending sub-regions of the travel trajectory are considered as a disease transmission area.

[0046] Optionally, the access probability of candidate activity regions is calculated, including:

[0047] Through the formula:

[0048]

[0049] Calculate the probability p of visiting the y-th candidate activity area corresponding to the s-th travel trajectory of the i-th disease-infected individual in the j-th historical time period. y_1 (c i ,day j );

[0050] Among them, Ultra y_1 Uact represents the travel utility value for the y-th candidate activity region. y_1 Ultra represents the access activity utility value of the y-th candidate activity region. y_0 Uact represents the non-visit travel utility value for the y-th candidate activity region. y_0 This represents the utility value of the non-accessed activity for the y-th candidate activity region:

[0051]

[0052] Where k = 1, 0, β k1 β k2 β k3 β k4 β k5 All represent coefficients. This represents the travel time from the starting point of the s-th travel trajectory to the y-th candidate activity area. This represents the travel time from the y-th candidate activity area to the destination of the s-th travel trajectory. Let represent the shortest road network distance from the starting point of the s-th travel trajectory to the y-th candidate activity area. Represents the shortest road network distance from the y-th candidate activity area to the destination of the s-th travel trajectory, div x This represents the urban venue diversity of the y-th candidate activity area. Mindur represents the maximum activity time budget for the i-th infected subject in the y-th candidate activity region. yThis represents the minimum activity time requirement for the i-th infected subject in the y-th candidate activity region.

[0053] Optionally, based on all disease transmission areas of all infected individuals, generate spatiotemporal co-occurrence relationships between every two infected individuals in historical time periods, including:

[0054] For each disease-infected object, iterate through each other disease-infected object. If there exists at least one disease transmission area that simultaneously belongs to the disease transmission area of ​​the disease-infected object in the historical time period and the disease transmission area of ​​other disease-infected objects in the historical time period, then the spatiotemporal co-occurrence relationship between the disease-infected object and other disease-infected objects in the historical time period is recorded as 1.

[0055] Optionally, calculate the transmission probability of each infected individual in a historical time period based on all spatiotemporal co-occurrence relationships, including:

[0056] Obtain the diagnosis time of each disease-infected subject, and based on the diagnosis time of each disease-infected subject and all corresponding spatiotemporal co-occurrence relationships, obtain multiple infection time periods for each disease-infected subject;

[0057] Based on all infection time periods for each disease-infected individual, calculate the probability of transmission of each disease-infected individual's infectious capacity during the historical time period.

[0058] Based on the diagnosis time of each disease-infected individual and the corresponding spatiotemporal co-occurrence relationships, multiple infection time periods for each disease-infected individual are obtained, including:

[0059] Through the formula:

[0060]

[0061]

[0062] Get the set of infection time periods for the i-th disease-infected object.

[0063] in, STCoR(c) represents an infection time period for the i-th infected subject. i ,c e ,day j ) represents the spatiotemporal co-occurrence relationship between the i-th and e-th disease-infected subjects in the j-th historical time period, c i Let c represent the i-th infected object. e Indicates the e-th infected person, day j This represents the j-th historical time period. Let T represent the time of diagnosis for the i-th infected individual, and let T represent the longest incubation period of the disease. Let i = 1, 2, ..., I, where I represents the number of infected individuals and j = 1, 2, ..., J, where J represents the number of historical time periods.

[0064] Based on all infection time periods for each infected individual, calculate the probability of transmission of each infected individual's infectivity within the historical time period, including:

[0065] Through the formula:

[0066]

[0067] Calculate the transmission probability TIP(c) of the i-th infected person in the j-th historical time period. i ,day j );

[0068] Among them, IP(c i (t) represents the probability of the i-th infected person being infected in the t-th historical time period:

[0069]

[0070] Based on the transmission probability of all infected individuals and the total disease transmission areas of all infected individuals within a historical time period, calculate the hazard risk of each disease transmission area corresponding to that historical time period, including:

[0071] Through the formula:

[0072]

[0073] Calculate the hazard risk H(taz) of the z-th disease transmission area in the j-th historical time period. z ,day j );

[0074] Where, p z_1 (c i ,day j Let z = 1, 2, ..., Z, where z represents the number of disease transmission areas for the i-th infected person in the j-th historical time period.

[0075] Optionally, based on the hazard risk of each disease transmission area, calculate the transmission risk of each disease transmission area over a historical period, including:

[0076] Through the formula:

[0077] STR(taz z ,day j )=H(taz z ,day j )*V(taz z )

[0078] Calculate the transmission risk STR(taz) in the z-th disease transmission area during the j-th historical time period. z ,day j );

[0079] Among them, V(taz) z The z-th disease transmission area represents the vulnerability of the disaster-prone environment.

[0080]

[0081] Where, β l Indicates the weighting coefficient. Let represent the kernel density estimate of the l-th type of urban site within the z-th disease transmission area, where l = 1, 2, ..., L, and L represents the number of urban site types.

[0082] Secondly, embodiments of this application provide a spatiotemporal risk assessment device for disease transmission areas, comprising:

[0083] The acquisition module acquires multiple travel trajectories of each infected individual in the target area over multiple historical time periods, as well as the means of transportation and travel speed corresponding to each travel trajectory; the target area includes multiple sub-areas, and the infected individuals are those with latent symptoms of the target disease;

[0084] The calculation module calculates the total travel time for each travel trajectory within a historical time period, based on the travel trajectory and the corresponding mode of transportation and speed. It then obtains multiple reachable areas corresponding to the travel trajectory based on the total travel time. Each reachable area is a sub-region of the target area.

[0085] The determination module, for each infected person, determines multiple candidate activity areas from all reachable areas corresponding to the infected person based on the total travel time of all travel trajectories of the infected person. Based on all candidate activity areas and all travel trajectories of the infected person in the historical time period, it obtains multiple disease transmission areas of the infected person in the historical time period; the disease transmission area is a sub-region visited by the infected person in the historical time period.

