Epidemic prevention and control oriented primary health resource allocation method and system

Abstract

The invention discloses an epidemic prevention and control oriented primary health resource allocation method and system, and relates to the technical field of public health epidemic prevention and control, and the method comprises the steps: predicting the epidemic trend of each community based on a distributed space resource coupling SEIR model, introducing parameters such as a medical resource index, constructing a community-side preference function and a medical facility-side preference function, and carrying out the optimization of the community-side preference function and the medical facility-side preference function; and solving a matching relationship between the community and the medical facility at each moment and an isolation resource allocation scheme under the edge cloud collaborative architecture. According to the scheme, the service priority is comprehensively measured by utilizing the peak value advance, the resource occupation proportion and the trust factor, the configuration priority of the resource tension community is improved through the urban and rural compensation item, the urban and rural gap is reduced, the sorting result is uploaded by adopting edge preprocessing and encryption, the data transmission quantity is reduced, and the scheduling timeliness, fairness and security are improved. And closed-loop linkage of epidemic situation propagation suppression and resource dynamic scheduling is realized.

Description

A method and system for primary health resource allocation in response to epidemic prevention and control Technical Field

[0001] This invention relates to the field of public health epidemic prevention and control technology, and in particular to a method and system for primary health resource allocation for epidemic prevention and control. Background Technology

[0002] In existing technologies, on the one hand, epidemic assessment is mostly based on traditional SEIR epidemiological models for trend prediction at a single region and macro scale. These models typically assume a uniform population mix and do not delve into the spatial differences between multiple primary communities. They also lack characterization of the medical resource carrying capacity of different communities, making it difficult to reflect the dynamic coupling relationship between community epidemic development and medical resource pressure. On the other hand, the allocation and scheduling of primary health resources rely heavily on administrative experience, fixed indicators, or simple bed occupancy statistics. They lack quantitative decision-making mechanisms that take multi-community epidemic evolution indicators and available medical facility capacity as inputs. Furthermore, they do not introduce dual-side preferences from the community and medical facility sides to constrain requirements such as cross-community transfer distance and medical load balancing. This makes it difficult to timely and accurately match the actual prevention and control needs of each community at different times. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides a method for allocating primary health resources for epidemic prevention and control, which solves the problem of the difficulty in dynamically and accurately allocating primary health resources based on changes in the epidemic situation in multiple communities and the carrying capacity of medical resources.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a method for primary health resource allocation for epidemic prevention and control, which includes dividing the prevention and control area into communities, introducing population state variables and medical resource indices in each community, constructing a SEIR dynamic prediction model, and performing numerical solutions within a preset prediction period to obtain the epidemic evolution indicators of each community.

[0007] Based on the epidemic evolution indicators of each community and the available capacity of each medical facility, a dynamic spatiotemporal resource matching objective function is established to solve the resource allocation relationship between the community and the medical facility at each time point, and to obtain the initial health resource allocation scheme between the community and the medical facility at each time point.

[0008] Based on the epidemic evolution indicators, medical resource index and initial health resource allocation scheme of each community, a community-side preference function and a medical facility-side preference function are constructed to quantify the preference order between communities and medical facilities at each time point.

[0009] Using the community-side preference function and the medical facility-side preference function as inputs, a stable matching algorithm is executed to adjust the initial health resource allocation scheme under the constraints of the matching relationship, and generate the final matching result between the community and the medical facility.

[0010] As a preferred embodiment of the primary health resource allocation method for epidemic prevention and control described in this invention, the SEIR dynamic prediction model includes dividing the prevention and control area into M primary communities, and in the... Define population state variables in each community.

[0011]

[0012] in, Indicates community At any moment Total population; Indicates community At any moment The number of susceptible individuals; Indicates community At any moment The number of exposed individuals; Indicates community At any moment The number of symptomatic infections; Indicates community At any moment The number of recoveries.

[0013] For the first Each community at any time The standardized availability rates of hospital beds, medical staff, and epidemic prevention materials were collected, and a medical resource index was constructed according to preset weighting coefficients.

[0014]

[0015] in, Indicates community At any moment The medical resource index; Indicates community At any moment Bed availability rate; Indicates community At any moment The attendance rate of medical staff; Indicates community At any moment The availability rate of epidemic prevention materials; This indicates the preset bed weighting coefficient; This indicates the preset weighting coefficient for medical staff; This indicates the preset material weighting coefficient.

[0016] Communities are determined based on medical resource index. At any moment The transmission rate of symptomatic infected individuals, the transmission rate of exposed individuals, and the recovery rate.

[0017] The symptomatic infection rate is introduced into the community at any given time, based on the baseline infection rate. The medical resource index and the preset resource sensitivity coefficient are determined according to a linear relationship.

[0018] The infection rate of exposed individuals is based on the baseline infection rate of exposed individuals, and incorporates community data at any given time. The detection coverage is determined as a linear function.

[0019] The recovery rate is based on the baseline recovery rate, according to the community at any given time. The power function relationship of the medical resource index is determined.

