A method and device for the isolation and control of infectious diseases.

Abstract

This invention provides a method and apparatus for the isolation and control of infectious diseases, comprising: constructing a physical contact network based on the contact behavior between individuals in the control area at the previous moment; estimating the infection probability of each individual in the control area at the current moment based on the physical contact network and the number of newly confirmed cases in the control area at the current moment; and determining the individuals in the control area that need to be isolated at the current moment based on the infection probability. This invention formulates a detailed isolation and control strategy down to the individual level based on the accurate estimation of the infection probability of each individual in the control area, thereby improving the effectiveness of isolation and control of infectious diseases.

Description

Technical Field

[0001] This invention relates to the field of infectious disease prevention and control technology, and in particular to a method and apparatus for the isolation and control of infectious diseases. Background Technology

[0002] Isolation is an important measure in the prevention and control of infectious diseases. It can effectively cut off the transmission routes of pathogens and curb the spread of infectious diseases.

[0003] Currently, existing isolation and control technologies can be divided into two categories: macro and micro. Macro-level isolation and control can only provide the isolation ratio for each region, not the specific isolation rate for individuals. This method is highly inaccurate and leads to a significant waste of public resources. Micro-level isolation and control models the contact relationships between individuals as a network, the disease transmission between individuals as a transmission problem on the network, and considers the degree of influence of an individual on the largest eigenvalue of the adjacency matrix as the individual's disease transmission capacity, thus determining the individuals requiring isolation. This method can only select individuals to be isolated based on the network structure and cannot dynamically determine the number of people to be isolated based on the disease transmission situation. Therefore, its isolation and control effectiveness is poor.

[0004] Therefore, there is an urgent need to develop a precise isolation and control strategy that is detailed down to the individual level and dynamically adjusted based on the spread of the disease. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a method and apparatus for the isolation and control of infectious diseases. Based on an accurate prediction of the infection probability of each individual in the control area, a detailed isolation and control strategy is formulated down to the individual level, thereby improving the effectiveness of the isolation and control of infectious diseases.

[0006] In a first aspect, the present invention provides a method for the isolation and control of infectious diseases, the method comprising:

[0007] Construct a physical contact network based on the contact behavior between individuals in the control area at the previous moment;

[0008] Based on the physical contact network and the current number of newly confirmed cases in the prevention and control area, the probability of infection for each individual in the prevention and control area at the current moment is estimated.

[0009] Based on the infection probability, determine the individuals in the control area who need to be quarantined at the current moment.

[0010] According to the method for isolation and control of infectious diseases provided by the present invention, the step of constructing a physical contact network based on the contact behavior between individuals in the control area at the previous moment includes:

[0011] Each individual in the control area is considered as a node, and the physical contact between two individuals in the control area at the previous moment is considered as an edge between the corresponding nodes of the two individuals, thus generating the physical contact network.

[0012] According to the method for isolation and control of infectious diseases provided by the present invention, when there are no newly confirmed cases in the control area at the current moment, the step of estimating the infection probability of each individual in the control area at the current moment based on the physical contact network and the current number of newly confirmed cases in the control area includes:

[0013] Using the infection probability of each individual at the previous moment and the adjacency matrix of the physical contact network, the probability that each individual was in a latent state at the previous moment, the probability that each individual was in an infected state at the previous moment and is also in an infected state at the current moment, and the probability that each individual was in a susceptible state at the previous moment and is in a latent state at the current moment are estimated.

[0014] The probability of each individual being in a latent state at the previous moment, the probability of each individual being in an infected state at the previous moment and also being in an infected state at the current moment, and the probability of each individual being in a susceptible state at the previous moment and being in a latent state at the current moment are summed as the infection probability of each individual at the current moment.

[0015] According to the method for isolation and control of infectious diseases provided by the present invention, the probability of infection for each individual at the current moment is calculated as follows:

[0016]

[0017] Where t is the current time, t-1 is the previous time, and γ i Let β be the recovery rate of the i-th individual in the control area. i Let i be the transmission rate of the i-th individual in the control area. Let be the infection probability of the i-th individual in the control area at time t. Let be the infection probability of the i-th individual in the control area at time t-1. Let be the probability of infection of the j-th individual in the control area at time t-1. V is the element in the i-th row and j-th column of the adjacency matrix of the physical contact network. t-1 Let be the set of all individuals in the control area at time t-1. The value represents whether the i-th individual in the control area before time t-1 has been diagnosed. If so, the value is 0; otherwise, the value is 1.

[0018] According to the method for isolation and control of infectious diseases provided by the present invention, when there are newly confirmed cases in the control area at the current moment, the step of estimating the infection probability of each individual in the control area at the current moment based on the physical contact network and the current number of newly confirmed cases in the control area includes:

[0019] Based on the probability distribution of the incubation period of infectious diseases, calculate the probability that each newly diagnosed patient was in the latent state at the previous moment and at every moment before that.

