Emergency medical resource allocation method based on individual historical infection information

Abstract

The invention provides an emergency medical resource allocation method based on individual historical infection information. Comprising the following steps: S1, inputting urban population census, statistical data and historical infectious disease parameters; s2, generating a multi-scene urban interpersonal contact network; s3, constructing a propagation dynamics model; s4, simulating a historical propagation dynamic state and generating individual historical infection information; s5, calculating an individual vulnerability index; and S6, outputting an emergency medical resource allocation scheme. According to the method, a multi-scene urban interpersonal contact network and historical propagation dynamic simulation are adopted, and different scales and types of infectious disease outbreak situations can be adapted; through a dynamic evaluation mechanism based on historical infection information, an allocation strategy is optimized under the condition that resources are limited, efficient utilization of emergency medical resources is ensured, and therefore the epidemic prevention and control effect is maximized.

Description

Technical Field

[0001] The present invention belongs to the field of public safety emergency management; in particular, it relates to an emergency medical resource allocation method based on individual historical infection information. Background Art

[0002] In the early stages of a public health emergency, limited resources and a dearth of critical information such as the pathogen's biological characteristics, transmission pathways, and susceptible populations often force public health decision-makers to quickly develop and implement effective interventions to curb the spread of the epidemic. Rapidly identifying and prioritizing high-risk populations is crucial. The spread of infectious diseases relies on social interactions between individuals, and the structure of interpersonal contact networks effectively captures potential transmission pathways between individuals. Therefore, transmission dynamics models based on individual contact networks provide a valuable tool for understanding transmission dynamics. The allocation and utilization of medical resources, as a key intervention, can effectively curb the spread of infectious diseases within interpersonal contact networks. For example, distributing masks or vaccinating individuals can reduce the probability of infection among susceptible individuals following contact with infected individuals, thereby mitigating transmission risks.

[0003] Under limited resources, different resource allocation strategies target different individuals within a network of interpersonal contacts for intervention. While these strategies can all slow disease transmission and reduce the number of infections, their effectiveness varies significantly. When complete information about the network topology is known, resource allocation strategies based on network centrality are considered the best approach for controlling transmission. Among these, strategies based on degree centrality (i.e., prioritizing resources to individuals with the largest number of neighbors) are widely used and have been shown to be highly effective. Another widely studied set of information is spectral properties, namely the eigenvalues and eigenvectors associated with a graph's adjacency matrix and Laplacian operator. Studies have shown that transmission exhibits threshold behavior, meaning that transmission disappears when the spectral radius falls below a certain threshold. Some studies, based on models of transmission dynamics in heterogeneous networks, optimize resource allocation at each node to control outbreaks with minimal total cost and an asymptotic exponential decay rate. However, the topology of interpersonal contact networks in reality is often unknown, making intervention strategies based on global network information difficult to directly apply to actual public health systems.

[0004] Currently, there are also many indirect strategies that use local network information to control the spread of infectious diseases in interpersonal contact networks. One of the simplest strategies is to randomly allocate resources, but it usually requires a high resource coverage rate to achieve ideal results. The acquaintance strategy indirectly identifies high-influence nodes by randomly selecting individuals and preferentially allocating resources to their neighbors. Compared with the random strategy, the acquaintance strategy is more likely to select nodes with higher degrees, but it still requires a lot of resources to achieve effective control. In addition, although the acquaintance strategy does not require global network information, it still needs to understand the individual's social contact information, which is usually complex and sensitive. Another method uses electronic health records to identify high-risk individuals and verifies its feasibility, but only considers the number and time of infection, and does not involve more detailed individual medical records data and spread control applications. Through the above analysis, the problems and defects of the existing technology are:

[0005] (1) Existing methods based on network topology mainly rely on the connection relationship between nodes in the interpersonal contact network to identify high-influence nodes, but often ignore the behavioral characteristics and epidemiological information of the nodes themselves in the historical spread of infectious diseases;

[0006] (2) Existing methods based on global network information require complete topological information of the interpersonal contact network. However, in practical applications, this information is usually implicit and unknown. It is a very challenging task to accurately understand each node and its edges and construct a real communication network.