[0020] The SEIR dynamic prediction model incorporates the impact of population movement between different communities; based on population movement data between communities, it determines the time frame at which... From the community Inflow into the community The intensity of population movement, and based on the time of each community The ratio of infected individuals to the total population is used to weight and sum the population mobility intensity to obtain the community... At any moment External infection pressure.

[0021]

[0022] in, Indicates community At any moment External infection pressure; Indicates the total number of communities within the area; Indicates the index of the source community, indicating from the first... The flow of the first community to the first One community; Indicates at time From the community Inflow into the community The intensity of population movement; Indicates community At any moment The number of infected people; Indicates community At any moment The total population.

[0023] As a preferred embodiment of the primary health resource allocation method for epidemic prevention and control described in this invention, the SEIR dynamic prediction model includes, based on the traditional SEIR model, incorporating the community's time-varying... External infection pressure With the number of susceptible individuals The product of these terms is introduced as an additional infection term into the susceptible population evolution equation and the exposed population evolution equation; this additional infection term reflects the impact of externally input infections on the community. The impact of the number of exposed individuals.

[0024] Based on the symptomatic infection rate, exposed person infection rate, recovery rate, and additional infection item, a set of SEIR transmission dynamics equations is established for each community to characterize the relationship between the number of various population groups and time. Within a preset prediction period, the SEIR transmission dynamics equations for each community are numerically solved to obtain the number of susceptible persons, exposed persons, symptomatic infected persons, and recovered persons in each community at each prediction time. Based on the change in the number of symptomatic infected persons in each community within the prediction period, the predicted peak time of the epidemic in each community and the number of symptomatic infected persons at the peak are determined as indicators of the epidemic evolution in each community.

[0025] As a preferred embodiment of the primary health resource allocation method for epidemic prevention and control described in this invention, the dynamic spatiotemporal resource matching objective function includes: under the constraint of medical facility capacity, uniformly and quantitatively evaluating the isolation resource allocation schemes for different communities and at different times, and determining the optimal scheme for each medical facility to allocate isolation resources to each community at each time.

[0026] The sum of the number of infected individuals and exposed individuals in the same community at the same time is subtracted from the number of isolation resources in the community, and the difference is weighted by a dynamic weighting coefficient. The time offset is determined based on the time difference between the predicted peak time of the epidemic and the time of resource allocation, and is weighted by a preset time-related coefficient. The weighted difference and weighted time offset of all communities and all times are accumulated, and the minimum value of the accumulated result is used as the optimization objective.

[0027]

[0028] in, Let represent a binary decision variable, when at time Through medical facilities To the community When allocating isolation resources, When at time Failed medical facilities To the community When allocating isolation resources, ; Indicates at time Medical facilities For the community The amount of isolation resources allocated; Indicates community At any moment Dynamic weighting coefficients; Indicates community At any moment The number of infected people; Indicates community At any moment The number of exposed individuals; Indicates the coefficient of time-related terms; Indicates community At any moment The time offset.

[0029] As a preferred embodiment of the primary health resource allocation method for epidemic prevention and control described in this invention, the community-side preference function includes: [the function is missing here, likely referring to a specific parameter or parameter]. The preference for each medical facility is numerically represented to obtain the corresponding community-side preference value.

[0030] The community obtained from the SEIR dynamic prediction model at time [time] Time remaining until the predicted peak of the epidemic, and the community's status at any given moment. The ratio of the sum of symptomatic infections and exposed individuals to the maximum service capacity of medical facilities, along with the community medical resource index, are used to construct a community-side preference function with linear weighting, thus obtaining the community's preference at time [time value missing]. For each medical facility, a community-side preference value is assigned, and the community's location at time [time value] is determined based on the community-side preference value. Prioritization of preferences for various medical facilities.

[0031] As a preferred embodiment of the primary health resource allocation method for epidemic prevention and control described in this invention, the medical facility preference function includes: [the function is missing here, likely related to medical facilities]. At any moment The degree of preference in each community is numerically represented to obtain the corresponding medical-side preference value.

[0032] Based on the epidemic evolution indicators of each community at each time point obtained from the SEIR dynamic prediction model and the isolation resource allocation scheme determined by the dynamic spatiotemporal resource matching objective function, an epidemic prevention and control benefit index and a comprehensive cost index are constructed. Based on the ratio of the epidemic prevention and control benefit index to the comprehensive cost index, and introducing a trust enhancement factor and a trust enhancement weight coefficient, a medical facility preference function is constructed to obtain the medical facility... At any moment For the community The preference value, and determine the medical facility based on the medical facility-side preference value. At any moment Prioritization of preferences for each community.

[0033] As a preferred embodiment of the primary health resource allocation method for epidemic prevention and control described in this invention, the final matching result includes calculating the matching relationship between communities and medical facilities at each time point using a stable matching algorithm, with community preference order and medical facility preference priority order as inputs.

[0034] Under the constraints of the matching relationship, the number of isolation resources allocated to each community at each time point, as determined by the dynamic spatiotemporal resource matching objective function, is allocated to the matched medical facilities to obtain the final matching result between the community and the medical facilities at each time point and the corresponding number of isolation resources.

[0035] Secondly, the present invention provides a primary health resource allocation system for epidemic prevention and control, including an epidemic evolution prediction module: dividing the prevention and control area into communities, introducing population state variables and medical resource indices in each community, constructing an SEIR dynamic prediction model and performing numerical solutions within a preset prediction period to obtain the epidemic evolution indicators of each community.

