Intelligent allocation method, device and equipment for medical resources of respiratory infectious diseases and medium

Abstract

The invention discloses an intelligent medical resource allocation method, device and equipment for respiratory infectious diseases and a medium, and relates to the technical field of medical resource allocation, and the method comprises the steps: constructing a respiratory infectious disease model of a target respiratory infectious disease; the respiratory infectious disease model is a respiratory infectious disease model based on vaccine immune failure; based on the respiratory infectious disease model, calculating the number of infected people of the newly added target respiratory infectious diseases in unit time; emergency medical resources to be allocated are determined according to the number of newly-added target respiratory infectious disease infected people in unit time; and with the goal of minimizing the basic regeneration number of the infected cases in the unit time, a distribution strategy for distributing the emergency medical resources to non-hospitalized infected persons is determined, and the non-hospitalized infected persons comprise non-vaccinated infected persons and vaccine immunization failed infected persons. According to the invention, medical resource allocation of respiratory infectious diseases can be optimized.

Description

Technical Field

[0001] The present application relates to the technical field of medical resource allocation, and in particular to a method, device, equipment and medium for intelligent allocation of medical resources for respiratory infectious diseases. Background Art

[0002] Respiratory infectious diseases (RIDs) are one of the infectious diseases with the highest morbidity and case fatality rate (CFR). Since the 21st century, with the frequent occurrence of cross-species transmission of animal-borne pathogens and the increasing risk of transnational import and export of respiratory infectious diseases, humans have entered a high incidence stage of respiratory infectious diseases.

[0003] In the early stages of a respiratory infectious disease outbreak, the infectious disease spread rapidly due to the limited emergency medical resources such as hospital beds, ICU beds, ventilators, doctors or drugs. Therefore, how to optimize the allocation of emergency medical resources is one of the key factors in controlling the spread of the virus. Summary of the invention

[0004] The purpose of this application is to provide a method, device, equipment and medium for intelligent allocation of medical resources for respiratory infectious diseases, which can optimize the allocation of medical resources for respiratory infectious diseases.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for intelligent allocation of medical resources for respiratory infectious diseases, comprising:

[0007] Constructing a respiratory infectious disease model of a target respiratory infectious disease; the respiratory infectious disease model is a respiratory infectious disease model based on vaccine immunization failure;

[0008] Calculate the number of new infections of the target respiratory infectious disease per unit time based on the respiratory infectious disease model;

[0009] Determine the emergency medical resources to be deployed based on the number of new infections of the target respiratory infectious disease within a unit time;

[0010] With the goal of minimizing the basic reproduction number of infected cases per unit time, a strategy for allocating the emergency medical resources to infected persons who are not hospitalized and isolated is determined. The infected persons who are not hospitalized and isolated include unvaccinated infected persons and infected persons whose vaccination fails.

[0011] In a second aspect, the present application provides a medical resource intelligent allocation device for respiratory infectious diseases, the medical resource intelligent allocation device for respiratory infectious diseases comprising:

[0012] A respiratory infectious disease model building module, used to build a respiratory infectious disease model of a target respiratory infectious disease; the respiratory infectious disease model is a respiratory infectious disease model based on vaccine immunization failure;

[0013] An infection number determination module, used to calculate the number of new infections of the target respiratory infectious disease per unit time based on the respiratory infectious disease model;

[0014] A module for determining emergency medical resources to be deployed, used to determine the emergency medical resources to be deployed according to the number of new infections of the target respiratory infectious disease within a unit time;

[0015] An intelligent allocation strategy determination module is used to determine an intelligent allocation strategy for allocating the emergency medical resources to non-hospitalized infected persons with the goal of minimizing the basic reproduction number of infected cases within the unit time. The non-hospitalized infected persons include unvaccinated infected persons and infected persons whose vaccination has failed.

[0016] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for intelligent allocation of medical resources for respiratory infectious diseases described in any one of the above-mentioned methods.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for intelligent allocation of medical resources for respiratory infectious diseases.

[0018] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0019] The present application provides a method, device, equipment and medium for intelligent allocation of medical resources for respiratory infectious diseases. A respiratory infectious disease model is constructed based on vaccine immunization failure, and the impact of non-vaccination and vaccine immunization failure on the spread of respiratory infectious diseases is considered. With the goal of minimizing the basic reproduction number of infected cases per unit time, an allocation strategy for allocating emergency medical resources to infected persons who are not hospitalized and isolated is determined, thereby realizing the quantitative allocation of medical resources and optimizing the allocation of medical resources for respiratory infectious diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A flowchart of a method for intelligently allocating medical resources for respiratory infectious diseases provided in one embodiment of the present application;

[0022] Figure 2 A schematic diagram of the respiratory infectious disease model provided in one embodiment of the present application;

[0023] Figure 3 A schematic diagram of allocation rules for emergency medical resources provided in an embodiment of the present application;

[0024] Figure 4 An embodiment of the present application provides and Schematic diagram of numerical simulation;

[0025] Figure 5 An embodiment of the present application provides and Schematic diagram of the first numerical simulation;

[0026] Figure 6 An embodiment of the present application provides and Schematic diagram of the second numerical simulation;

[0027] Figure 7 An embodiment of the present application provides and Schematic diagram of the third numerical simulation;

[0028] Figure 8 An embodiment of the present application provides and Schematic diagram of the fourth numerical simulation;

[0029] Fig. 9 A first numerical simulation diagram of the scale of respiratory infectious diseases and resource allocation ratio provided in an embodiment of the present application;

[0030] Fig.10 A second numerical simulation diagram of the scale of respiratory infectious diseases and resource allocation ratio provided in an embodiment of the present application;

[0031] Fig.11 A third numerical simulation diagram of the scale of respiratory infectious diseases and resource allocation ratio provided in an embodiment of the present application;

[0032] Fig.12 A fourth numerical simulation diagram of the scale of respiratory infectious diseases and resource allocation ratio provided in an embodiment of the present application;

[0033] Fig.13A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0035] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0036] Inactivated vaccines and live attenuated vaccines are key public health tools to combat respiratory infectious diseases. Most individuals will stimulate their immune system to respond after vaccination. However, some individuals fail to produce a corresponding immune response or the immune response is significantly weakened due to variations in vaccine immunogenicity or individual reactivity. This phenomenon is called immune failure. Immune failure is divided into primary vaccine failure (PVF) and secondary vaccine failure (SVF). PVF means that an individual does not have an immune response after vaccination and cannot obtain specific antibodies, and may have severe clinical signs similar to those of unvaccinated patients; SVF means that an individual has an immune response after vaccination and produces specific antibodies, but the antibody level is lower than the protection threshold or the vaccine effectiveness (VE) decreases with the increase of vaccination time.