[0007] (3) Existing methods based on local network information rely on complex and potentially inaccurate individual contact data. The actual implementation of these strategies not only faces data quality issues, but also often requires sufficient resources to achieve better control effects.

[0008] Based on the above-mentioned deficiencies in the existing technology, there is an urgent need for a method to effectively solve the above-mentioned technical deficiencies. Summary of the Invention

[0009] The purpose of this invention is to provide a method for allocating emergency medical resources based on individual historical infection information. This method addresses the practical challenges of limited resource reserves and insufficient information on high-risk populations in the early stages of a public health emergency. This method can enhance public health emergency response capabilities and provide a scientific basis for policymakers.

[0010] The present invention relates to a method for allocating emergency medical resources based on individual historical infection information, which is mainly achieved through the following technical solutions:

[0011] S1, input city census, statistical data and historical infectious disease parameters; historical infectious disease parameters include: basic reproduction number and triangular distribution parameters of infectious period;

[0012] S2, Generate multi-scenario urban interpersonal contact networks: Use the SynthPops method, based on urban census and statistical data, to construct a multi-scenario urban interpersonal contact network that includes family, school, workplace, and community contact layers;

[0013] S3, constructing a transmission dynamics model: generating parameters of the transmission dynamics model of historically relevant infectious diseases based on the basic reproduction number and triangular distribution parameters of the infectious period of historically relevant infectious diseases;

[0014] S4, simulate historical transmission dynamics and generate individual historical infection information: Use the random binomial method to simulate historical transmission dynamics on multi-scenario urban interpersonal contact networks and generate individual historical infection information; the individual historical infection information includes whether the individual was infected in each historical transmission season and the infection time;

[0015] S5, calculate individual vulnerability index: Based on individual historical infection information, calculate the individual vulnerability index value, assess their infection risk, and determine the individual allocation priority of emergency medical resources accordingly;

[0016] S6, output emergency medical resource allocation plan: the generated individual emergency medical resource allocation priority list is used as a reference for policy makers to allocate emergency medical resources.

[0017] Preferably, in S1, the urban population census and statistical data include: contact matrix between different age groups, population age distribution, family size distribution, school size distribution, workplace size distribution, enrollment rate and employment rate data; historical infectious disease parameters include: triangular distribution parameters of basic reproduction number and infectious period, time span of electronic health records of individual historical infection information, population size covered by resources and effectiveness of resources;

[0018] The contact matrix between different age groups is C, where the element c ij represents the average contact frequency between age group i and age group j, the basic reproduction number R0 follows a triangular distribution (a1, b1, c1), the infectious period IP follows a triangular distribution (a2, b2, c2), the electronic health record covers data from η historical transmission seasons, the resource coverage is c%, and the resource effectiveness is R e .

[0019] Preferably, in S2, the specific method of generating the multi-scenario city interpersonal contact network is:

[0020] Using the SynthPops method, population characteristics are captured through census and statistical survey data. Age-specific contact matrices across different contact scenarios are then used to infer high-resolution age-mixed contact patterns within each scenario. This generates synthetic contact networks encompassing four primary contact settings: family, school, workplace, and community. Within these settings, individuals are statically connected through multiple, multi-class edges, reflecting interaction patterns at different levels of contact.

[0021] Step 1: Generate households of different sizes based on the household size distribution in the city and assign members based on the population age distribution and the family contact matrix. All nodes within each household are connected by edges.

[0022] Step 2: Allocate students of appropriate age to kindergartens, primary schools, middle schools, vocational schools, and colleges based on school size and type, and randomly generate connections between students, teachers, and students using a Poisson distribution based on the school contact matrix.

[0023] Step 3: Randomly assign employees to workplaces of different sizes according to employment rate and workplace size, and randomly generate contact between employees using Poisson distribution based on the workplace contact matrix;

[0024] Step 4: Based on the community contact matrix, calculate the average number of contacts of an individual and randomly establish connections between the remaining nodes to match the community contact pattern;

[0025] Step 5: Finally, a multi-scenario synthetic contact network that conforms to the city’s population and contact characteristics is generated.