[0036] Dynamic resource matching module: Based on the epidemic evolution indicators of each community and the available capacity of each medical facility, a dynamic spatiotemporal resource matching objective function is established to solve the resource allocation relationship between communities and medical facilities at each time point, and to obtain the initial health resource allocation scheme between communities and medical facilities at each time point.

[0037] Bilateral preference construction module: Based on the epidemic evolution indicators, medical resource index and initial health resource allocation scheme of each community, construct community-side preference function and medical facility-side preference function to quantify the matching priority of communities and medical facilities at each time point.

[0038] Stable matching and scheduling module: Taking the community-side preference function and the medical facility-side preference function as input, it executes a stable matching algorithm, adjusts the initial health resource allocation scheme under the constraints of the matching relationship, and generates the final matching result between the community and the medical facility.

[0039] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the primary health resource allocation method for epidemic prevention and control as described in the first aspect of the present invention.

[0040] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the primary health resource allocation method for epidemic prevention and control as described in the first aspect of the present invention.

[0041] The beneficial effects of this invention are as follows: By constructing a distributed, space- and resource-coupled SEIR dynamic prediction model, it simultaneously introduces population state variables and a medical resource index composed of beds, medical personnel, and epidemic prevention materials at the community scale. This allows the evolution of the epidemic and the carrying capacity of primary healthcare resources to be modeled in a unified model. Compared to the improved SEIR model that only predicts the epidemic trend, this model adds a characterization of the relationship between epidemic development and resource pressure. Based on multi-community epidemic evolution indicators and the available capacity of various medical facilities, a dynamic spatiotemporal resource matching objective function oriented towards the three-dimensional structure of community, medical facilities, and time is established. This function provides a unified quantitative evaluation and solution for isolation resource allocation schemes at different times and in different communities, directly providing an executable solution. The resource allocation results transform primary healthcare resource allocation from an experience-based, static approach to a model-driven, dynamic decision-making process. By constructing community-side preference functions and medical facility-side preference functions, factors such as cross-community transport distance and existing load levels are incorporated into the matching process. This satisfies isolation and treatment needs while constraining the load distribution among different medical facilities, reducing overcrowding in single facilities, and ensuring residents' access to medical care. By integrating case data, population flow data, and medical resource data in the cloud, a visualized decision support interface is created, facilitating integration with existing regional epidemic prevention and control or health information platforms. This reduces manual statistics and multiple rounds of communication, improving the automation and response efficiency of primary healthcare resource allocation. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 is a flowchart of the primary health resource allocation method for epidemic prevention and control.

[0044] Figure 2 is a system logic diagram of the primary health resource allocation method for epidemic prevention and control.

[0045] Figure 3 is a schematic diagram of the three-layer collaborative architecture of edge, regional cloud and central cloud for primary health resource allocation methods for epidemic prevention and control. Detailed Implementation

[0046] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0047] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0049] Example 1, referring to Figures 1-3, is an embodiment of the present invention. This embodiment provides a method for primary health resource allocation for epidemic prevention and control, including the following steps:

[0050] S1: Divide the prevention and control area into communities, introduce population state variables and medical resource index into each community, construct a SEIR dynamic prediction model, and perform numerical solution within the preset prediction time to obtain the epidemic evolution index of each community.

[0051] The SEIR dynamic prediction model includes dividing the prevention and control area into M grassroots communities, and in the... Define population state variables in each community.

[0052]

[0053] in, Indicates community At any moment Total population; Indicates community At any moment The number of susceptible individuals; Indicates community At any moment The number of exposed individuals; Indicates community At any moment The number of symptomatic infections; Indicates community At any moment The number of recoveries.

[0054] For the first Each community at any time The standardized availability rates of hospital beds, medical staff, and epidemic prevention materials were collected, and a medical resource index was constructed according to preset weighting coefficients.

[0055]

[0056] in, Indicates community At any moment The medical resource index; Indicates community At any moment Bed availability rate; Indicates community At any moment The attendance rate of medical staff; Indicates community At any moment The availability rate of epidemic prevention materials; This indicates the preset bed weighting coefficient; This indicates the preset weighting coefficient for medical staff; This indicates the preset material weighting coefficient.

[0057] Communities are determined based on medical resource index. At any moment The transmission rate of symptomatic infected individuals, the transmission rate of exposed individuals, and the recovery rate.

[0058] The symptomatic infection rate is introduced into the community at any given time, based on the baseline infection rate. The medical resource index and the preset resource sensitivity coefficient are determined according to a linear relationship.

[0059]

[0060] in, Indicates community At any moment The transmission rate of symptomatic infected individuals; Indicates the baseline infection rate; Indicates the resource sensitivity coefficient; This indicates the medical resource index.

[0061] The infection rate of exposed individuals is based on the baseline infection rate of exposed individuals, and incorporates community data at any given time. The detection coverage is determined as a linear function.

[0062]

[0063] in, Indicates community At any moment The infection rate among those exposed; This represents the baseline infection rate among exposed individuals; Indicates community At any moment The detection coverage rate has a value range of [0,1].