[0086] The generation module generates spatiotemporal co-occurrence relationships between every two disease-infected objects in a historical time period, based on all disease transmission areas of all disease-infected objects. Spatiotemporal co-occurrence relationships are used to describe the situation where two corresponding disease-infected objects appear in the same disease transmission area in a historical time period.

[0087] The disaster risk calculation module calculates the infectivity probability of each disease-infected object in a historical time period based on all spatiotemporal co-occurrence relationships, and calculates the disaster risk of each disease-infected object in each disease-infected area in a historical time period based on the infectivity probability of all disease-infected objects in a historical time period and all disease transmission areas of all disease-infected objects. The infectivity probability is used to describe the probability that a disease-infected object has the ability to infect in a historical time period, and the disaster risk is used to describe the probability that the target disease will spread in the disease transmission area.

[0088] The transmission risk calculation module calculates the transmission risk of each disease transmission area over a historical period, based on the area's hazard level.

[0089] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned method for assessing the spatiotemporal risk of disease transmission areas.

[0090] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for assessing the spatiotemporal risk of disease transmission areas.

[0091] The above-mentioned solution in this application has the following beneficial effects:

[0092] In the embodiments of this application, multiple travel trajectories of each infected individual in the target area over multiple historical time periods are obtained, along with the means of transportation and speed corresponding to each travel trajectory. Then, for each travel trajectory in the historical time periods, the total travel time of the travel trajectory is calculated based on the travel trajectory and the corresponding means of transportation and speed. Based on the total travel time, multiple reachable areas corresponding to the travel trajectory are obtained. Then, for each infected individual, multiple candidate activity areas are determined from all reachable areas corresponding to the infected individual based on the total travel time of all travel trajectories. Finally, based on all candidate activity areas and the infected individual's location within the target area, multiple candidate activity areas are determined. The system obtains all travel trajectories within a historical time period, identifies multiple disease transmission areas for infected individuals within that time period, and then generates spatiotemporal co-occurrence relationships between every two infected individuals within that time period based on all spatiotemporal co-occurrence relationships. It then calculates the transmission probability of each infected individual within the historical time period based on all spatiotemporal co-occurrence relationships, and calculates the hazard risk of each disease transmission area within the historical time period based on the transmission probability of all infected individuals and all disease transmission areas for all infected individuals. Finally, it calculates the transmission risk of each disease transmission area within the historical time period based on its hazard risk. Specifically, by obtaining the reachable areas of infected individuals based on their travel trajectories, the travel status of infected individuals can be described. Candidate activity areas are determined from the reachable areas based on the total travel time, taking into account the time dimension. This improves the accuracy of describing the travel status of infected individuals and thus obtains precise disease transmission areas. The transmission probability obtained from the precise disease transmission areas can accurately describe the probability that infected individuals are infectious within a historical time period. The accuracy of the transmission risk of disease transmission areas obtained based on the accurate transmission probability is improved, thereby improving the accuracy of disease transmission risk assessment in the region.

[0093] Other beneficial effects of this application will be described in detail in the following detailed description section. Attached Figure Description

[0094] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0095] Figure 1 A flowchart illustrating a method for assessing the spatiotemporal risk of disease transmission areas provided in an embodiment of this application;

[0096] Figure 2 A schematic diagram of a spatiotemporal activity chain provided in an embodiment of this application;

[0097] Figure 3 A schematic diagram of an accessible area provided in an embodiment of this application;

[0098] Figure 4 A schematic diagram of a candidate activity region provided in an embodiment of this application;

[0099] Figure 5 A schematic diagram of the structure of a spatiotemporal risk assessment device for disease transmission areas provided in an embodiment of this application;

[0100] Figure 6 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0101] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0102] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0103] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0104] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0105] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0106] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0107] To address the issue of low accuracy in existing regional disease transmission risk assessments, this application provides a spatiotemporal risk assessment method for disease transmission areas. This method acquires multiple travel trajectories for each infected individual in a target area across multiple historical time periods, along with the corresponding mode of transportation and speed for each trajectory. Then, for each travel trajectory within a historical time period, the total travel time is calculated based on the trajectory and its corresponding mode of transportation and speed. Based on this total travel time, multiple reachable areas corresponding to the travel trajectory are obtained. Finally, for each infected individual, multiple [areas] are determined from all reachable areas corresponding to that infected individual based on the total travel time of all their corresponding travel trajectories. Candidate activity areas are identified, and based on all candidate activity areas and the travel trajectories of infected individuals throughout the historical time period, multiple disease transmission areas of infected individuals within the historical time period are obtained. Then, based on all disease transmission areas of all infected individuals, spatiotemporal co-occurrence relationships between every two infected individuals within the historical time period are generated. Next, the transmission probability of each infected individual within the historical time period is calculated based on all spatiotemporal co-occurrence relationships. Finally, based on the transmission probability of all infected individuals within the historical time period and all disease transmission areas of all infected individuals, the disaster risk of each disease transmission area within the historical time period is calculated. Finally, for each disease transmission area, the transmission risk of each disease transmission area within the historical time period is calculated based on the disaster risk of each disease transmission area. Specifically, by obtaining the reachable areas of infected individuals based on their travel trajectories, the travel status of infected individuals can be described. Candidate activity areas are determined from the reachable areas based on the total travel time, taking into account the time dimension. This improves the accuracy of describing the travel status of infected individuals, thereby obtaining precise disease transmission areas. The transmission probability obtained from the precise disease transmission areas can accurately describe the probability that infected individuals are infectious within a historical time period. The accuracy of the hazard risk of transmission based on the accurate transmission probability is improved, thereby enhancing the accuracy of disease transmission risk assessment in the region.

[0108] The risk assessment method for disease transmission areas provided in this application will be illustrated below.

[0109] like Figure 1 As shown, the spatiotemporal risk assessment method for disease transmission areas provided in this application includes the following steps:

[0110] Step 11: Obtain multiple travel trajectories for each disease-infected individual in the target area over multiple historical time periods, as well as the mode of transportation and speed corresponding to each travel trajectory.