[0026] Preferably, in S3, the specific method of constructing the propagation dynamics model is:

[0027] Since each individual can be in one of three states: susceptible (S), infected (I) or recovered (R), and the infection rate of the infectious disease is defined as β and the recovery rate is γ, then in the contact network, if a susceptible individual has k infected neighbors, the probability of being infected is φ = 1-e -βk Once infected, individuals recover after an average infectious period of IP = 1 / γ days. The infection rate β and the basic reproduction number R0 satisfy the following relationship:

[0028]

[0029] in, <k>and <k 2 >represent the mean and mean square of the degree of all nodes in the network respectively. In order to simulate the data of η historical propagation seasons, η random numbers R are drawn from the given R0 triangular distribution. 0，j (j=1,...,η), and calculate the infection rate β for each historical transmission season j Similarly, n random numbers IP are drawn from the triangular distribution of IP 0，j (j=1,...,η), and then calculate the recovery rate γ for each historical propagation season j .

[0030] Preferably, in S4, the specific method of simulating historical transmission dynamics and generating individual historical infection information is:

[0031] (1) Initial state: At the beginning of the simulation, all individuals are susceptible, except for the randomly selected initial infected node;

[0032] (2) Transmission process: In a contact network, each infected individual spreads the virus with a probability φ, making the susceptible individuals connected to it potentially infected. The infection event can be modeled as a binomial random process: there are n independent trials (contacts), and the probability of success (infection) in each trial is φ. Then the number of successes X satisfies the binomial distribution: X~Bin(n,φ); each contact is an independent event. If a susceptible person contacts multiple infected persons at the same time, the total infection probability is calculated by the sum of multiple independent infection events.

[0033] (3) Update state: Susceptible individuals are transformed into infected states through contact with infected individuals. Infected individuals recover after a certain infectious period and enter the recovered state, and are no longer contagious.

[0034] (4) Simulation output: By simulating the spread dynamics, we can determine whether each individual is infected and when they were infected, providing support for the allocation of emergency medical resources.

[0035] Preferably, in S5, the specific method for calculating the individual vulnerability index is:

[0036] Assume that in η historical transmission seasons, individual i has η infections i times, indicating that individual i has η i The historical infection records of seasons; the infection time of individual i in season j is τ i，j It represents the τth time since the beginning of the season i，j If individual i is not infected in season j, then τ i，j =0; the calculation of individual vulnerability is as shown in the formula:

[0037]

[0038] Among them, V i represents the vulnerability of individual i, τ i，j represents the infection time of individual i in season j, R 0，j 、IP j represent the basic reproduction number and infection period of season j respectively; if individual i has never been infected, then η i = 0, then for all j, η i，j =0, in this case, define V i =0, indicating that its vulnerability index is the lowest; otherwise, V i The larger , the more vulnerable individual i is, and thus the ranking of individual i is also higher; in seasons with lower transmissibility and pathogenicity, individuals infected earlier have higher vulnerability.

[0039] Preferably, in S6, the specific method of outputting the emergency medical resource allocation plan is: sorting individuals in the network from high to low according to the vulnerability index, and preferentially allocating resources to individuals with the highest ranking. For example, when resources can only cover 10% of the population, this strategy recommends allocating resources indiscriminately to the top 10% of individuals.

[0040] The present invention has the following advantages:

[0041] (1) The method of the present invention achieves accurate allocation of emergency medical resources at the individual level: the method calculates vulnerability indicators based on individual historical infection information and determines the priority allocation order of emergency medical resources accordingly, which is of great significance for public health departments to respond to future outbreaks of new and emerging infectious diseases.

[0042] (2) The method of the present invention reduces the dependence on network topology information: even though the method uses network simulation to generate electronic health records, in the individual selection process, resources are allocated only based on historical infectious disease characteristics and individual infection records, without relying on the network topology or its mapping relationship. This strategy successfully and effectively avoids the challenges brought about by the unknown propagation network topology in practical applications, and improves the applicability of the method in complex real-world scenarios.