[0064] The recovery rate is based on the baseline recovery rate, according to the community at any given time. The power function relationship of the medical resource index is determined.

[0065]

[0066] in, Indicates the recovery rate; Indicates the baseline recovery rate; Indicates the first Each community at any time Medical resource index Resource factors obtained by exponentiation. .

[0067] The SEIR dynamic prediction model incorporates the impact of population movement between different communities; based on population movement data between communities, it determines the time frame at which... From the community Inflow into the community The intensity of population movement, and based on the time of each community The ratio of infected individuals to the total population is used to weight and sum the population mobility intensity to obtain the community... At any moment External infection pressure.

[0068]

[0069] in, Indicates community At any moment External infection pressure; Indicates the total number of communities within the area; Indicates the index of the source community, indicating from the first... The flow of the first community to the first One community; Indicates at time From the community Inflow into the community The intensity of population movement; Indicates community At any moment The number of infected people; Indicates community At any moment The total population.

[0070] The SEIR dynamic prediction model includes, based on the traditional SEIR model, incorporating the community at time [time]. External infection pressure With the number of susceptible individuals The product of these terms is introduced as an additional infection term into the susceptible population evolution equation and the exposed population evolution equation; this additional infection term reflects the impact of externally input infections on the community. The impact of the number of exposed individuals.

[0071] Based on the symptomatic infection rate, exposed person infection rate, recovery rate, and additional infection item, a set of SEIR transmission dynamics equations is established for each community to characterize the relationship between the number of various population groups and time. Within a preset prediction period, the SEIR transmission dynamics equations for each community are numerically solved to obtain the number of susceptible persons, exposed persons, symptomatic infected persons, and recovered persons in each community at each prediction time. Based on the change in the number of symptomatic infected persons in each community within the prediction period, the predicted peak time of the epidemic in each community and the number of symptomatic infected persons at the peak are determined as indicators of the epidemic evolution in each community.

[0072] The predicted peak time of the epidemic:

[0073]

[0074] in, This indicates the predicted peak time of the epidemic in community i; Indicates that all conditions are met. At what point in time did the number of infected people The point in time when the maximum value is reached.

[0075] The SEIR propagation dynamics equations:

[0076]

[0077] in, Indicates community At any moment The number of susceptible individuals; Indicates community The transmission rate of symptomatic infected individuals at time t; Indicates community At any moment The number of symptomatic infections; Indicates community At any moment Total population; Indicates community At any moment The infection rate among those exposed; Indicates community At any moment The number of exposed individuals; Community At any moment External infection pressure; At any moment From the community Inflow into the community The population flow intensity is generated by integrating traffic and communication data from the cloud platform.

[0078]

[0079] in, Indicates community At any moment The number of exposed individuals; Indicates community At any moment The number of susceptible individuals; Indicates community At any moment The transmission rate of symptomatic infected individuals; Indicates community At any moment The number of symptomatic infections; Indicates community At any moment Total population; Indicates community At any moment The infection rate among those exposed; Community At any moment External infection pressure; Community At any moment External infection pressure; Indicates isolation efficiency; This represents the testing coverage rate, ranging from 0 to 1, and is reported by primary healthcare institutions.

[0080]

[0081] in, Indicates community At any moment The number of symptomatic infections; This represents the rate at which exposed individuals become infected, and its reciprocal is the average incubation period; it is set as a constant based on pathogen characteristics and is updated globally through the cloud platform. Indicates community At any moment The number of exposed individuals; Indicates the recovery rate; Indicates the medical resource conversion rate; Indicates community At any moment The medical resource index.

[0082]

[0083] Among them, it represents the community At any moment The number of recovered patients; Indicates the recovery rate; Indicates community At any moment The number of symptomatic infections; Indicates isolation efficiency; This represents the testing coverage rate, ranging from 0 to 1, and is reported by primary healthcare institutions. Indicates community At any moment The number of exposed individuals; Indicates community At any moment The medical resource index.

[0084] S2: Based on the epidemic evolution indicators of each community and the available capacity of each medical facility, a dynamic spatiotemporal resource matching objective function is established to solve the resource allocation relationship between the community and the medical facility at each time point, and to obtain the initial health resource allocation scheme between the community and the medical facility at each time point.

[0085] Figure 2 illustrates a three-tiered collaborative work architecture and resource flow in the context of epidemic prevention and control, where communities report epidemic information, and the cloud platform coordinates PHC institutions at all levels to allocate primary health resources such as manpower, medicines, beds, and vaccines to each community according to a matching algorithm.

[0086] The objective function for dynamic spatiotemporal resource matching includes, under the constraint of medical facility capacity, a unified quantitative evaluation of the allocation schemes for isolation resources in different communities and at different times, to determine the optimal scheme for each medical facility to allocate isolation resources to each community at each time.

[0087] The sum of the number of infected individuals and exposed individuals in the same community at the same time is subtracted from the number of isolation resources in the community, and the difference is weighted by a dynamic weighting coefficient. The time offset is determined based on the time difference between the predicted peak time of the epidemic and the time of resource allocation, and is weighted by a preset time-related coefficient. The weighted difference and weighted time offset of all communities and all times are accumulated, and the minimum value of the accumulated result is used as the optimization objective.