[0111] The target area mentioned above includes multiple sub-areas, which can be divided according to the transportation network. For example, the target area can be divided into multiple traffic zones according to the transportation network, and the traffic zones can be used as sub-areas. The target of disease infection is people who are incubating the target disease.

[0112] It should be noted that the target area is the region where disease risk assessment needs to be conducted, which can be a city or a county, etc. Multiple historical time periods can be set based on the length of the disease's incubation period. For example, if the incubation period is 5 days and the current date is September 7th, then multiple historical time periods could be September 2nd, September 3rd, September 4th, September 5th, and September 6th, to study whether individuals on September 7th are within the incubation period of the target disease. The target disease is an infectious disease, such as influenza or rubella, and the mode of transportation can be the subway, bus, or shared bicycle, etc.

[0113] For example, location software such as the Global Positioning System can be used to obtain the travel trajectory, mode of transportation, and speed of movement of individuals infected with the disease.

[0114] In some embodiments of this application, the spatiotemporal activity chain of a disease-infected individual during a historical time period can be obtained based on all travel trajectories of that individual, thus providing a visual description of their travel patterns, such as:

[0115]

[0116] in, This represents the spatiotemporal activity chain of the i-th disease-infected object within the j-th historical time period. This represents the starting point of the s-th travel trajectory. This represents the destination of the s-th travel trajectory. This represents the mode of transportation corresponding to the s-th travel trajectory. This represents the speed of movement corresponding to the s-th travel trajectory. Let represent the duration of stay at the destination of the i-th infected person in the s-th travel trajectory, where s∈n, n represents the number of travel trajectories of the i-th infected person in the j-th historical time period, i=1,2,...,I, where I represents the number of infected persons, j=1,2,...,J, where J represents the number of historical time periods.

[0117] The following example illustrates the spatiotemporal activity chain.

[0118] like Figure 2As shown in the figure, the vertical axis represents time, from 0 hours (h) to 24 hours. Circles represent road network nodes, diamond-shaped dots represent subway stations, and dots with crosses represent bus stops. Straight lines between stations represent the corresponding road networks (subway network, bus network, city road network). The polygonal areas divided by stations and road networks are traffic zones (i.e., sub-regions mentioned above), including the home and the starting point taz of the s-th travel trajectory. s-1 The destination of the sth travel trajectory, taz s The first travel trajectory starts at taz1. Gray dots represent the start or end point of the travel trajectory of the infected individual. Dashed lines with arrows represent the travel trajectory, with the arrowhead indicating the end point and the tail indicating the start point. tool1 represents the mode of transportation for the first travel trajectory, and v1 represents the movement speed for the first travel trajectory. s v represents the mode of transportation corresponding to the s-th travel trajectory. s Dur represents the movement speed corresponding to the s-th travel trajectory, and dur1 represents the dwell time at the destination of the 1st travel trajectory. s-1 Dur represents the duration of the activity at the destination of the (s-1)th travel trajectory. s Dur represents the duration of the activity at the destination of the s-th travel trajectory. n This represents the duration of the activity at the destination of the nth travel trajectory.

[0119] After step 11, perform the following steps for each historical time period:

[0120] Step 12: For each travel trajectory in the historical time period, calculate the total travel time of the travel trajectory based on the travel trajectory and the corresponding means of transportation and speed, and obtain multiple reachable areas corresponding to the travel trajectory based on the total travel time.

[0121] The aforementioned reachable area is a sub-region of the target area, describing the sub-regions that an infected person may reach during their journey from the starting point to the ending point of their travel trajectory.

[0122] In some embodiments of this application, the steps of calculating the total travel time of the travel trajectory based on the travel trajectory and the corresponding means of transportation and speed, and obtaining multiple reachable areas corresponding to the travel trajectory based on the total travel time, are specifically as follows:

[0123] The first step is to calculate the total travel time based on the travel trajectory and the corresponding mode of transportation and speed.

[0124] Through the formula:

[0125]

[0126] Calculate the total travel time of the s-th travel trajectory of the ith disease-infected subject in the j-th historical time period.

[0127] in, This represents the activity time of the i-th infected person at the end of the a-th travel trajectory within the j-th historical time period. Let s represent the speed corresponding to the _a_th travel trajectory, where a, a∈n, s≠a, n represents the number of travel trajectories of the _i_th infected person in the _j_th historical time period, i=1,2,...,I, where I represents the number of infected persons, j=1,2,...,J, where J represents the number of historical time periods. The shortest road network distance for the a-th travel trajectory is:

[0128]

[0129] in, This represents the mode of transportation corresponding to the a-th travel trajectory. This represents the road network corresponding to the mode of transportation for the a-th travel trajectory. This represents the node in the road network corresponding to the starting point of the a-th travel trajectory. This represents the node in the road network corresponding to the destination of the a-th travel trajectory. This represents the starting point of the a-th travel trajectory. This represents the destination of the a-th travel trajectory. This indicates that, under the constraints of the road network for the mode of transportation corresponding to the a-th travel trajectory, and The shortest distance between them.

[0130] The second step is to obtain multiple reachable areas corresponding to the travel trajectory based on the total travel time.

[0131] Through the formula:

[0132]

[0133] Get the set of all reachable areas corresponding to the s-th travel trajectory.

[0134] in, Let represent the set of all travel trajectories of the i-th disease-infected individual within the j-th historical time period. This represents the starting point of the s-th travel trajectory. This represents the destination of the s-th travel trajectory. Let x represent the x-th reachable region corresponding to the s-th travel trajectory, where x = 1, 2, ..., X, and X represents the number of reachable regions corresponding to the s-th travel trajectory. This represents the travel time from the starting point of the s-th travel trajectory to the x-th reachable area. This represents the travel time from the x-th reachable area to the s-th destination of the travel trajectory:

[0135]

[0136] in, This represents the distance from the starting point of the s-th travel trajectory to the reachable area. The shortest road network distance, Indicates from the reachable area The shortest road network distance to the destination of the s-th travel trajectory.