[0043] (3) The method of the present invention improves the fairness and efficiency of resource allocation: the method adopts a multi-scenario urban interpersonal contact network and historical transmission dynamic simulation, which can adapt to infectious disease outbreak scenarios of different scales and types; through a dynamic evaluation mechanism based on historical infection information, the allocation strategy is optimized under limited resources, ensuring the efficient use of emergency medical resources, thereby maximizing the effect of epidemic prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flow chart of the method involved in the present invention;

[0045] Figure 2 A modular detailed diagram of the method involved in the present invention;

[0046] Figure 3 A comparison chart of different resource allocation strategies under different resource coverage rates;

[0047] Figure 4 A comparison chart of different resource allocation strategies under different resource availability;

[0048] Figure 5 A comparison chart of different resource allocation strategies under different basic reproduction numbers. DETAILED DESCRIPTION

[0049] The present invention will be described in detail below with reference to specific embodiments. It should be noted that the following embodiments are only for further explanation of the present invention, but the protection scope of the present invention is not limited to the following embodiments.

[0050] Example 1

[0051] This embodiment provides an emergency medical resource allocation method based on individual historical infection information. Figure 1 and Figure 2 As shown, the following steps are included:

[0052] S1. Input city census and statistical data, including population age distribution, family size distribution, school size distribution, workplace size distribution, enrollment rate, employment rate, and the contact matrix C between different age groups in four scenarios: family, school, workplace, and community. k , where element c ij represents the average contact frequency between age group i and age group j in scene k;

[0053] Input historically relevant infectious disease parameters, including the basic reproduction number R0 and infectious period IP. R0 follows a triangular distribution (a1, b1, c1), and IP follows a triangular distribution (a2, b2, c2).

[0054] The input electronic health records containing historical infection information of individuals span a time span η, covering data for η historical transmission seasons of relevant infectious diseases.

[0055] The input resource coverage rate is c%, that is, the total amount of emergency medical resources can cover c% of the nodes in the network.

[0056] Input resource validity is R e , assuming that the probability of a susceptible individual being infected in the contact network is φ, when the susceptible individual and the infectious individual have resources, the infection probability is (1-R e )φ, when both parties have resources, the infection probability is further reduced to (1-R e ) 2 φ.

[0057] S2. Using the SynthPops method, we use census and statistical survey data to understand population characteristics. We then infer high-resolution age-mixed contact patterns in each contact scenario based on the age-divided contact matrix in different contact scenarios, thereby generating a synthetic contact network. This network includes four major contact scenarios: family, school, workplace, and community. In these scenarios, individuals are statically connected through multiple edges, reflecting the interaction patterns of different contact scenarios. The specific process includes:

[0058] Step 1: Generate households of different sizes based on the urban household size distribution and assign members based on the population age distribution and the family contact matrix. All nodes within each household are connected by edges.

[0059] Step 2: Allocate students of appropriate age to kindergartens, primary schools, middle schools, vocational schools, and colleges based on school size and type, and randomly generate connections between students, teachers, and students using a Poisson distribution based on the school contact matrix.

[0060] In step 3, employees are randomly assigned to workplaces of different sizes based on employment rate and workplace size, and contacts between employees are randomly generated using Poisson distribution based on the workplace contact matrix.

[0061] Step 4: Based on the community contact matrix, calculate the average number of contacts of an individual and randomly establish connections between the remaining nodes to match the community contact pattern.

[0062] Step 5: Finally, a multi-scenario synthetic contact network that conforms to the city’s population and contact characteristics is generated.

[0063] S3. Build a dynamic model for the spread of historically correlated infectious diseases: Taking the spread of seasonal influenza as an example, each individual can be in one of three states: susceptible (S), infected (I), or recovered (R). Define the infectious disease's transmission rate as β and the recovery rate as γ. Then, in a contact network, if a susceptible individual has k infected neighbors, the probability of being infected is φ = 1-e -βk Once infected, individuals recover after an average infectious period of IP = 1 / γ days. The infection rate β and the basic reproduction number R0 satisfy the following relationship:

[0064]

[0065] in, <k>and <k 2 >represent the mean and mean square of the degrees of all nodes in the network respectively.