[0088]

[0089] in, Let represent a binary decision variable, when at time Through medical facilities To the community When allocating isolation resources, When at time Failed medical facilities To the community When allocating isolation resources, ; Indicates at time Medical facilities For the community The amount of isolation resources allocated; Indicates community At any moment Dynamic weighting coefficients; Indicates community At any moment The number of infected people; Indicates community At any moment The number of exposed individuals; Indicates the coefficient of time-related terms; Indicates community At any moment The time offset.

[0090] The time mentioned Medical facilities For the community Number of allocated isolation resources satisfy:

[0091]

[0092] in, Indicates at time Medical facilities For the community The amount of isolation resources allocated; Indication facilities Real-time available capacity.

[0093] The dynamic weight index includes those based on community. Medical resource index A non-uniform weighted function is constructed by combining the urban and rural indicator functions.

[0094]

[0095] in, Indicates community At any moment Dynamic weighting coefficients; This represents the amplification factor for urban-rural disparities. ; Indicates community At any moment The medical resource index; Indicates community Urban and rural indicator functions; when community When it is a rural community, ;when community When it is an urban community, .

[0096] The time offset includes a time offset constructed based on the time difference between the predicted peak time of the epidemic and the time of resource scheduling, which is used to measure the degree of advance in resource allocation.

[0097]

[0098] in, Indicates community At any moment Time offset; Indicates community The predicted peak time of the epidemic; Indicates the current time of resource scheduling; This indicates the preset lead time.

[0099] S3: Based on the epidemic evolution indicators, medical resource index and initial health resource allocation scheme of each community, construct community-side preference function and medical facility-side preference function to quantify the preference order between communities and medical facilities at each time point.

[0100] The community-side preference function includes the community preference at time [time]. The preference for each medical facility is numerically represented to obtain the corresponding community-side preference value.

[0101] The community obtained from the SEIR dynamic prediction model at time [time] Time remaining until the predicted peak of the epidemic, and the community's status at any given moment. The ratio of the sum of symptomatic infections and exposed individuals to the maximum service capacity of medical facilities, along with the community medical resource index, are used to construct a community-side preference function with linear weighting, thus obtaining the community's preference at time [time value missing]. For each medical facility, a community-side preference value is assigned, and the community's location at time [time value] is determined based on the community-side preference value. Prioritization of preferences for various medical facilities.

[0102]

[0103] in, Indicates community At any moment For medical facilities Preference values; This represents the time gap weighting coefficient, when hour, Automatically increased to 0.8; Indicates community At any moment Time remaining until the predicted peak of the epidemic; Indicates community At any moment The number of symptomatic infections; Indicates community At any moment The number of exposed individuals; Indicates medical facilities Maximum service capacity; The weighting coefficient representing compensation for resource scarcity; Indicates community The medical resource index.

[0104] The medical facility preference function includes preferences for medical facilities. At any moment The degree of preference in each community is numerically represented to obtain the corresponding medical-side preference value.

[0105] Based on the epidemic evolution indicators of each community at each time point obtained from the SEIR dynamic prediction model and the isolation resource allocation scheme determined by the dynamic spatiotemporal resource matching objective function, an epidemic prevention and control benefit index and a comprehensive cost index are constructed. Based on the ratio of the epidemic prevention and control benefit index to the comprehensive cost index, and introducing a trust enhancement factor and a trust enhancement weight coefficient, a medical facility preference function is constructed to obtain the medical facility... At any moment For the community The preference value, and determine the medical facility based on the medical facility-side preference value. At any moment Prioritization of preferences for each community.

[0106]

[0107] in, Indicates that medical facility j is at time For the community Preference values; Indicates medical facilities At any moment To the community Indicators of the effectiveness of providing services in epidemic prevention and control; Indicates medical facilities At any moment For the community The overall cost indicators required to provide the service; This represents the trust enhancement weighting coefficient; This indicates a trust-enhancing factor.

[0108] The aforementioned indicators of effectiveness in epidemic prevention and control:

[0109]

[0110] Indicates medical facilities At any moment To the community Indicators of the effectiveness of providing services in epidemic prevention and control; Indicates community At any moment The number of symptomatic infections; Indicates community At any moment The number of exposed individuals; This indicates the expected rate of epidemic containment.

[0111]

[0112] in, Indicates the expected rate of epidemic containment; Indicates the baseline recovery rate; Indicates the first Each community at any time Medical resource index Resource factors obtained by exponentiation. .

[0113] The comprehensive cost index:

[0114]

[0115] in, Indicates medical facilities At any moment For the community The overall cost indicators required to provide the service; Indicates at time Medical facilities For the community The amount of isolation resources allocated; Indication facilities Real-time available capacity; Indicates community The historical contract fulfillment rate is recorded by the cloud platform; Indicates community With medical facilities The cost of distance between them; This represents the trust penalty weighting coefficient.

[0116] The trust enhancement factor :

[0117]

[0118] in, Indicates a trust-enhancing factor; Indicates community The historical performance rate is recorded by the cloud platform.