[0137] It should be noted that the formula for obtaining the set of reachable areas mentioned above can be understood as follows: for each sub-region of the target area, if the sum of the travel time from the starting point of the travel trajectory to the sub-region and the travel time from the sub-region to the end point of the travel trajectory is less than or equal to the total travel time of the travel trajectory, then the sub-region is regarded as a reachable area of ​​the travel trajectory.

[0138] For example, the total travel time of the travel trajectory is 1 hour. The travel time from the starting point of the travel trajectory to the first sub-area is 40 minutes, and the travel time from the first sub-area to the end point of the travel trajectory is 30 minutes, totaling 70 minutes, which is greater than 1 hour. This indicates that the infected person could not have reached the first sub-area during the travel trajectory. The travel time between the second sub-area and the starting point and end point of the travel trajectory is 20 minutes and 10 minutes respectively, totaling 30 minutes, which is less than 1 hour. This indicates that the infected person may have reached the second sub-area during the travel trajectory. The second sub-area is a reachable area of ​​the travel trajectory.

[0139] It is worth mentioning that by calculating the total travel time of the travel trajectory and determining the reachable area based on the total travel time, time-related information is taken into account, thus improving the accuracy of the reachable area.

[0140] The above-mentioned reachable area will be illustrated with a specific example below.

[0141] A schematic diagram of the reachable area obtained using a spacetime prism is shown below. Figure 3 As shown in the figure, the vertical axis represents time, the circles represent road network nodes, the diamond-shaped dots represent subway stations, the cross-shaped dots represent bus stops, the straight lines between stations represent the corresponding road network, and the polygonal areas divided by stations and road networks are traffic zones (i.e., sub-regions mentioned above), including the home, the starting point taz of the s-th travel trajectory. s-1 The destination of the sth travel trajectory, taz sThe gridded traffic zones are the reachable areas (i.e., areas that can be reached), taz s A cone with vertex taz is a backward cone. s-1 The cone with the vertex is the forward cone, and the time span between the two is the time budget (i.e., the total travel time mentioned above). The time period within the time budget in the figure is the maximum activity duration budget (i.e., the maximum activity time budget mentioned above). The black shaded area between the two cones is the coverage area of ​​the two cones, and the gray shaded area is the overlapping area of ​​the two cones.

[0142] Step 13: For each infected person, determine multiple candidate activity areas from all reachable areas corresponding to the infected person based on the total travel time of all travel trajectories of the infected person. Based on all candidate activity areas and all travel trajectories of the infected person in the historical time period, obtain multiple disease transmission areas of the infected person in the historical time period.

[0143] The aforementioned disease transmission area is a sub-region visited by individuals infected with the disease during a historical time period.

[0144] In some embodiments of this application, the steps described above—determining multiple candidate activity areas from all reachable areas corresponding to the infected person based on the total travel time of all travel trajectories of the infected person, and obtaining multiple disease transmission areas of the infected person in the historical time period based on all candidate activity areas and all travel trajectories of the infected person in the historical time period—are specifically as follows:

[0145] The first step is to identify multiple candidate activity areas from all reachable areas corresponding to the infected individuals based on the total travel time of all travel trajectories.

[0146] Specifically, for each reachable area, the following steps are performed:

[0147] First, based on the total travel time of the travel trajectories corresponding to the reachable areas, calculate the maximum estimated activity time of the infected individuals in the reachable areas during the historical time period.

[0148] Through the formula:

[0149]

[0150] Calculate the maximum activity time budget for the i-th infected subject in the x-th reachable area.

[0151] in, This represents the total travel time of the s-th travel trajectory of the ith disease-infected individual within the j-th historical time period. This represents the travel time from the starting point of the s-th travel trajectory to the x-th reachable area. Let x represent the travel time from the x-th reachable area to the destination of the s-th travel trajectory, where x = 1, 2, ..., X, X represents the number of reachable areas corresponding to the s-th travel trajectory, s ∈ n, n represents the number of travel trajectories of the i-th infected person in the j-th historical time period, i = 1, 2, ..., I, I represents the number of infected persons, j = 1, 2, ..., J, J represents the number of historical time periods.

[0152] Then, obtain all city locations within the reachable area and calculate the minimum activity time requirement for the reachable area based on all city locations.

[0153] Through the formula:

[0154]

[0155] Calculate the minimum activity time required for the i-th infected subject in the x-th reachable area. x .

[0156] Among them, NP x_l NP represents the number of city locations of type l in the x-th reachable region. x Let L represent the total number of city locations in the x-th reachable region, and let L represent the number of different types of city locations. l This indicates the shortest activity duration for city venues of type l.

[0157] Finally, if the maximum activity time budget is greater than the minimum activity time requirement, the reachable area will be considered as a candidate activity area for disease-infected individuals in the historical time period.

[0158] It should be noted that the total travel time of the s-th travel trajectory of the ith disease-infected subject in the j-th historical time period mentioned above... The total travel time can be calculated using the formula used in step 12, which represents the travel time from the starting point of the s-th travel trajectory to the x-th reachable area. Travel time from the xth reachable area to the sth destination of the travel route All can be calculated using the formula for calculating travel time in step 12.

[0159] The second step involves obtaining multiple disease transmission areas of the infected individuals during historical time periods, based on all candidate activity areas and all travel trajectories of the infected individuals.

[0160] Specifically, for each candidate activity area, the access probability of the candidate activity area is calculated. If the access probability is greater than the access probability threshold, the candidate activity area is regarded as a disease transmission area.

[0161] For each travel trajectory of a person infected with the disease during a historical period, both the starting and ending sub-regions of the travel trajectory are considered as a disease transmission area.

[0162] The specific steps for calculating the access probability of candidate activity regions are as follows:

[0163] Through the formula:

[0164]

[0165] Calculate the probability p of visiting the y-th candidate activity area corresponding to the s-th travel trajectory of the i-th disease-infected individual in the j-th historical time period. y_1 (c i ,day j ).