[0037] Existing studies are mainly based on the assumption that the infected are homogeneous mixtures. In fact, the infected may be either immune failure or unvaccinated. Therefore, the present invention constructs a respiratory infectious disease model considering immune failure to describe the evolution differences of different types of infected people in respiratory infectious disease epidemics and their impact on epidemic outcomes.

[0038] This application provides a method for intelligent allocation of medical resources for respiratory infectious diseases, such as Figure 1 As shown, the intelligent allocation method of medical resources for respiratory infectious diseases includes:

[0039] Step 101: construct a respiratory infectious disease model of a target respiratory infectious disease; the respiratory infectious disease model is a respiratory infectious disease model based on vaccine immunization failure.

[0040] Step 102: Calculate the number of new infections of the target respiratory infectious disease per unit time based on the respiratory infectious disease model.

[0041] Step 103: Determine the emergency medical resources to be deployed according to the number of new infections of the target respiratory infectious disease within a unit time.

[0042] Step 104: With the goal of minimizing the basic reproduction number of infected cases within the unit time, determine an allocation strategy for allocating the emergency medical resources to infected persons who are not hospitalized and isolated, wherein the infected persons who are not hospitalized and isolated include unvaccinated infected persons and infected persons whose vaccination fails.

[0043] This application constructs a respiratory infectious disease model based on vaccine immunity failure, taking into account the impact of non-vaccination and vaccine immunity failure on the spread of respiratory infectious diseases; with the goal of minimizing the basic reproduction number of infected cases per unit time, an intelligent allocation strategy for allocating emergency medical resources to infected persons who are not hospitalized and isolated is determined, thereby realizing the quantitative allocation of medical resources and optimizing the allocation of medical resources for respiratory infectious diseases.

[0044] Based on the modeling of respiratory infectious diseases, this application proposes a dynamic resource allocation strategy for different types of infected people under the constraints of emergency medical resources. First, according to the characteristics of the infection degree of respiratory infectious diseases, the endowment of emergency medical resources and the public immunization status, a respiratory infectious disease model based on immune failure is constructed. The respiratory infectious disease model emphasizes the role played by moderately infected and severely infected people who are not vaccinated and have immune failure, hospitalized and isolated, and not hospitalized in isolation in the epidemic process of infectious diseases and their impact on the results of the epidemic. Second, based on the transmission characteristics of respiratory infectious diseases, a numerical simulation of resource allocation under the constraints of scarce medical resources is carried out, and a more refined and real-time dynamic allocation strategy for emergency medical resources is provided according to the epidemic situation of infectious diseases in different regions.

[0045] This application constructs a respiratory infectious disease model based on the Susceptible-Infected-Recovered (SIR) model that takes into account immune failure and multiple infection stages, referred to as SIR. V I n R model.

[0046] S V I n The construction process of the R model includes expanding the population into 13 categories, divided according to the following:

[0047] (1) Differences in immune response

[0048] According to the differences in immune response, this application denotes unvaccinated susceptible individuals as S(t), and immune failures of PVF and SVF as SV (t), the number of vaccine recipients who are immune within the set period is denoted as V(t).

[0049] (2) Differences in clinical signs

[0050] According to the development stage of respiratory infectious diseases, the infected person I(t) generally has mild symptoms in the early stage of infection, and most of I(t) will develop moderate symptoms after a few days, and then recover or recover spontaneously after a few days; some I(t) may further develop into severe infection due to the presence of chronic underlying diseases, or being elderly people over 65 years old, unvaccinated people, immune failure, late pregnancy or perinatal women, etc. Therefore, the present invention divides I(t) into moderately infected people I(t) and severely infected people I(t). M (t) and severely infected persons I S (t). The present invention assumes that S (t) can be transformed into I through good treatment or autoimmunity M (t), and then gradually recover and become a recovered person R(t) with a certain immunity.

[0051] (3) Hospitalization and isolation status

[0052] Hypothesis I M (t) and I S (t) In the initial period, none of them were hospitalized and isolated, but then according to I M (t) and I S (t) can be converted to hospitalization and isolation status according to the clinical signs and local medical resources, until the disease is cured and converted to recovered person R(t) or death due to illness and converted to death Therefore, this application will M (t) and I S (t) Further divided into: moderately infected people who are not hospitalized and isolated Severely infected people who are not hospitalized or isolated Moderately infected patients in hospital isolation Severely infected patients in hospital isolation Moderately infected people who have failed to be vaccinated and are not hospitalized for isolation Severely infected people who have failed to be vaccinated and are not hospitalized for isolation Moderately ill hospitalized patients with immune failure and severely ill hospitalized patients with immune failure

[0053] The unvaccinated infected persons include unvaccinated moderately infected persons and unvaccinated severely infected persons, and the infected persons who have failed vaccine immunity include moderately infected persons who have failed vaccine immunity and severely infected persons who have failed vaccine immunity; the moderately infected persons are those who meet the first preset conditions, and the severely infected persons are those who meet the second preset conditions.