[0119] S4: Using the community-side preference function and the medical facility-side preference function as inputs, execute the stable matching algorithm to adjust the initial health resource allocation scheme under the constraints of the matching relationship, and generate the final matching result between the community and the medical facility.

[0120] The final matching result includes calculating the matching relationship between the community and the medical facility at each time point using a stable matching algorithm, with the community-side preference order and the medical facility-side preference priority order as inputs.

[0121] Under the constraints of the matching relationship, the number of isolation resources allocated to each community at each time point, as determined by the dynamic spatiotemporal resource matching objective function, is allocated to the matched medical facilities to obtain the final matching result between the community and the medical facilities at each time point and the corresponding number of isolation resources.

[0122] As shown in Figure 3, the stable matching algorithm adopts a distributed edge and cloud collaborative structure, which collaboratively completes the matching calculation between edge nodes and regional cloud nodes.

[0123] Locally at edge nodes such as community health service center servers, the community is calculated based on the community-side preference function. The preference values ​​for each medical facility were sorted to obtain a sequence. Calculate medical facilities based on medical facility preference function The preference values ​​of each community were sorted to obtain a sequence. The preference ranking results are encrypted and uploaded to the regional cloud node, without uploading the original case data and resource data. Within a given time window, the regional cloud node ranks the data according to the remaining time of the predicted epidemic peak. The communities will be handled sequentially from smallest to largest. When the urgency level is below the preset threshold, the community will be directly assigned to [the relevant authority / organization]. Highest priority medical facilities and reserved resources The remaining communities follow Submit a matching request to the target medical facility, within the real-time available capacity. Under constraints, regional cloud nodes are based on Select communities with higher priority to retain matching relationships, and continue to drive subsequent rounds of requests for communities that have not been accepted, until there are no more unmatched communities or the time window ends.

[0124] Based on the matching results, the regional cloud node triggers a resource occupancy confirmation process, recording the resource quantity through an encrypted commitment token. In accordance with the confirmation deadline, the fulfillment rate indicator is updated based on the relationship between the actual usage in the community and the promised usage. The updated fulfillment rate is then written into the medical facility preference function for matching calculations at subsequent time points. The final resource allocation result is obtained. The data is written back to the SEIR dynamic prediction model to adjust the recovery rate and the infection rate, forming a closed-loop linkage between resource allocation and epidemic spread prediction.

[0125] This embodiment also provides a primary health resource allocation system for epidemic prevention and control, including: an epidemic evolution prediction module: dividing the prevention and control area into communities, introducing population state variables and medical resource indices in each community, constructing an SEIR dynamic prediction model, and performing numerical solutions within a preset prediction period to obtain the epidemic evolution indicators of each community.

[0126] Dynamic resource matching module: Based on the epidemic evolution indicators of each community and the available capacity of each medical facility, a dynamic spatiotemporal resource matching objective function is established to solve the resource allocation relationship between communities and medical facilities at each time point, and to obtain the initial health resource allocation scheme between communities and medical facilities at each time point.

[0127] Bilateral preference construction module: Based on the epidemic evolution indicators, medical resource index and initial health resource allocation scheme of each community, construct community-side preference function and medical facility-side preference function to quantify the matching priority of communities and medical facilities at each time point.

[0128] Stable matching and scheduling module: Taking the community-side preference function and the medical facility-side preference function as input, it executes a stable matching algorithm, adjusts the initial health resource allocation scheme under the constraints of the matching relationship, and generates the final matching result between the community and the medical facility.

[0129] This embodiment also provides a computer device applicable to the primary health resource allocation method for epidemic prevention and control, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the primary health resource allocation method for epidemic prevention and control as proposed in the above embodiment.

[0130] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0131] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for allocating primary health resources for epidemic prevention and control as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0132] Example 2 is an embodiment of the present invention, which provides a method for primary health resource allocation for epidemic prevention and control. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment.

[0133] Singapore, Ireland, Shenzhen, and Yangzhou were selected as research sites. COVID-19 statistics and primary healthcare resource data were collected from these regions to set parameters for the SEIR model and matching algorithm. Machine learning methods were used to fit and validate the parameters, ensuring the model accurately reflects the actual situation.

[0134] Table 1 shows a comparison of SEIR parameters and primary health resource allocation indicators for typical cities. The first column of the table contains the model indicator symbols, the second column contains the meaning of each indicator in the SEIR model, and the remaining columns correspond to the actual values ​​for the four cities of Shenzhen, Singapore, Yangzhou, and Ireland, respectively, reflecting the differences in population size, epidemic status, and primary health resource allocation among different cities.

[0135] The data in the table reveals two key differences: firstly, there are significant variations in population size and disease transmission intensity among different cities; secondly, the density of primary healthcare institutions and per capita treatment capacity are also markedly uneven. This table, by comprehensively presenting epidemic transmission parameters and healthcare resource allocation levels within the same framework, exposes the structural differences in epidemic transmission characteristics and the carrying capacity of primary healthcare resources among different cities.