[0166] Among them, Ultra y_1 Uact represents the travel utility value for the y-th candidate activity region. y_1 Ultra represents the access activity utility value of the y-th candidate activity region. y_0 Uact represents the non-visit travel utility value for the y-th candidate activity region. y_0 This represents the utility value of the non-accessed activity for the y-th candidate activity region:

[0167]

[0168] Where k = 1, 0, β k1 β k2 β k3 β k4 β k5 All represent coefficients. This represents the travel time from the starting point of the s-th travel trajectory to the y-th candidate activity area. This represents the travel time from the y-th candidate activity area to the destination of the s-th travel trajectory. Let represent the shortest road network distance from the starting point of the s-th travel trajectory to the y-th candidate activity area. Represents the shortest road network distance from the y-th candidate activity area to the destination of the s-th travel trajectory, div x This represents the urban venue diversity of the y-th candidate activity area. Mindur represents the maximum activity time budget for the i-th infected subject in the y-th candidate activity region. yThis represents the minimum activity time requirement for the i-th infected subject in the y-th candidate activity region.

[0169] It should be noted that urban locations can include educational facilities (such as schools and libraries), medical facilities (such as hospitals and clinics), and dining establishments (such as restaurants). The travel time from the starting point of the s-th travel trajectory to the y-th candidate activity area is also mentioned. Travel time from the y-th candidate activity area to the destination of the s-th travel trajectory All can be calculated using the formula for calculating travel time in step 12, which is the shortest road network distance from the starting point of the s-th travel trajectory to the y-th candidate activity area. The shortest road network distance from the y-th candidate activity area to the destination of the s-th travel trajectory All of these can be calculated using the formula for calculating the shortest road network distance in step 12, which is the maximum estimated activity time of the i-th infected subject in the y-th candidate activity area. The minimum activity time budget for the i-th infected individual in the y-th candidate activity area can be calculated using the formula for calculating the maximum activity time budget described in the steps above. y The minimum activity time requirement can be calculated using the formula described in the steps above.

[0170] For example, in the first method of obtaining disease transmission areas, if the access probability of the first candidate activity area is 78%, which is greater than the access probability threshold of 60%, then the first candidate activity area is considered a disease transmission area. In the second method of obtaining disease transmission areas, since the travel trajectory indicates the actual travel status of the infected individual, it can be known that the sub-regions where the starting and ending points of the travel trajectory are located are areas that the infected individual must have visited. Therefore, both the sub-regions where the starting and ending points of the travel trajectory are considered disease transmission areas.

[0171] It is worth mentioning that by identifying multiple candidate activity areas from all accessible areas and obtaining disease transmission areas based on candidate activity areas and travel trajectories, the accuracy and practicality of the description of the travel status of disease-infected individuals have been improved.

[0172] The above-mentioned candidate activity areas will be illustrated with a specific example below.

[0173] based on Figure 3 The accessible area shown Figure 4 The medium bars represent the minimum activity duration requirement (i.e., the minimum activity time requirement mentioned above). Only reachable areas with a minimum activity duration requirement less than the maximum activity duration budget are considered candidate activity areas. Figure 4 The area with a grid.

[0174] Step 14: Based on all disease transmission areas of all disease-infected objects, generate spatiotemporal co-occurrence relationships between every two disease-infected objects in historical time periods.

[0175] The above spatiotemporal co-occurrence relationship is used to describe the situation where two corresponding disease-infected individuals appear in the same disease transmission area during a historical time period.

[0176] Specifically, for each disease-infected object, each other disease-infected object is traversed. If there is at least one disease transmission area that simultaneously belongs to the disease transmission area of ​​the disease-infected object in the historical time period and the disease transmission area of ​​other disease-infected objects in the historical time period, then the spatiotemporal co-occurrence relationship between the disease-infected object and other disease-infected objects in the historical time period is recorded as 1.

[0177] For example, during the historical time period of September 4th, all disease transmission areas of the first infected subject correspond to the second and seventh sub-regions of the target region, and all disease transmission areas of the third infected subject correspond to the third and seventh sub-regions of the target region. Both have visited the seventh sub-region, and the spatiotemporal co-occurrence relationship between the first and third infected subjects on September 4th is recorded as 1.

[0178] It is worth mentioning that the spatiotemporal co-occurrence relationship between disease-infected individuals obtained through disease transmission areas with high accuracy and realism is highly accurate.

[0179] Step 15: Calculate the transmission probability of each disease-infected object in the historical time period based on all spatiotemporal co-occurrence relationships, and calculate the disaster risk of each disease transmission area of ​​each disease-infected object in the historical time period based on the transmission probability of all disease-infected objects in the historical time period and all disease transmission areas of all disease-infected objects.

[0180] The above-mentioned transmission probability describes the probability that a disease-infected object is infectious within a historical time period, while the hazard risk describes the probability that the target disease will spread in the disease transmission area.

[0181] In some embodiments of this application, the steps described above for calculating the infectivity probability of each disease-infected object in a historical time period based on all spatiotemporal co-occurrence relationships, and for calculating the hazard risk of each disease transmission area of ​​each disease-infected object in a historical time period based on the infectivity probability of all disease-infected objects in the historical time period and all disease transmission areas of all disease-infected objects, are specifically as follows:

[0182] The first step is to obtain the diagnosis time of each infected person and, based on the diagnosis time of each infected person and all corresponding spatiotemporal co-occurrence relationships, obtain multiple infection time periods for each infected person.

[0183] Specifically, through the formula:

[0184]

[0185] Get the set of infection time periods for the i-th disease-infected object.

[0186] in, STCoR(c) represents an infection time period for the i-th infected subject. i ,c e ,day j ) represents the spatiotemporal co-occurrence relationship between the i-th and e-th disease-infected subjects in the j-th historical time period, c i Let c represent the i-th infected object. e Indicates the e-th infected person, day j This represents the j-th historical time period. Let T represent the time of diagnosis for the i-th infected individual, and let T represent the longest incubation period of the disease. Let i = 1, 2, ..., I, where I represents the number of infected individuals and j = 1, 2, ..., J, where J represents the number of historical time periods.

[0187] For example, the diagnosis time of a person infected with a disease can be obtained by analyzing their medical records.

[0188] The second step is to calculate the probability of each infected individual's infectivity during the historical time period, based on all infection time periods for each infected individual.