[0054] In order to reasonably simplify the respiratory infectious disease model and facilitate subsequent experimental quantitative research on the dynamics of respiratory infectious diseases, this application provides the following premise assumptions based on the development stage of the respiratory infectious disease course:

[0055] Assumption 1.I M (t) and I S The transformation path of (t) is bidirectional and reversible, but I S (t) Only restore to I first M (t), only then can we recover.

[0056] Hypothesis 2. The transformation path from non-hospitalized patients to hospitalized patients is one-way and irreversible. Once an infected person is hospitalized, he or she cannot return to the non-hospitalized state unless he or she recovers or dies.

[0057] Assumption 3. Hospitalized infected persons and Not contagious.

[0058] Assumption 4. All I(t) have a probability of natural death, but only I S (t) will die from illness.

[0059] Assumption 5. During hospitalization, they can receive effective medical treatment and services. and The mortality rate is lower than and and The cure rate is higher than and

[0060] Since the newly infected person is S(t) or S V (t) is effectively contacted with I(t). According to hypothesis 3, the formula for the number of new infections of the target respiratory infectious disease per unit time is:

[0061]

[0062] Among them, S n is the number of new infections of the target respiratory infectious disease per unit time, e is the vaccination rate, τ is the effectiveness of the vaccine, β M , β S , β VM , β VS They are effective contact rate.

[0063] Assume T(t) is the total population of the homogeneous space at time t, Λ is the population input rate per unit time, w, m, c, q, They are the disease deterioration rate, improvement rate, cure rate, hospitalization isolation rate and mortality rate per unit time. V I n The basic framework of the R epidemic model is as follows Figure 2 shown.

[0064] According to the assumptions, the dynamic change process of S(t) after being infected is: S(t) has an effective contact rate of Under the influence of Possible deterioration rate The impact of the disease has turned into severe infection or in hospitalization rate Under the influence of It may also be in the cure rate Under the influence of Deteriorated to back, Possible improvement rate The impact of It may also be in the hospitalization rate Under the influence of There may also be a mortality rate Similarly, and You can also pass and Mutual conversion, or moderate hospitalization The cure rate Under the influence of In the case fatality rate died under the influence of.

[0065] The rate of change of the recovered patient R(t) with respect to time t It is expressed as:

[0066] Among them, R(t) is the number of recovered patients at time t.

[0067] S(t) and S V The evolution path of (t) is shown in Table 1.

[0068] Table 1 S(t) and S V The evolution path of (t)

[0069]

[0070]

[0071] The respiratory infectious disease model is described by a nonlinear dynamic system:

[0072]

[0073] in, are moderately infected people who are not hospitalized or isolated and have not been vaccinated at time t. for The rate of change, are severely infected people who are not hospitalized or isolated and have not been vaccinated at time t. for The rate of change, is a moderately infected person who is hospitalized and isolated at time t, for The rate of change, For severely infected people who are hospitalized and isolated at time t, for The rate of change, are moderately infected people who are not hospitalized or isolated at time t and whose vaccination fails. for The rate of change, For severely infected people who are not hospitalized and isolated at time t and whose vaccination fails, for The rate of change, For those with moderate infection who have failed vaccination and are hospitalized in isolation, for The rate of change, For severely infected people who have failed to be vaccinated and are hospitalized in isolation, for The rate of change of M is S(t) per unit time and The effective contact rate, β S for S(t) and Effective contact rate, β VM S is the unit time V (t) and The effective contact rate, β VS S is the unit time V and Effective contact rate, m N for The improvement rate of the condition per unit time, m Q for The improvement rate of the condition per unit time, m NV for The improvement rate of the condition per unit time, m QV for The rate of improvement per unit time, q M for The hospitalization rate per unit time, q S for The hospitalization rate per unit time, q VM for The hospitalization rate per unit time, q VS for The hospitalization rate per unit time, w N for The rate of disease progression per unit time, w Q for The rate of disease progression per unit time, w NV for The rate of disease progression per unit time, w QV for The rate of disease progression per unit time, c N for The cure rate per unit time, c Q for The cure rate per unit time, c NV for The cure rate per unit time, c QV for The cure rate per unit time, for The mortality rate per unit time due to disease is for The mortality rate per unit time due to disease is for The mortality rate per unit time due to disease is for The unit time mortality rate due to disease, S(t) is the susceptible population that has not been vaccinated, S V (t) Those who have failed to be vaccinated, For S V (t), V(t) is the vaccine recipient with immunity within the set period, R(t) is the recovered person at time t, is the rate of change of R(t), Λ 1 is the population input rate per unit time of S(t), Λ 2 For S V (t) is the population input rate per unit time, Λ 3 is the population input rate per unit time of V(t), e is the vaccination per unit time of S(t), d is the natural death rate per unit time, and η is the conversion of V into S per unit time. V (t) is the conversion rate, τ is S V The points on the letter expression represent the rate of change.

[0074] The mathematical properties of the respiratory infectious disease model are analyzed below.

[0075] First, analyze the existence and uniqueness of disease-free equilibrium points.

[0076] Known S V I n The solution of the R model is a 12-dimensional vector function. For the initial vector, it is assumed that all components are non-negative. When the initial vector is in the set Ω, if the solution vector at any time t>0 also belongs to the set Ω, then the set is called is an invariant set, and the above specific conclusions are given by the following theorem.

[0077] Theorem 1: When the foreign population input rate is Λ 1 , Λ 2 , Λ 3 When the sum of is greater than the natural death rate, S V I n The solution vector of the R model is non-negative and the closed set is an invariant set.

[0078]

[0079] In this formula, S = S(t), S V =S V (t), and so on, R=R(t).

[0080] Theorem 2: For any given initial value in Ω, when t ≥ 0, S V I n The R model has a unique solution, and the solution of the model is in the closed set Ω. V I n The R model always has a disease-free equilibrium point E 0 .

[0081]

[0082] In this formula, S = S(t), S V =S V (t), and so on, R=R(t).