[0066] In order to simulate the spread of historically related infectious diseases, assuming that the spread data of historical influenza seasons are known, it is necessary to extract the infection rate and infectious period parameters of the historical transmission season from a given triangular distribution. The specific process includes:

[0067] 1) Draw n random numbers R from the given R0 triangular distribution 0，j (j=1, ..., η), as the basic reproduction number for each historical propagation season;

[0068] 2) According to the R of each season 0，j value, calculate the corresponding infection rate β j ;

[0069] 3) Extract n random number IPs from the triangular distribution of IPs 0，j (j=1,...,η), as the infectious period of each historical transmission season;

[0070] 4) Based on the IP of each season 0，j value, and the formula IP = 1 / γ, calculate the corresponding recovery rate θ j .

[0071] S4. Use the random binomial method to simulate historical transmission dynamics on the synthetic contact network. This simulation process gradually updates the population state in time steps and generates historical infection records for η seasons under different seasonal parameters.

[0072] The specific process includes:

[0073] 1) Initial state: At the beginning of the simulation, all individuals are susceptible, except for the randomly selected initial infected node;

[0074] 2) Transmission process: In a contact network, each infected individual spreads the virus with a probability of φ, potentially infecting the susceptible individuals connected to them. The infection event can be modeled as a binomial random process: there are n independent trials (contacts), each with a probability of success (infection) of φ, then the number of successes X satisfies the binomial distribution: X ~ Bin(n, φ). Each contact is an independent event; if a susceptible individual comes into contact with multiple infected individuals simultaneously, the total infection probability is calculated from the combined results of multiple independent infection events.

[0075] 3) Update state: Susceptible individuals become infected through contact with infected individuals. Infected individuals recover after a certain infectious period and enter the recovered state, no longer contagious.

[0076] 4) Simulation output: By simulating the spread of infection, we can determine whether each individual is infected and when they became infected, providing support for the allocation of emergency medical resources.

[0077] S5. Assume that in η historical transmission seasons, individual i has η infections. i times, indicating that individual i has η i The historical infection records of seasons; specifically, the infection time of individual i in season j is τ i，j It represents the τth time since the beginning of the season i，j If individual i is not infected in season j, then τ i，j =0; R 0，j and IP j represent the basic reproduction number and infection period of season j respectively; based on this, the individual vulnerability index is defined as follows:

[0078]

[0079] Among them, V i represents the vulnerability of individual i, τ i，j represents the infection time of individual i in season j, R 0，j 、IP j represent the basic reproduction number and infection period of season j respectively; if individual i has never been infected, then η i = 0, then for all j, τ i，j =0, in this case, define V i =0, indicating that its vulnerability index is the lowest; otherwise, V i The larger , the more vulnerable individual i is, and thus the ranking of individual i is also higher; in seasons with lower transmissibility and pathogenicity, individuals infected earlier have higher vulnerability.

[0080] S6. Rank individuals in the network from high to low according to their vulnerability index, prioritizing resource allocation to the top-ranked individuals. Specifically, when resources are limited (for example, resources only cover 10% of the population), this strategy recommends allocating resources indiscriminately to the top 10% of individuals. In emergency response to emerging infectious diseases, this scheme provides a feasible solution for the allocation of emergency medical resources. Using COVID-19 as an example, this strategy was validated using a transmission simulation.

[0081] The data sets involved in this embodiment are shown in Table 1:

[0082] Table 1 Network dataset Number of nodes Number of connected edges Student Network 4629 273104 Montreal Network 103425 630893 Shenzhen City Interpersonal Contact Network 10000 123109

[0083] The resource allocation strategies involved in this embodiment include topology-free strategy (TFS), maximum connection strategy (MCS), random strategy (RDS), acquaintance strategy (AQS), and no resource strategy (NOS):

[0084] 1) Topology-free strategy (TFS): Rank nodes from high to low according to their vulnerability index values, and prioritize resources for the top c% nodes with the highest vulnerability rankings.