[0189] Specifically, through the formula:

[0190]

[0191] Calculate the transmission probability TIP(c) of the i-th infected person in the j-th historical time period. i ,day j ).

[0192] Among them, IP(c i (t) represents the probability of the i-th infected person being infected in the t-th historical time period:

[0193]

[0194] The third step is to calculate the hazard risk of each disease transmission area for each disease-infected individual during the historical time period, based on the transmission probability of all disease-infected individuals and the disease transmission areas of all disease-infected individuals during the historical time period.

[0195] Specifically, through the formula:

[0196]

[0197] Calculate the hazard risk H(taz) of the z-th disease transmission area in the j-th historical time period. z ,day j ).

[0198] Where, p z_1 (c i ,day j Let z = 1, 2, ..., Z, where z represents the number of disease transmission areas for the i-th infected person in the j-th historical time period.

[0199] For example, computer software such as MATLAB or Mathematica can be used to run the calculation formulas in the above steps to obtain the hazard risk of the disease transmission area.

[0200] It is worth mentioning that the transmission probability obtained from the spatiotemporal co-occurrence relationship can accurately describe the probability that the infected object has the ability to spread the disease in a historical period of time, and the accuracy of the disaster risk of the disease transmission area obtained based on the accurate transmission probability is improved.

[0201] Step 16: For each disease transmission area, calculate the transmission risk of the disease transmission area over a historical period based on the hazard risk of the disease transmission area.

[0202] Specifically, through the formula:

[0203] STR(taz z ,day j )=H(taz z ,day j )*V(taz z )

[0204] Calculate the transmission risk STR(taz) in the z-th disease transmission area during the j-th historical time period. z ,day j ).

[0205] Among them, V(taz) z The z-th disease transmission area represents the vulnerability of the disaster-prone environment.

[0206]

[0207] Where, β l Indicates the weighting coefficient. Let represent the kernel density estimate of the l-th type of urban site within the z-th disease transmission area, where l = 1, 2, ..., L, and L represents the number of urban site types.

[0208] It should be noted that the above transmission risk describes the risk of susceptible individuals being infected with the target disease after being exposed to a disease transmission area during a historical period. The higher the transmission risk value, the higher the risk of being infected with the target disease.

[0209] For example, computer software such as MATLAB and Mathematica can be used to run the above calculation formula to obtain the transmission risk. By obtaining the transmission risk of disease transmission areas over historical time periods, it is possible to determine whether multiple individuals are currently infected. For instance, if the transmission risk of the first disease transmission area of ​​the first infected person on September 4th is greater than the risk threshold, then individuals who visited that disease transmission area on September 4th can be considered high-risk individuals, requiring disease testing and control for these individuals.

[0210] It is worth mentioning that by obtaining the reachable areas of infected individuals based on their travel trajectories, the travel status of infected individuals can be described. Candidate activity areas are determined from the reachable areas based on the total travel time, taking into account the time dimension. This improves the accuracy of describing the travel status of infected individuals, thereby obtaining precise disease transmission areas. The transmission probability obtained from the precise disease transmission area can accurately describe the probability that infected individuals are infectious within a historical time period. The accuracy of the transmission risk of the disease transmission area obtained based on the accurate transmission probability is improved, thereby improving the accuracy of the disease transmission risk assessment of the area.

[0211] The following is an exemplary description of the spatiotemporal risk assessment device for disease transmission areas provided in this application.

[0212] like Figure 5 As shown, this application embodiment provides a spatiotemporal risk assessment device for disease transmission areas. The spatiotemporal risk assessment device 500 for disease transmission includes:

[0213] The acquisition module 501 acquires multiple travel trajectories of each infected person in the target area over multiple historical time periods, as well as the means of transportation and speed corresponding to each travel trajectory; the target area includes multiple sub-areas, and the infected persons are individuals with latent diseases of the target disease;

[0214] The calculation module 502 calculates the total travel time for each travel trajectory in the historical time period, based on the travel trajectory and the corresponding mode of transportation and speed, and obtains multiple reachable areas corresponding to the travel trajectory based on the total travel time; each reachable area is a sub-region of the target area.

[0215] The determination module 503, for each infected person, determines multiple candidate activity areas from all reachable areas corresponding to the infected person based on the total travel time of all travel trajectories of the infected person. Based on all candidate activity areas and all travel trajectories of the infected person in the historical time period, it obtains multiple disease transmission areas of the infected person in the historical time period. The disease transmission area is a sub-region visited by the infected person in the historical time period.

[0216] The generation module 504 generates a spatiotemporal co-occurrence relationship between every two disease-infected objects in a historical time period, based on all disease transmission areas of all disease-infected objects. The spatiotemporal co-occurrence relationship is used to describe the situation where two corresponding disease-infected objects appear in the same disease transmission area in a historical time period.

[0217] The disaster risk calculation module 505 calculates the infectivity probability of each disease-infected object in a historical time period based on all spatiotemporal co-occurrence relationships, and calculates the disaster risk of each disease-infected object in each disease-infected area in a historical time period based on the infectivity probability of all disease-infected objects in a historical time period and all disease transmission areas of all disease-infected objects. The infectivity probability is used to describe the probability that a disease-infected object has the ability to infect in a historical time period, and the disaster risk is used to describe the probability that the target disease will spread in the disease transmission area.

[0218] The transmission risk calculation module 506 calculates the transmission risk of each disease transmission area over a historical period based on the hazard risk of the disease transmission area.

[0219] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0220] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0221] like Figure 6 As shown, an embodiment of this application provides a terminal device, wherein the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 6 The diagram shows only one processor, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 executes the computer program D102 to implement the steps in any of the above method embodiments.

[0222] Specifically, when the processor D100 executes the computer program D102, it obtains the reachable areas that the infected person can reach based on the travel trajectory, and can describe the travel status of the infected person. Based on the total travel time, it determines candidate activity areas from the reachable areas, taking into account the time dimension information, which improves the accuracy of describing the travel status of the infected person, thereby obtaining a precise disease transmission area. The transmission probability obtained based on the precise disease transmission area can accurately describe the probability that the infected person has the ability to spread the disease in a historical time period. The accuracy of the transmission risk of the disease transmission area obtained based on the accurate transmission probability is improved, thereby improving the accuracy of the disease transmission risk assessment of the area.