[0083] Second, the basic reproduction number and system stability analysis are performed, and S is calculated based on the Next Generation Matrix (NGM) method. V I n The basic reproduction number R of the R model 0 , the basic reproduction number of infected cases per unit time is expressed as:

[0084]

[0085] in, represents the basic reproduction number of unvaccinated people, represents the basic reproduction number of immune failure, T 1represents the total population of unvaccinated people in the homogeneous space, T 2 It represents the total population of people who have failed to be vaccinated in a homogeneous space. The basic reproduction number includes the basic reproduction number of unvaccinated people and the basic reproduction number of people who have failed to be vaccinated.

[0086] If R 0 <1, the disease will disappear on its own; if R 0 >1 and no relevant intervention is carried out, the respiratory infectious disease will break out and become an epidemic.

[0087] Equilibrium point stability analysis: Theorem 3 When R 0 When <1, S V I n The disease-free equilibrium point E of the R model 0 is locally asymptotically stable; when R 0 >1 is established, E 0 Become unstable.

[0088] Theorem 4 When R 0 When <1, S V I n E of R model 0 is globally asymptotically stable in Ω. If R 0 >1, then E 0 Unstable in Ω, and infectious diseases persist.

[0089] Third, sensitivity analysis of the basic reproduction number: In order to further study the main influencing factors of the morbidity and mortality of respiratory infectious diseases, this application analyzes S V I n The sensitivity analysis of the parameters reflecting different propagation factors in the R model is given in Table 1. and Sensitivity index to all baseline parameter values.

[0090] Table 1 and Sensitivity index

[0091]

[0092]

[0093] Among them, ε, δ and φ are both intermediate parameters. δ=q M +w N +c N +d,φ=q VM +w NV +c NV +d.

[0094] From Table 1, we can see that and This is consistent with the actual physical meaning of each parameter and its impact on the transmission capacity of respiratory infectious diseases. With Λ 1 , β M , β S Proportional to w N , m N ,q M ,q S , c N ,d, Inversely proportional; similarly, With Λ 2 , β VM , β VS Proportional to w NV , m NV ,q VM ,q VS , c NV ,d, Inversely proportional.

[0095] The principle of deployment of emergency medical resources in this application is that, assuming that a respiratory infectious disease breaks out in a certain area at a certain time, the transmission mechanism of respiratory infectious diseases is S V I n The R model determines the dynamic allocation of emergency medical resources according to the number of new cases in the region every day. Let C(t) be all the emergency medical resources that can be allocated in the region at time t, π be the emergency medical resources allocated for each new case, and set C(t) to be linearly related to the number of new infection cases at time t, that is, the emergency medical resources to be allocated are expressed as:

[0096]

[0097] Among them, C(t) is the emergency medical resources to be deployed at time t, π is the emergency medical resources allocated for each newly infected person, and π is an emergency medical resource unit, such as masks, beds, and medical staff.

[0098] Since the basic reproduction number R 0 It plays a key role in the prevention and control of respiratory infectious diseases. Under the constraints of scarce emergency medical resources, we should consider maximizing the use of emergency medical resources and reducing R as much as possible. 0 For example, by increasing the hospitalization rate, infectious disease parameters can be changed through real-time allocation of medical resources. The deterioration rate and improvement rate of non-hospitalized patients are mainly determined by the infectious disease itself, and the vaccination rate e and immunization failure rate η of the input population per unit time cannot be controlled. Therefore, not all infectious disease parameters can be improved through the optimal allocation of emergency medical resources.

[0099] because About q M and q S Decreasing, About q VM and q VS Decreasing, therefore increasing and The hospitalization rate can be effectively reduced and Since different types of infected people have different clinical signs, investing the same amount of emergency medical resources will have different effects on improving the efficiency of hospitalization. For SVF patients, their clinical symptoms are generally milder than those of PVF patients, and their demand for medical resources is relatively less. For moderately infected people, they do not need too much medical intervention. A small number of medical staff and necessary medicines can provide them with good hospitalization and treatment conditions. For severely infected people, due to their serious condition, they usually need special wards with special equipment and the care of multiple intensive care nurses. Therefore, in the face of many limitations on the number and type of emergency medical resources, it is necessary to adjust C(t) according to the actual situation. and Dynamically adjust between them to minimize the basic reproduction number.

[0100] Will and Let C(t) be assigned to and The ratio of 1 , b 2 , b 3 , b 4 for and The marginal hospitalization efficiency is the number of infected people that can be treated by a unit of emergency medical resources. The marginal hospitalization efficiency is determined by many factors, such as the number of medical staff and beds, the price of medical resources, etc. are the hospitalization rates after using emergency medical resources C(t). In addition, a large number of studies have shown that the outbreak of respiratory infectious diseases is extremely rapid and quickly causes a shortage of medical resources in various countries. Therefore, assuming that under the constraint of scarce emergency medical resources, no matter how C(t) is allocated, it is impossible to treat all and Right now α=α(t)=α 1 (t)+α 2 (t). Let g(t) be the efficiency of C(t) in treating infected people, which is called the utilization efficiency of C(t), that is, get:

[0101] Among them, g1 (t)∈(0,1-q M ],g 2 (t)∈(0,1-q S ],g 3 (t)∈(0,1-q VM ],g 4 (t)∈(0,1-q VS ], They represent the hospitalization rate after using emergency medical resources C(t). The allocation rules of emergency medical resources C(t) and the meaning of each parameter are as follows: Figure 3 shown.

[0102] Will Replace the original R 0 The hospitalization rate is about Replace q M , Replace q S , Replace q VM , Replace q VM , so that it becomes the basic reproduction number R at time t 0 (α(t)), that is

[0103]

[0104] Then, the optimal emergency medical resource allocation strategy is found at each time t: It represents the optimal allocation strategy of emergency medical resources at time t.