[0085] 2) Most connected strategy (MCS): prioritizes allocating resources to the top c% of nodes with the highest degree in the network;

[0086] 3) Random strategy (RDS): randomly select c% of nodes in the network and allocate resources to them;

[0087] 4) Acquaintance Strategy (AQS): Randomly select a node in the network, then randomly select a neighbor of the node and allocate resources to it, repeating this process until the resource coverage reaches c%;

[0088] 5) No resource strategy (NOS): No resources are allocated to nodes. c% represents the resource coverage, that is, the total amount of resources can satisfy c% of the nodes in the network.

[0089] The peak times and magnitudes of infection and hospitalization for different resource allocation strategies involved in this embodiment are shown in Table 2:

[0090] Table 2

[0091] Table 2 shows the timing and magnitude of peak infection and hospitalization rates for different resource allocation strategies, assuming a resource coverage rate of 10%, a resource effectiveness of 50%, and a basic reproduction number of 2.5. The results shown are the average of 100 simulated outbreak experiments. Results in bold represent the best performance under specific evaluation criteria, while underlined results represent suboptimal strategies. Generally, later and lower peaks indicate a more effective strategy. TFS significantly outperformed RDS and AQS in most scenarios, and its performance approached the theoretically optimal MCS.

[0092] The performance ratio of the no-topology strategy involved in this embodiment to the maximum connection strategy is shown in Table 3:

[0093] Table 3

[0094] Table 3 shows the effectiveness ratio (CERM) of TFS relative to MCS. For the four evaluation criteria, CERM quantifies the average improvement ratio of TFS compared to MCS in delaying the peak days of infection and hospitalization, as well as the average reduction ratio in reducing the scale of infection and hospitalization peaks. Specifically, taking the evaluation criterion of infection peak as an example, assuming that the resource coverage rate is 10% and the resource effectiveness is 10%, TFS reduces the infection peak by a people, while MCS reduces the infection peak by b people, then the effect ratio of TFS relative to MCS is a 10 / b 10 Finally, CERM takes the average of the calculated effect ratios for all parameter combinations to comprehensively evaluate the overall effectiveness of TFS compared to MCS. TFS performed well across all networks and evaluation criteria, with an average performance of at least 90% of MCS. In the Montreal network and Shenzhen contact network, TFS achieved an average CERM of 97%, demonstrating that TFS's performance is very close to that of MCS.

[0095] The relative advantage ratio of the no-topology strategy involved in this embodiment compared to the random strategy is shown in Table 4, and the relative advantage ratio of the no-topology strategy compared to the acquaintance strategy is shown in Table 5:

[0096] Table 4

[0097] Table 5

[0098] Tables 4 and 5, respectively, show the relative superiority ratio (CSPR / CSPA) of TFS compared to RDS and AQS. This ratio measures the proportion of parameter combinations that resulted in an outbreak in which TFS outperformed or performed equally well. Evaluation criteria include delaying the peak time of infection and hospitalization, as well as reducing the peak size of infection and hospitalization. CSPR / CSPA calculates the ratio of the number of parameter combinations in which TFS outperformed RDS / AQS on these metrics to the total number of parameter combinations that resulted in an outbreak. For example, a CSPR / CSPA for peak infection of 90% indicates that TFS outperformed RDS / AQS in reducing the peak size of infection in 90% of parameter combinations. TFS outperformed RDS and AQS in the vast majority of parameter combinations. Overall, the CSPR value was slightly higher than the CSPA. TFS performed particularly well in the student network and Shenzhen contact network: in the former, it outperformed RDS and AQS in over 75% of parameter combinations, and in the latter, this percentage reached 91%.

[0099] Example 2

[0100] This embodiment provides an emergency medical resource allocation method based on individual historical infection information. In the Montreal network, when resource effectiveness is greater than 50%, TFS can almost completely prevent the outbreak. Therefore, the sensitivity analysis only shows the results of the student network and the Shenzhen contact network. Figure 3 As shown in the figure, the impact of changes in resource coverage on various resource allocation strategies under four evaluation criteria is demonstrated. The resource coverage is increased from 10% to 100% in increments of 10%, and the other parameters remain at the initial settings, that is, the resource effectiveness is 50% and the basic reproduction number is 2.5. As the resource coverage increases, the time of the peak of infection and hospitalization is gradually delayed, and the peak size is significantly reduced. Overall, TFS and MCS perform the best. In the student network, TFS generally performs better than MCS, while in the Shenzhen contact network, the performance of TFS and MCS is almost exactly the same.