[0136] Table 1: Comparison of SEIR parameters and primary health resource allocation indicators in typical cities

[0137]

[0138]

[0139] As shown in Table 2, the comprehensive index of community epidemic prevention and control is decomposed into four element layers: population mobility intensity, population susceptibility intensity, government awareness of prevention and control, and disease control resource capacity. Each element layer is quantified through statistically available indicators. This table, through the structured design of the element and indicator layers, clarifies the main influencing factors and their quantitative dimensions that constitute the comprehensive index of community epidemic prevention and control.

[0140] Table 2: Composition of Element and Indicator Layers of the Comprehensive Index for Community Epidemic Prevention and Control

[0141]

[0142] In summary, this invention achieves the following: First, it constructs a distributed spatial and resource-coupled SEIR dynamic prediction model, introducing parameters such as medical resource index, testing coverage, and population flow at the community level to predict the spatiotemporal evolution trend of the epidemic in each community. Second, it generates community-side preference functions and medical facility-side preference functions through a dynamic spatiotemporal resource matching objective function based on the remaining time of the epidemic peak, resource occupancy ratio, and trust factor. Third, it obtains a three-dimensional isolation resource allocation scheme of community, medical facility, and time under the constraints of medical facility capacity and community isolation demand through a stable matching algorithm that coordinates edge nodes and regional cloud nodes. Finally, it dynamically corrects resource occupancy performance and urban-rural differences by combining a trust enhancement mechanism and urban-rural compensation items. The final allocation results are written back to the SEIR dynamic prediction model to dynamically adjust parameters such as infection rate and recovery rate, forming a closed-loop linkage between epidemic transmission prediction and medical resource scheduling, thus realizing distributed, refined allocation and dynamic response of primary health resources.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for allocating primary healthcare resources for epidemic prevention and control, characterized in that: The process includes: dividing the prevention and control area into communities; introducing population state variables and medical resource indices into each community; constructing a SEIR dynamic prediction model; and performing numerical solutions within a preset prediction period to obtain the epidemic evolution indicators for each community. Based on the epidemic evolution indicators of each community and the available capacity of each medical facility, a dynamic spatiotemporal resource matching objective function is established to solve the resource allocation relationship between communities and medical facilities at each time point, obtaining the initial health resource allocation scheme between communities and medical facilities at each time point. Based on the epidemic evolution indicators, medical resource indices, and initial health resource allocation schemes for each community, a community-side preference function and a medical facility-side preference function are constructed to quantify the preference order between communities and medical facilities at each time point. Using the community-side preference function and the medical facility-side preference function as inputs, a stable matching algorithm is executed to adjust the initial health resource allocation scheme under the constraints of the matching relationship, generating the final matching result between communities and medical facilities.

2. The method for allocating primary health resources for epidemic prevention and control as described in claim 1, characterized in that: The SEIR dynamic prediction model includes dividing the prevention and control area into M grassroots communities, and in the... Define population state variables in each community; in, Indicates community At any moment Total population; Indicates community At any moment The number of susceptible individuals; Indicates community At any moment The number of exposed individuals; Indicates community At any moment The number of symptomatic infections; Indicates community At any moment The number of recovered patients; for the first Each community at any time The standardized availability rates of hospital beds, medical staff, and epidemic prevention materials were collected, and a medical resource index was constructed according to preset weighting coefficients. in, Indicates community At any moment The medical resource index; Indicates community At any moment Bed availability rate; Indicates community At any moment The attendance rate of medical staff; Indicates community At any moment The availability rate of epidemic prevention materials; This indicates the preset bed weighting coefficient; This indicates the preset weighting coefficient for medical staff; This indicates the preset weighting coefficient for supplies; the community is determined based on the medical resource index. At any moment The symptomatic infection rate, the exposed infection rate, and the recovery rate; the symptomatic infection rate is based on the baseline infection rate, incorporating community-based data at any given time. The medical resource index and the preset resource sensitivity coefficient are determined according to a linear relationship; the exposed person infection rate is based on the exposed person baseline infection rate, incorporating the community's response at any given time. The detection coverage is determined as a linear function; the recovery rate is based on the baseline recovery rate, adjusted according to the community's performance at any given time. The power function relationship of the medical resource index is determined; the impact of population flow between different communities is introduced into the SEIR dynamic prediction model; based on the population flow data between communities, the time interval is determined. From the community Inflow into the community The intensity of population movement, and based on the time of each community The ratio of infected individuals to the total population is used to weight and sum the population mobility intensity to obtain the community... At any moment External infection pressure; in, Indicates community At any moment External infection pressure; Indicates the total number of communities within the area; Indicates the index of the source community, indicating from the first... The flow of the first community to the first One community; Indicates at time From the community Inflow into the community The intensity of population movement; Indicates community At any moment The number of infected people; Indicates community At any moment The total population.

3. The method for allocating primary health resources for epidemic prevention and control as described in claim 2, characterized in that: The SEIR dynamic prediction model includes, based on the traditional SEIR model, incorporating the community at time [time]. External infection pressure With the number of susceptible individuals The product of these terms is introduced as an additional infection term into the susceptible population evolution equation and the exposed population evolution equation; this additional infection term reflects the impact of externally input infections on the community. The impact of the number of exposed individuals; based on the symptomatic infection rate, exposed individual infection rate, recovery rate, and additional infection item, a set of SEIR transmission dynamics equations is established for each community to characterize the relationship between the number of various population groups and time; within a preset prediction period, the SEIR transmission dynamics equations for each community are numerically solved to obtain the number of susceptible individuals, exposed individuals, symptomatic infected individuals, and recovered individuals in each community at each prediction time; and based on the change in the number of symptomatic infected individuals in each community within the prediction period, the predicted peak time of the epidemic in each community and the number of symptomatic infected individuals at the peak are determined as indicators of the epidemic evolution in each community.