[0105] Due to the scarcity of emergency medical resources, the useful life of C(t) is short. As a dynamic coefficient, it is introduced into S V I n R for finding new models in R models 0 (t), R 0 (t) = R 0 (α(t)), but rather as an exogenous parameter that can change in the short term; secondly, due to the mutation of the virus and the complexity of the real environment, it is difficult to accurately model the long-term dynamic changes of some parameters in practice. Therefore, it is necessary to estimate the current infectious disease parameters and calculate and track the immediate R 0 (t) is a common practice adopted by various countries and regions during the epidemic.

[0106] This application will provide a dynamic deployment strategy for emergency medical resources. Analyze the expression.

[0107] Theorem 5: The allocation strategy is expressed as:

[0108] (1) When f(α 1 =0)≤0, When f(α 2 =0)≤0,

[0109] (2) When f(α 1 =1)≥0, When f(α 2 =1)≥0,

[0110]

[0111]

[0112] in, Indicates unique existence.

[0113] Proof: Order A and B are both intermediate parameters.

[0114] but

[0115] get:

[0116]

[0117] make but

[0118]

[0119] make therefore The size of f(α 1 ), and because f(α 1 ) is α 1 is a quadratic function of Axis of Symmetry

[0120]

[0121] It can be seen from this that f(α 1 ) is monotonically decreasing on [0, 1] and has at most one root.

[0122] When f(0)≤0, f(α 1 )≤0 on [0, 1], ≥ 0 on [0, 1], so It is monotonically increasing on [0, 1], at this time

[0123] When f(1)≥0, f(α1 ) ≥ 0 on [0,1], ≤ 0 on [0,1], so is monotonically decreasing on [0,1], then

[0124] When f(1)<0 and f(0)>0, therefore exist It is monotonically decreasing,

[0125]

[0126]

[0127] make but

[0128]

[0129]

[0130] Subtract, and there is at most one root.

[0131] When f(0)≤0, f(α 2 )≤0 on [0, 1], ≥ 0 on [0, 1], so It is monotonically increasing on [0, 1], at this time

[0132] When f(1)≥0, f(α 2 )≥0 on [0, 1], ≤ 0 on [0, 1], so is monotonically decreasing on [0, 1], then

[0133] When f(1)<0 and f(1)>0, therefore exist It is monotonically decreasing,

[0134] Among them, σ represents the set value, Strategies for allocating emergency medical resources to unvaccinated infected persons, Strategies for allocating emergency medical resources to infected people who have failed vaccination, Estimated allocation of emergency medical resources for unvaccinated infected persons, Estimation of emergency medical resource allocation for infected people who fail to be vaccinated, α 1 represents the weight of emergency medical resource allocation for unvaccinated infected persons, α2 represents the weight of emergency medical resource allocation for infected people who fail to be vaccinated, g 1 =g 1 (t) is the hospitalization efficiency of unvaccinated moderately infected persons who are not isolated, g 2 =g 2 (t) is the hospitalization efficiency of unvaccinated, severely unisolated infected persons, g 3 =g 3 (t) represents the hospitalization efficiency of moderately infected unisolated patients with immune failure, g 4 =g 4 (t) represents the hospitalization efficiency of severely infected people who have failed immunity but are not isolated, ε, δ and φ are both intermediate parameters.

[0135]

[0136] in, is the emergency medical resource allocation strategy for unvaccinated infected persons at time t, is the emergency medical resource allocation strategy for infected people whose vaccination fails at time t, α 1 (t) is the weight of emergency medical resource allocation for unvaccinated infected persons at time t, α 2 (t) is the weight of emergency medical resource allocation for infected people who fail to be vaccinated at time t, is the basic reproduction number of unvaccinated people at time t, is the basic regeneration number of those who fail to be immune at time t.

[0137] T 1 ,t 2 and T 3 They are S(t) and S in the homogeneous space at time t respectively. V (t) and the population of V(t).

[0138] The results show that:

[0139] (1) When (or ), C(t) should be fully invested (or )middle;

[0140] (2) When (or ), C(t) should be fully invested in (or )middle;

[0141] (3) When (or ), C(t) should be and (or and ) to dynamically allocate between them.

[0142] Next, this application explores the contact rate of moderately infected people β M , β VM and the deployment strategy of scarce emergency medical resources The monotonic relationship between the infection rate and the contact rate of severely infected people β S , β VS and The monotonic relationship between the hospitalization efficiency of moderately infected people g 1 , g 3 and The monotonic relationship between g and the hospitalization efficiency of severely infected patients 2 , g 4 and The monotonic relationship between With g 1 and g 2 The monotonic relationship between With g 3 and g 4 monotonic relationship.

[0143] Theorem 6:

[0144]

[0145]

[0146] N, P, N' and P' are all intermediate parameters. P=β M (δβ S +β M m N )+β M β S g 1 , P′=β VM (φβ VS +β VM m NV )+β VM β VS g 3 , For the corresponding The minimum R 0 ,Right now

[0147] Proof: From Theorem 5, we know is f(α 1 ) is the root to the left of the axis of symmetry, and when hour,

[0148] Let εg 1 +δg 2 +g 1 g 2 =G,2β S g 1 w N +Gβ M =H(β S ),

[0149] because

[0150] Therefore G 2 ≥4w N m N g 1 g 2 , K 2 (β S )≥4w N g 1 H(β S ),so So M(β S )≤0,

[0151]

[0152]

[0153]

[0154] because

[0155] So G′ 2 ≥4w NV m NV g 3 g 4 , K′2(β VS )≥4w NV g 3 H′(β VS ),so So M′(β VS )≤0,

[0156] Next, we prove that g 1 , g 2 and The monotonic relationship between 3 , g 4 and A monotonic relationship.