[0223] The processor D100 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0224] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may be an external storage device of the terminal device D10, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device D10. Furthermore, the memory D101 may include both internal and external storage units of the terminal device D10. The memory D101 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory D101 can also be used to temporarily store data that has been output or will be output.

[0225] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0226] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0227] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a spatiotemporal risk assessment method / terminal device in a disease transmission area, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0228] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0229] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0230] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for assessing the spatiotemporal risk of disease transmission areas, characterized in that, include: The system acquires multiple travel trajectories for each infected individual in a target area across multiple historical time periods, along with the mode of transportation and travel speed corresponding to each trajectories; the target area includes multiple sub-areas, and the infected individuals are individuals with latent potential for the target disease. For each of the aforementioned historical time periods, the following steps are performed: For each travel trajectory within the historical time period, the total travel time of the travel trajectory is calculated based on the travel trajectory and the corresponding mode of transportation and speed. Multiple reachable areas corresponding to the travel trajectory are then obtained based on the total travel time. Each reachable area is a sub-region of the target area. For each of the disease-infected individuals, multiple candidate activity areas are determined from all reachable areas corresponding to the disease-infected individual based on the total travel time of all travel trajectories of the disease-infected individual. Based on all candidate activity areas and all travel trajectories of the disease-infected individual during the historical time period, multiple disease transmission areas of the disease-infected individual during the historical time period are obtained. The disease transmission area is a sub-region visited by the disease-infected individual during the historical time period. Based on all disease transmission areas of all disease-infected objects, a spatiotemporal co-occurrence relationship between every two disease-infected objects is generated within the historical time period; the spatiotemporal co-occurrence relationship is used to describe the situation where two corresponding disease-infected objects appear in the same disease transmission area within the historical time period. The infectivity probability of each disease-infected object in the historical time period is calculated based on all spatiotemporal co-occurrence relationships. The hazard risk of each disease transmission area of ​​each disease-infected object in the historical time period is calculated based on the infectivity probability of all disease-infected objects in the historical time period and all disease transmission areas of all disease-infected objects. The infectivity probability is used to describe the probability that a disease-infected object has infectivity in the historical time period, and the hazard risk is used to describe the probability that the target disease will spread in the disease transmission area. For each of the disease transmission areas, the risk of disease transmission in the historical time period is calculated based on the hazard level of the disease transmission area.

2. The spatiotemporal risk assessment method according to claim 1, characterized in that, The step of calculating the total travel time based on the travel trajectory and the corresponding mode of transportation and speed includes: Through the formula: Calculate the total travel time of the s-th travel trajectory of the ith disease-infected subject in the j-th historical time period. in, This represents the activity time of the i-th infected person at the end of the a-th travel trajectory during the j-th historical time period. Let s represent the movement speed corresponding to the a-th travel trajectory, where s, a∈n, s≠a, n represents the number of travel trajectories of the i-th infected subject in the j-th historical time period, i=1,2,...,I, where I represents the number of infected subjects, j=1,2,...,J, where J represents the number of historical time periods. The shortest road network distance for the a-th travel trajectory is: in, This represents the mode of transportation corresponding to the a-th travel trajectory. This represents the road network corresponding to the mode of transportation for the a-th travel trajectory. This indicates the node in the road network corresponding to the starting point of the a-th travel trajectory. This indicates the node in the road network corresponding to the endpoint of the a-th travel trajectory. This represents the starting point of the a-th travel trajectory. This represents the destination of the a-th travel trajectory. This indicates that, under the constraints of the road network for the mode of transportation corresponding to the a-th travel trajectory, and The shortest distance between them.

3. The spatiotemporal risk assessment method according to claim 2, characterized in that, The process of obtaining multiple reachable areas corresponding to the travel trajectory based on the total travel time includes: Through the formula: Obtain the set of all reachable areas corresponding to the s-th travel trajectory. in, This represents the set of all travel trajectories of the i-th disease-infected individual within the j-th historical time period. This represents the starting point of the s-th travel trajectory. This represents the destination of the s-th travel trajectory. Let x represent the x-th reachable region corresponding to the s-th travel trajectory, where x = 1, 2, ..., X, and X represents the number of reachable regions corresponding to the s-th travel trajectory. This represents the travel time from the starting point of the s-th travel trajectory to the x-th reachable area. This represents the travel time from the x-th reachable area to the destination of the s-th travel trajectory: in, This represents the distance from the starting point of the s-th travel trajectory to the reachable area. The shortest road network distance, Indicates from the reachable area The shortest road network distance to the destination of the s-th travel trajectory.

4. The spatiotemporal risk assessment method according to claim 1, characterized in that, The method involves determining multiple candidate activity areas from all reachable areas corresponding to the infected individual based on the total travel time of all travel trajectories. These include: For each of the accessible areas, the following steps are performed: Based on the total travel time of the travel trajectory corresponding to the reachable area, calculate the maximum estimated activity time of the disease-infected subject in the reachable area during the historical time period; Obtain all city locations within the reachable area, and calculate the minimum activity time requirement for the reachable area based on all city locations; If the maximum activity time budget is greater than the minimum activity time requirement, then the reachable area is considered as a candidate activity area for the disease-infected individuals during the historical time period.