[0101] See Figure 4 As shown in the figure, the impact of changes in resource effectiveness on various resource allocation strategies under four evaluation criteria is demonstrated. The resource effectiveness is increased from 10% to 100% in increments of 10%, and the other parameters remain at the initial settings, that is, the resource coverage is 10% and the basic reproduction number is 2.5. Similarly, with the increase in resource effectiveness, the overall trend is that the peak time of infection and the peak time of hospitalization are gradually delayed, and the peak shows a clear downward trend. When resource effectiveness is high, due to the limited number of simulations leading to infectious disease outbreaks and the low hospitalization rate, in some cases the peak changes will fluctuate to a certain extent and fail to strictly maintain a monotonic decrease, but the overall trend is still obvious. Overall, the performance of TFS and MCS is relatively superior, and TFS is better than MCS in most scenarios.

[0102] See Figure 5 Figure 2 shows the impact of varying basic reproduction numbers on various resource allocation strategies under four evaluation criteria. For the emerging infectious disease COVID-19, basic reproduction numbers were set to 1.5, 2.0, and 2.5, respectively, while other parameters remained at their initial settings: resource coverage of 10% and resource effectiveness of 50%. It can be observed that TFS performs well under different scenarios, further demonstrating the robustness of the strategy and providing an important reference for optimizing resource allocation.

[0103] In summary, the method of the present invention calculates the vulnerability index based on individual historical infection information, and determines the priority allocation order of emergency medical resources based on this, which is of great significance for public health departments to respond to future outbreaks of new and emerging infectious diseases; the method of the present invention reduces the dependence on network topology information: even if the method uses contact network simulation to generate electronic health records, in the individual selection process, resources are allocated only based on historical infectious disease characteristics and individual infection records, without relying on network topology or its mapping relationship. This strategy successfully and effectively avoids the challenges brought about by the unknown topology of the propagation network in practical applications, and improves the applicability of the method in complex real-life scenarios; the method of the present invention improves the fairness and efficiency of resource allocation: the method adopts multi-scenario urban interpersonal contact networks and historical propagation dynamic simulations, which can adapt to infectious disease outbreak scenarios of different scales and types; through a dynamic evaluation mechanism based on historical infection information, the allocation strategy is optimized under limited resources, ensuring the efficient use of emergency medical resources, thereby maximizing the epidemic prevention and control effect.

[0104] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various variations or modifications within the scope of the claims, which do not affect the essence of the present invention.< / k> < / k>

Claims

1. A method for allocating emergency medical resources based on individual historical infection information, characterized in that: The following steps are involved: S1, input city census, statistical data and historical infectious disease parameters; historical infectious disease parameters include: basic reproduction number and triangular distribution parameters of infectious period; S2, Generate multi-scenario urban interpersonal contact networks: Use the SynthPops method, based on urban census and statistical data, to construct a multi-scenario urban interpersonal contact network that includes family, school, workplace, and community contact layers; S3, constructing a transmission dynamics model: generating parameters of the transmission dynamics model of historically relevant infectious diseases based on the basic reproduction number and triangular distribution parameters of the infectious period of historically relevant infectious diseases; S4, simulate historical transmission dynamics and generate individual historical infection information: Use the random binomial method to simulate historical transmission dynamics on multi-scenario urban interpersonal contact networks and generate individual historical infection information; the individual historical infection information includes whether the individual was infected in each historical transmission season and the infection time; S5, calculate individual vulnerability index: Based on individual historical infection information, calculate the individual vulnerability index value, assess their infection risk, and determine the individual allocation priority of emergency medical resources accordingly; S6, output emergency medical resource allocation plan: the generated individual emergency medical resource allocation priority list is used as a reference for policy makers to allocate emergency medical resources.