[0157]

[0158] therefore

[0159]

[0160]

[0161] make Therefore f(g 2 ) is g 2 The quadratic function, axis of symmetry f(g 2 ) has a positive root of

[0162]

[0163] Similarly,

[0164]

[0165] therefore

[0166]

[0167] make therefore

[0168]

[0169] make

[0170] It can be seen from this that f(g 4 ) is g 4 The quadratic function, axis of symmetry Δ>0. f(g 4 ) has a positive root of

[0171]

[0172] Next, we prove that g 1 , g 2 and The monotonic relationship between 3 , g 4 and A monotonic relationship.

[0173]

[0174] Also because

[0175] The results show that:

[0176] (1) When β M (or β VM ) remains unchanged, β S (or β VS ) means that the contact rate of severely infected people has increased. At this time, more emergency medical resources should be invested in the hospitalization of severely infected people. That is, emergency medical resources should give priority to those in the worst situation.

[0177] (2) When g 1 (or g 3 ) increases, the deployment strategy of emergency medical resources (or ) can be maximized when g 2 ∈(0,i)or(g 4 ∈(0, i′)), g 2 (or g 4 ) will lead to a decrease in the efficiency of emergency medical resource allocation. 2 ∈(i, 1-q S ) or (g 4 ∈(i′, 1-q VS )) When 2 (or g 4 ) will improve the deployment efficiency. This shows that more emergency medical resources should be invested in the hospitalization of moderately infected people at this time, that is, the investment of emergency medical resources should consider the maximization of utility.

[0178] (3) As the hospitalization efficiency of infected people g 1 , g 2 , g 3 , g 4 Continuously improve the basic reproduction number and will continue to decline, when and When it drops to less than 1, the infectious disease will disappear. Therefore, improving the hospitalization efficiency of emergency medical resources is the main factor in controlling infectious diseases.

[0179] The following is a numerical simulation and discussion of an intelligent allocation method of medical resources for respiratory infectious diseases in this application.

[0180] Due to S V I n The R model leads to and The sensitivity index is complex. In addition to the theoretical analysis of infectious disease dynamics, in order to intuitively obtain numerical results to guide the formulation of epidemic prevention policies, this application also uses the Partial Rank Correlation Coefficient (PRCC) method to further analyze and parameter sensitivity. and The PRCC values ​​and p-values ​​of the relevant estimated parameters are shown in Table 2.

[0181] Table 2 and PRCC values ​​and p-values ​​of relevant estimated parameters

[0182]

[0183] The p-value is used to measure the probability of the observed result or a more extreme result occurring when the null hypothesis (usually meaning "no effect" or "no difference") is true. The p-value helps determine whether the data is strong enough to reject the null hypothesis. A commonly used significance level is 0.05 (5%). If the p-value is less than 0.05, the result can be considered statistically significant, that is, there is enough evidence to reject the null hypothesis. On the contrary, if the p-value is greater than 0.05, the null hypothesis cannot be rejected.

[0184] From Table 2, we can see that 1 , β M , β S and Proportional, and Inversely proportional; Λ 2 , β VM , β VS and Proportional, w NV , m NV ,q VM ,q VS , c NV ,d, and It is inversely proportional, which is consistent with the analysis of Table 2, that is, the basic reproduction number increases with the increase of the extraterritorial population input rate or the effective contact rate, which means that the infectiousness and transmission efficiency of respiratory infectious diseases also increase accordingly. The inequality relationship with each parameter is as follows:

[0185]

[0186]

[0187] From the inequality, we can see that for In terms of hospitalization rate, M and q S Yes The parameter with the greatest impact is that when more moderate and severe infections are hospitalized for isolation and treatment, The value will decrease faster; In terms of hospitalization rate, VS and the disease progression rate w NV Yes The parameter with the greatest impact is that more severely infected people are hospitalized, while the rate of disease progression is effectively reduced. The value will decrease faster.

[0188] In order to further study the basic mechanism of respiratory infectious disease dynamics in multiple types of infected people, a numerical simulation model was used to explore the allocation strategy of scarce emergency medical resources. With β M , β S , g 1 and g 2 The relationship between With β VM , β VS , g 3 and g 4 The relationship between them. Use Python to program and generate various set data sets. This application sets the following initial conditions: T(0) = 200, V(0) = 200, S V (0)=500, The parameter values ​​of the model are shown in Table 3.

[0189] Table 3 S v I n R model parameter simulation values

[0190]

[0191] The following situations are mainly considered:

[0192] (1) When R 0 <1, from Theorem 3 and Theorem 4, we can know that S V I n The disease-free equilibrium point E of the R model 0 is globally asymptotically stable in Ω. Figure 2 This is verified in the simulation of (a) above. The number of infected individuals who have not been vaccinated or whose vaccination has failed approaches 0, at which point this round of respiratory infectious disease is about to end.

[0193] (2) When R 0 >1, from Theorem 3 and Theorem 4, we can know that S V I nThe R model has at least one endemic equilibrium point and is locally asymptotically stable. Figure 4 Part (a) is The numerical simulation of Figure 4 Part (b) is The numerical simulation of Figure 4 From the simulation in part (b), we can see that the number of infected individuals who are not vaccinated and whose immunity fails to be vaccinated gradually tends to a stable value after oscillation, and forms an endemic disease. Therefore, with the outbreak and development of respiratory infectious diseases, the number of unvaccinated infected individuals gradually exceeds the number of infected individuals with immune failure. This shows that infected individuals with immune failure are less contagious than unvaccinated individuals.

[0194] (3) From Theorem 5, we can see that when f(0)≤0, When f(1)≥0, according to Figure 3 The simulation, when (g 1 , g 2 ) or (g 3 , g 4 ) is very large or very small, or The value of is almost unaffected, which is consistent with the conclusion of Theorem 5, that is, when emergency medical resources are extremely effective or extremely ineffective in treating a certain type of infected people, then emergency medical resources should be used entirely or not at all for this type of infected people.