5. The spatiotemporal risk assessment method according to claim 4, characterized in that, The step of calculating the maximum estimated activity time of the infected person in the reachable area within the historical time period based on the total travel time of the travel trajectory corresponding to the reachable area includes: Through the formula: Calculate the maximum activity time budget for the i-th infected subject in the x-th reachable area. in, This represents the total travel time of the s-th travel trajectory of the ith disease-infected individual within the j-th historical time period. This represents the travel time from the starting point of the s-th travel trajectory to the x-th reachable area. This represents the travel time from the xth reachable area to the destination of the sth travel trajectory, where x = 1, 2, ..., X, X represents the number of reachable areas corresponding to the sth travel trajectory, s ∈ n, n represents the number of travel trajectories of the i-th disease-infected subject in the j-th historical time period, i = 1, 2, ..., I, I represents the number of disease-infected subjects, j = 1, 2, ..., J, J represents the number of historical time periods; The calculation of the minimum activity time requirement for the reachable area based on all urban locations includes: Through the formula: Calculate the minimum activity time required for the i-th infected subject in the x-th reachable area. x ; Among them, NP x_l NP represents the number of city locations of type l in the x-th reachable region. x L represents the total number of urban locations in the x-th reachable region, and L represents the number of different types of urban locations. l This indicates the shortest activity duration for the first type of urban location.

6. The spatiotemporal risk assessment method according to claim 1, characterized in that, Based on all candidate activity areas and the travel trajectories of the infected individuals during the historical time period, multiple disease transmission areas of the infected individuals during the historical time period are obtained, including: For each candidate activity area, the access probability of the candidate activity area is calculated. If the access probability is greater than the access probability threshold, the candidate activity area is regarded as a disease transmission area. For each travel trajectory of the disease-infected individual during the historical time period, the sub-region where the starting point of the travel trajectory is located and the sub-region where the ending point of the travel trajectory is located are both regarded as a disease transmission area.

7. The spatiotemporal risk assessment method according to claim 6, characterized in that, The calculation of the access probability of the candidate activity region includes: Through the formula: Calculate the probability p of visiting the y-th candidate activity area corresponding to the s-th travel trajectory of the i-th disease-infected individual in the j-th historical time period. y_1 (c i ,day j ); Among them, Uact y_1 Uact represents the travel utility value for the u-th candidate activity region. y_1 Uact represents the access activity utility value of the y-th candidate activity region. y_0 Uact represents the non-visit travel utility value of the y-th candidate activity region. y_0 The non-access activity utility value of the y-th candidate activity region is: Where k = 1, 0, β k1 β k2 β k3 β k4 β k5 All represent coefficients. This represents the travel time from the starting point of the s-th travel trajectory to the y-th candidate activity area. This represents the travel time from the y-th candidate activity area to the destination of the s-th travel trajectory. This represents the shortest road network distance from the starting point of the s-th travel trajectory to the y-th candidate activity area. Represents the shortest road network distance from the y-th candidate activity area to the endpoint of the s-th travel trajectory, div x This represents the urban venue diversity of the y-th candidate activity area. Mindur represents the maximum activity time budget of the i-th disease-infected subject in the y-th candidate activity region. y This represents the minimum activity time requirement of the i-th disease-infected subject in the y-th candidate activity area.

8. The spatiotemporal risk assessment method according to claim 1, characterized in that, The generation of spatiotemporal co-occurrence relationships between every two infected individuals within the historical time period, based on all disease transmission areas of all infected individuals, includes: For each of the disease-infected objects, each other disease-infected object is traversed. If there is at least one disease transmission area that simultaneously belongs to the disease transmission area of ​​the disease-infected object in the historical time period and the disease transmission area of ​​the other disease-infected objects in the historical time period, then the spatiotemporal co-occurrence relationship between the disease-infected object and the other disease-infected objects in the historical time period is recorded as 1.

9. The spatiotemporal risk assessment method according to claim 1, characterized in that, The calculation of the infectivity probability of each disease-infected object in the historical time period based on all spatiotemporal co-occurrence relationships includes: The diagnosis time of each of the disease-infected subjects is obtained, and multiple infection time periods of each of the disease-infected subjects are obtained based on the diagnosis time of each of the disease-infected subjects and all corresponding spatiotemporal co-occurrence relationships; Based on all infection time periods for each of the disease-infected individuals, calculate the probability of each of the disease-infected individuals having the ability to infect during the historical time periods. The step of obtaining multiple infection time periods for each disease-infected individual based on their diagnosis time and corresponding spatiotemporal co-occurrence relationships includes: Through the formula: Get the set of infection time periods for the i-th disease-infected object. in, STCoR(c) represents an infection time period of the i-th disease-infected object. i ,c e ,day j ) represents the spatiotemporal co-occurrence relationship between the i-th disease-infected object and the e-th disease-infected object in the j-th historical time period, c i c represents the i-th disease-infected object. e This represents the e-th disease-infected individual, day j This represents the j-th historical time period. This represents the diagnosis time of the i-th infected person, and T represents the longest incubation period of the disease. Let i = 1, 2, ..., I, where I represents the number of infected individuals and j = 1, 2, ..., J, where J represents the number of historical time periods. The calculation of the probability of each infected individual's infectivity during the historical time period, based on all infection time periods of each infected individual, includes: Through the formula: Calculate the transmission probability TIP(c) of the i-th disease-infected object in the j-th historical time period. i ,day j ); Among them, IP(c i (t) represents the probability of the i-th disease-infected object being infected in the t-th historical time period: The step of calculating the hazard risk of each disease transmission area corresponding to the historical time period based on the transmission probability of all infected individuals and the disease transmission areas of all infected individuals includes: Through the formula: Calculate the hazard risk H(taz) in the z-th disease transmission area during the j-th historical time period. z ,day j ); Among them, p z_1 (c i ,day j Let z = 1, 2, ..., Z, where z represents the number of disease transmission areas for the i-th infected person in the j-th historical time period.

10. The spatiotemporal risk assessment method according to claim 9, characterized in that, The step of calculating the transmission risk of each disease transmission area during the historical time period based on the hazard risk of each disease transmission area includes: Through the formula: STR(taz z ,day j )=H(taz z ,day j )*V(taz z ) Calculate the transmission risk STR(taz) in the z-th disease transmission area during the j-th historical time period. z ,day j ); Among them, V(taz) z The z-th disease transmission area represents the vulnerability of the disaster-prone environment. Where, β l Indicates the weighting coefficient. Let represent the kernel density estimate of the l-th type of urban location within the z-th disease transmission area, where l = 1, 2, ..., L, and L represents the number of urban location types.

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