4. The method for allocating primary health resources for epidemic prevention and control as described in claim 3, characterized in that: The dynamic spatiotemporal resource matching objective function includes: under the constraint of medical facility capacity, uniformly and quantitatively evaluating the isolation resource allocation schemes for different communities and at different times, determining the optimal scheme for each medical facility to allocate isolation resources to each community at each time; summing the number of infected persons and exposed persons in the same community at the same time, subtracting the number of isolation resources in the community, and weighting the difference according to the dynamic weight coefficient; The time offset is determined based on the time difference between the predicted peak time of the epidemic and the time of resource allocation, and then weighted according to the preset time-related coefficients. The weighted differences and weighted time offsets of all communities and all times are accumulated, and the minimum value of the accumulated result is used as the optimization objective. in, Let represent a binary decision variable, when at time Through medical facilities To the community When allocating isolation resources, When at time Failed medical facilities To the community When allocating isolation resources, ； Indicates at time Medical facilities For the community The amount of isolation resources allocated; Indicates community At any moment Dynamic weighting coefficients; Indicates community At any moment The number of infected people; Indicates community At any moment The number of exposed individuals; Indicates the coefficient of time-related terms; Indicates community At any moment The time offset.

5. The method for allocating primary health resources for epidemic prevention and control as described in claim 4, characterized in that: The community-side preference function includes the community preference at time [time]. The preference for each medical facility is numerically represented to obtain the corresponding community-side preference value; the community preference value at time t is obtained from the SEIR dynamic prediction model. Time remaining until the predicted peak of the epidemic, and the community's status at any given moment. The ratio of the sum of symptomatic infections and exposed individuals to the maximum service capacity of medical facilities, along with the community medical resource index, are used to construct a community-side preference function with linear weighting, thus obtaining the community's preference at time [time value missing]. For each medical facility, a community-side preference value is assigned, and the community's location at time [time value] is determined based on the community-side preference value. Prioritization of preferences for various medical facilities.

6. The method for allocating primary health resources for epidemic prevention and control as described in claim 5, characterized in that: The medical facility preference function includes preferences for medical facilities. At any moment The preference levels of each community are numerically represented to obtain the corresponding medical-side preference values. Based on the epidemic evolution indicators of each community at each time point obtained from the SEIR dynamic prediction model and the isolation resource allocation scheme determined by the dynamic spatiotemporal resource matching objective function, epidemic prevention and control benefit indicators and comprehensive cost indicators are constructed. Based on the ratio of epidemic prevention and control benefit indicators to comprehensive cost indicators, and by introducing trust enhancement factors and trust enhancement weight coefficients, a medical facility-side preference function is constructed to obtain the preference value of medical facility j at time point j. For the community The preference value is determined, and the medical facility j at time is determined based on the medical facility-side preference value. Prioritization of preferences for each community.

7. The method for allocating primary health resources for epidemic prevention and control as described in claim 6, characterized in that: The final matching result includes: taking the community-side preference order and the medical facility-side preference priority order as input, using a stable matching algorithm to calculate the matching relationship between the community and the medical facility at each time; under the constraint of the matching relationship, the number of isolation resources allocated to each community at each time determined by the dynamic spatiotemporal resource matching objective function is allocated to the matched medical facility to obtain the final matching result between the community and the medical facility at each time and the corresponding number of isolation resources.

8. A primary health resource allocation system for epidemic prevention and control, based on the primary health resource allocation method for epidemic prevention and control as described in any one of claims 1 to 7, characterized in that: The system includes: an epidemic evolution prediction module, which divides the prevention and control area into communities, introduces population state variables and medical resource indices into each community, constructs a SEIR dynamic prediction model, and performs numerical solutions within a preset prediction period to obtain the epidemic evolution indicators for each community; a dynamic resource matching module, which establishes a dynamic spatiotemporal resource matching objective function based on the epidemic evolution indicators of each community and the available capacity of each medical facility, solves the resource allocation relationship between communities and medical facilities at each time point, and obtains the initial health resource allocation scheme between communities and medical facilities at each time point; a two-sided preference construction module, which constructs a community-side preference function and a medical facility-side preference function based on the epidemic evolution indicators, medical resource indices, and initial health resource allocation schemes of each community, and quantifies the matching priority between communities and medical facilities at each time point; and a stable matching scheduling module, which takes the community-side preference function and the medical facility-side preference function as inputs, executes a stable matching algorithm, adjusts the initial health resource allocation scheme under the constraints of the matching relationship, and generates the final matching result between communities and medical facilities.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the primary health resource allocation method for epidemic prevention and control as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the primary health resource allocation method for epidemic prevention and control as described in any one of claims 1 to 7.

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