[0195] (4) According to (1) in Theorem 6, like but according to Figure 5-Figure 8 By comparing any (g 1 , g 2 ) or (g 3 , g 4 )Discover, Figure 6 In Basically greater than Figure 5 middle Figure 8 In Basically greater than Figure 7 of Therefore, when β M (or β VM ) does not change, (or )About β S (or β VS ) is monotonically non-increasing, which is consistent with the conclusion in (1) of Theorem 6. Figure 5 β M=0.01,β S = 0.05 and Numerical simulation of Figure 6 β M =0.01,β S = 0.01 and Numerical simulation of Figure 7 β VM =0.01,β VS = 0.05 and Numerical simulation of Figure 8 β VM =0.01, Figure 5 and Figure 7 It can be found that when g 2 (or g 4 ) remains unchanged, (or )With g 1 (or g 3 ) increases, at this time (or ) can be maximized; when g 1 (or g 3 ) remains unchanged, (or )With g 2 (or g 4 ) increases and decreases. Figure 6 In, when g 2 When unchanged, With the g 1 increases with the increase of g 1 ∈[0.521, 0685], if g 2 <0.401, then With the g 2 If g 2 >0.401, then With the g 2 The similar situation also occurs in g 1 ∈[0.218, 0.397], g 1 ∈[0.259, 0.286], g 1 ∈[0.196, 0.223]. Figure 8 In, when g 4 When unchanged, With the g 3 increases with the increase of g 3 ∈[0.384, 0.495], if g4 <0.390, then With the g 4 If g 4 >0.390, then With the g 4 The similar situation also occurs in g 3 ∈[0.242, 0.304] and g 3 ∈[0.188, 0.239], and various other situations were also tried, and the conclusions were consistent with the situation in (2) in Theorem 6.

[0196] Finally, to explore the impact of respiratory infectious diseases on and The impact of the distribution ratio, this example simulates the unvaccinated patient I N (t) greater or less than immune failure patients I NV (t) In the case of severe infection, immunocompromised patients Unvaccinated patients with severe infection Moderately infected immunocompromised patients and moderately infected unvaccinated patients Four situations, set and The value range of is [0,1000000], and the simulation results are as follows Figure 9-12 As shown, Fig. 9 For I NV (t)>I N (t), and Changes in population size The impact of Fig.10 For I N (t)>I NV (t), and Changes in population size The impact of Fig.11 For I NV (t)>I N (t), and Changes in population size The impact of Fig.12 For I N (t)>I NV (t), and Changes in population size impact.

[0197] Based on the same inventive concept, the embodiment of the present application also provides a medical resource intelligent allocation device for respiratory infectious diseases for implementing the medical resource intelligent allocation method for respiratory infectious diseases involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more medical resource intelligent allocation devices for respiratory infectious diseases provided below can refer to the limitations of the medical resource intelligent allocation method for respiratory infectious diseases above, and will not be repeated here.

[0198] In an exemplary embodiment, the present application provides a medical resource intelligent allocation device for respiratory infectious diseases, comprising:

[0199] The respiratory infectious disease model building module is used to build a respiratory infectious disease model of a target respiratory infectious disease; the respiratory infectious disease model is a respiratory infectious disease model based on vaccine immunization failure.

[0200] The infection number determination module is used to calculate the number of new infections of the target respiratory infectious disease per unit time based on the respiratory infectious disease model.

[0201] The module for determining the emergency medical resources to be deployed is used to determine the emergency medical resources to be deployed according to the number of new infections of the target respiratory infectious disease within a unit time.

[0202] An intelligent allocation strategy determination module is used to determine an intelligent allocation strategy for allocating the emergency medical resources to non-hospitalized infected persons with the goal of minimizing the basic reproduction number of infected cases within the unit time. The non-hospitalized infected persons include unvaccinated infected persons and infected persons whose vaccination has failed.

[0203] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.13As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store medical resource allocation data for respiratory infectious diseases. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for intelligent allocation of medical resources for respiratory infectious diseases is implemented.

[0204] Those skilled in the art will understand that Fig.13 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0205] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the steps in the above-mentioned embodiments of the method for intelligent allocation of medical resources for respiratory infectious diseases.

[0206] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0207] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0208] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a data processing logic of a programmable logic device, etc., but is not limited thereto.

[0209] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0210] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for intelligent allocation of medical resources for respiratory infectious diseases, characterized in that: The intelligent allocation method of medical resources for respiratory infectious diseases includes: Constructing a respiratory infectious disease model of a target respiratory infectious disease; the respiratory infectious disease model is a respiratory infectious disease model based on vaccine immunization failure; Calculate the number of new infections of the target respiratory infectious disease per unit time based on the respiratory infectious disease model; Determine the emergency medical resources to be deployed based on the number of new infections of the target respiratory infectious disease within a unit time; With the goal of minimizing the basic reproduction number of infected cases per unit time, a strategy for allocating the emergency medical resources to infected persons who are not hospitalized and isolated is determined. The infected persons who are not hospitalized and isolated include unvaccinated infected persons and infected persons whose vaccination fails.

2. The intelligent allocation method of medical resources for respiratory infectious diseases according to claim 1 is characterized in that: The unvaccinated infected persons include unvaccinated moderately infected persons and unvaccinated severely infected persons, and the infected persons who have failed vaccine immunity include moderately infected persons who have failed vaccine immunity and severely infected persons who have failed vaccine immunity; the moderately infected persons are those who meet the first preset conditions, and the severely infected persons are those who meet the second preset conditions.