2. The method for allocating emergency medical resources based on individual historical infection information according to claim 1, characterized in that: In S1, the city population census and statistical data include: contact matrix between different age groups, population age distribution, family size distribution, school size distribution, workplace size distribution, enrollment rate and employment rate data. The contact matrix between different age groups is C, where the element c ij represents the average contact frequency between age group i and age group j, the basic reproduction number R0 follows a triangular distribution (a1, b1, c1), the infectious period IP follows a triangular distribution (a2, b2, c2), the electronic health record covers data from η historical transmission seasons, the resource coverage is c%, and the resource effectiveness is R e .

3. The method for allocating emergency medical resources based on individual historical infection information according to claim 1, characterized in that: In S2, the specific method of generating a multi-scenario urban interpersonal contact network is: Step 1: Generate households of different sizes based on the household size distribution in the city and assign members based on the population age distribution and the family contact matrix. All nodes within each household are connected by edges. Step 2: Allocate students of appropriate age to kindergartens, primary schools, middle schools, vocational schools, and colleges based on school size and type, and randomly generate connections between students, teachers, and students using a Poisson distribution based on the school contact matrix. Step 3: Randomly assign employees to workplaces of different sizes according to employment rate and workplace size, and randomly generate contact between employees using Poisson distribution based on the workplace contact matrix; Step 4: Based on the community contact matrix, calculate the average number of contacts of an individual and randomly establish connections between the remaining nodes to match the community contact pattern; Step 5: Finally, a multi-scenario synthetic contact network that conforms to the city’s population and contact characteristics is generated.

4. The method for allocating emergency medical resources based on individual historical infection information according to claim 1, characterized in that: In S3, the specific method of constructing the propagation dynamics model is: Since each individual can be in three states: susceptible, infected or recovered; Define the infectious rate of an infectious disease as β and the recovery rate as γ. Then, in a contact network, if a susceptible individual has k infected neighbors, the probability of being infected is φ = 1-e -βk Once infected, individuals recover after an average infectious period of IP = 1 / γ days. The infection rate β and the basic reproduction number R0 satisfy the following relationship: in, <k>、 <k 2 >represent the mean and mean square of the degrees of all nodes in the network respectively.< / k> 5. The method for allocating emergency medical resources based on individual historical infection information according to claim 1, characterized in that: In S4, the specific method of simulating historical transmission dynamics and generating individual historical infection information is: (1) Initial state: At the beginning of the simulation, all individuals are susceptible, except for the randomly selected initial infected node; (2) Transmission process: In the contact network, each infected individual spreads the virus with a probability of φ, making the susceptible individuals connected to it possibly infected. The infection event can be modeled as a binomial random process: there are n independent contact trials, and the probability of successful infection in each trial is φ, then the number of successes X satisfies the binomial distribution: X ~ Bin(n, φ); each contact is an independent event. If a susceptible person contacts multiple infected persons at the same time, the total infection probability is obtained by the comprehensive calculation of multiple independent infection events; (3) Update state: Susceptible individuals are transformed into infected states through contact with infected individuals. Infected individuals recover after a certain infectious period and enter the recovered state, and are no longer contagious. (4) Simulation output: By simulating the spread dynamics, we can determine whether each individual is infected and when they were infected, providing support for the allocation of emergency medical resources.

6. The method for allocating emergency medical resources based on individual historical infection information according to claim 1, characterized in that: In S5, the specific method for calculating the individual vulnerability index is: Assume that in η historical transmission seasons, individual i has η infections i times, indicating that individual i has η i The historical infection records of seasons; the infection time of individual i in season j is τ i，j It represents the τth time since the beginning of the season i，j If individual i is not infected in season j, then τ i，j =0; the calculation of individual vulnerability is as shown in the formula: Among them, V i represents the vulnerability of individual i, τ i，j represents the infection time of individual i in season j, R 0，j 、IP j represent the basic reproduction number and infection period of season j respectively; if individual i has never been infected, then η i = 0, then for all j, τ i，j =0, in this case, define V i =0, indicating that its vulnerability index is the lowest.

7. The method for allocating emergency medical resources based on individual historical infection information according to claim 1, characterized in that: In S6, the specific method of outputting the emergency medical resource allocation plan is: sorting the individuals in the network from high to low according to the vulnerability index, and preferentially allocating resources to the individuals with higher rankings.

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