3. The intelligent allocation method of medical resources for respiratory infectious diseases according to claim 2 is characterized in that: The respiratory infectious disease model is expressed as: in, are moderately infected people who are not hospitalized or isolated and have not been vaccinated at time t. for The rate of change, are severely infected people who are not hospitalized or isolated and have not been vaccinated at time t. for The rate of change, is a moderately infected person who is hospitalized and isolated at time t, for The rate of change, is a severely infected person who is hospitalized and isolated at time t. for The rate of change, are moderately infected people who are not hospitalized or isolated at time t and whose vaccination fails. for The rate of change, For severely infected people who are not hospitalized and isolated at time t and whose vaccination fails, for The rate of change, For those with moderate infection who have failed vaccination and are hospitalized in isolation, for The rate of change, For severely infected people who have failed to be vaccinated and are hospitalized in isolation, for The rate of change of M is S(t) per unit time and The effective contact rate, β S for S(t) and Effective contact rate, β VM S is the unit time V (t) and The effective contact rate, β VS S is the unit time V and Effective contact rate, m N for The improvement rate of the condition per unit time, m Q for The improvement rate of the condition per unit time, m NV for The improvement rate of the condition per unit time, m QV for The rate of improvement per unit time, q M for The hospitalization rate per unit time, q S for The hospitalization rate per unit time, q VM for The hospitalization rate per unit time, q VS for The hospitalization rate per unit time, w N for The rate of disease progression per unit time, w Q for The rate of disease progression per unit time, w NV for The rate of disease progression per unit time, w QV for The rate of disease progression per unit time, c N for The cure rate per unit time, c Q for The cure rate per unit time, c NV for The cure rate per unit time, c QV for The cure rate per unit time, for The mortality rate per unit time due to disease is for The mortality rate per unit time due to disease is for The mortality rate per unit time due to disease is for The unit time mortality rate due to disease, S(t) is the susceptible population that has not been vaccinated, is the rate of change of S(t), S V (t) Those who have failed to be vaccinated, For S V (t), V(t) is the vaccine recipient with immunity within the set period, R(t) is the recovered person at time t, is the rate of change of R(t), Λ1 is the population input rate per unit time of S(t), and Λ2 is the population input rate per unit time of S(t). V =(t) is the population input rate per unit time of V(t), Λ3 is the population input rate per unit time of V(t), e is the vaccination per unit time of S(t), d is the natural death rate per unit time, and η is the conversion of V(t) to S per unit time. V (t) is the conversion rate, τ is S V (t) is the vaccine effectiveness per unit time.

4. The intelligent allocation method of medical resources for respiratory infectious diseases according to claim 3 is characterized in that: The formula for calculating the number of new infections of the target respiratory infectious disease per unit time based on the respiratory infectious disease model is: Among them, S n It is the number of new infections of the target respiratory infectious disease per unit time.

5. The intelligent allocation method of medical resources for respiratory infectious diseases according to claim 3 is characterized in that: The emergency medical resources to be deployed are expressed as: Among them, C(t) is the emergency medical resources to be deployed at time t, and π is the emergency medical resources allocated to each newly infected person.

6. The intelligent allocation method of medical resources for respiratory infectious diseases according to claim 3 is characterized in that: The basic reproduction number of infected cases per unit time is expressed as: in, represents the basic reproduction number of unvaccinated people, represents the basic reproduction number of those who fail to be vaccinated, T1 represents the total population of unvaccinated people in the homogeneous space, and T2 represents the total population of those who fail to be vaccinated in the homogeneous space.

7. The intelligent allocation method of medical resources for respiratory infectious diseases according to claim 6, characterized in that: The allocation strategy is expressed as: (1) When f(α1=0)≤0, When f(α2=0)≤0, (2) When f(α1=1)≥0, When f(α2=1)≥0, (3) When f(α1=1)<0 and f(α1=0)>0, at this time When f(α2=1)<0 and f(α2=0)>0, at this time Among them, σ represents the set value, Strategies for allocating emergency medical resources to unvaccinated infected persons, Strategies for allocating emergency medical resources to infected people who have failed vaccination, Estimated allocation of emergency medical resources for unvaccinated infected persons, is the emergency medical resource allocation estimate for infected persons with vaccine immunization failure, α1 represents the emergency medical resource allocation weight for infected persons who have not been vaccinated, α2 represents the emergency medical resource allocation weight for infected persons with vaccine immunization failure, g1=g1(t) is the hospitalization efficiency of unvaccinated moderate non-isolated infected persons, g2=g2(t) is the hospitalization efficiency of unvaccinated severe non-isolated infected persons, g3=g3(t) represents the hospitalization efficiency of moderate non-isolated infected persons with immunization failure, g4=g4(t) represents the hospitalization efficiency of severe non-isolated infected persons with immunization failure, ε, δ and φ are both intermediate parameters. A=T1(β M ε+β S w N ), B = δε-m N w N , A, B, C, and D are all intermediate parameters; in, is the emergency medical resource allocation strategy for unvaccinated infected persons at time t, is the emergency medical resource allocation strategy for infected persons whose vaccination fails at time t, α1(t) is the emergency medical resource allocation weight for infected persons who are not vaccinated at time t, α2(t) is the emergency medical resource allocation weight for infected persons whose vaccination fails at time t, is the basic reproduction number of unvaccinated people at time t, is the basic regeneration number of those who fail to be immune at time t.

8. An intelligent allocation device for medical resources for respiratory infectious diseases, characterized in that: The medical resource intelligent allocation device for respiratory infectious diseases includes: A respiratory infectious disease model building module, used to build a respiratory infectious disease model of a target respiratory infectious disease; the respiratory infectious disease model is a respiratory infectious disease model based on vaccine immunization failure; An infection number determination module, used to calculate the number of new infections of the target respiratory infectious disease per unit time based on the respiratory infectious disease model; A module for determining emergency medical resources to be deployed, used to determine the emergency medical resources to be deployed according to the number of new infections of the target respiratory infectious disease within a unit time; An intelligent allocation strategy determination module is used to determine an allocation strategy for intelligently allocating the emergency medical resources to non-hospitalized isolated infected persons with the goal of minimizing the basic reproduction number of infected cases within the unit time. The non-hospitalized isolated infected persons include unvaccinated infected persons and infected persons whose vaccination has failed.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the intelligent allocation method for medical resources for respiratory infectious diseases described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for intelligent allocation of medical resources for respiratory infectious diseases described in any one of claims 1 to 7 is implemented.

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