[0020] Based on the infection probability of each newly confirmed patient at each time before the previous time and the probability that each newly confirmed patient was in a latent state at each time before the previous time, the infection probability correction time relative to the control area is determined.

[0021] Based on the infection probability correction time, the infection probability of each individual in the prevention and control area at the previous time is corrected;

[0022] Using the infection probability of each individual in the control area at the previous time step and the adjacency matrix of the physical contact network, the infection probability of each individual in the control area at the current time step is estimated.

[0023] According to the method for isolation and control of infectious diseases provided by the present invention, determining the infection probability correction time relative to the control area based on the infection probability of each newly diagnosed patient at each time before the previous time and the probability that each newly diagnosed patient was in a latent state at each time before the previous time includes:

[0024] The probability of infection of each newly diagnosed patient at the previous time and at each time before that time is subtracted from the probability of each newly diagnosed patient being in the latent state at the previous time and at each time before that time, and the time when the difference is greater than 0 is taken as the correction time corresponding to each newly diagnosed patient.

[0025] The minimum time among all the corrected times corresponding to confirmed patients is taken as the infection probability corrected time.

[0026] According to the method for isolation and control of infectious diseases provided by the present invention, the step of correcting the infection probability of each individual in the control area at the previous time based on the infection probability correction time includes:

[0027] Based on the corrected value of the infection probability of each individual at the previous time step, the infection probability of each individual at the previous time step is re-estimated;

[0028] If each of the individuals is not a newly confirmed patient, then the infection probability of each individual at the previous time step is corrected to the maximum value of the infection probability of each individual at the previous time step and the re-estimated infection probability of each individual at the previous time step.

[0029] If each of the individuals is a newly confirmed patient, then the infection probability of each individual at the previous moment is corrected to the maximum value among the infection probability of each individual at the previous moment, the re-estimated infection probability of each individual at the previous moment, and the probability that each individual was in the latent state at the previous moment.

[0030] The correction value of the infection probability of each individual at the previous time step is obtained by correcting the infection probability of each individual step by step, starting from the infection probability correction time step.

[0031] According to the method for isolation and control of infectious diseases provided by the present invention, determining the individuals who need to be isolated in the control area at the current moment based on the infection probability includes:

[0032] Construct an economic network based on the current economic exchanges between individuals within the control area;

[0033] The weighted sum of the probabilities of all individuals in the control area going from healthy to infected at the current moment is defined as the disease transmission loss of the control area. The disease transmission loss is constructed based on the infection probability and the unassigned isolation vector.

[0034] The total number of economic network edge disruptions caused by isolation in the current moment in the control area is defined as the economic loss of the control area, and the economic loss is constructed based on the economic network and the isolation vector.

[0035] The total number of individuals currently isolated in the control area is defined as the isolation loss of the control area, and the isolation loss is constructed based on the isolation vector;

[0036] The isolation vector is solved with the objective of minimizing the weighted sum of the disease transmission loss, the economic loss, and the isolation loss.

[0037] Based on the solution results of the isolation vector, determine the individuals in the prevention and control area who need to be isolated at the current moment;

[0038] The isolation vector is used to characterize whether each individual in the control area should be isolated at the current moment.

[0039] According to the isolation and control method for infectious diseases provided by the present invention, the disease transmission loss The expression is:

[0040]

[0041]

[0042] The economic losses The expression is:

[0043]

[0044]

[0045] The isolation loss The expression is:

[0046]

[0047] Where t is the current time, t+1 is the next time, and δ i Let i be the weight of the i-th individual in the prevention and control area. V represents the probability that the i-th individual in the control area will change from healthy to infected at time t. t Let t be the set of all individuals in the control area at time t. and These are the i-th and j-th elements in the isolation vector, respectively, where a value of 1 indicates isolation and a value of 0 indicates no isolation; β i Let i be the transmission rate of the i-th individual in the control area. Let be the infection probability of the i-th individual in the control area at time t. Let be the probability of infection of the j-th individual in the control area at time t. Let be the element in the i-th row and j-th column of the adjacency matrix of the physical contact network at time t. The element in the i-th row and j-th column of the adjacency matrix of the economic network. The value represents whether the i-th individual in the control area before time t has been diagnosed. If so, the value is 0; otherwise, the value is 1.

[0048] Secondly, the present invention provides an isolation and control device for infectious diseases, the device comprising:

[0049] The module is used to construct a physical contact network based on the contact behavior between individuals in the control area at the previous moment;

[0050] The prediction module is used to predict the infection probability of each individual in the prevention and control area at the current moment, based on the physical contact network and the current number of newly confirmed cases in the prevention and control area.

[0051] The determination module is used to determine, based on the infection probability, the individuals in the prevention and control area who need to be isolated at the current moment.

[0052] This invention provides a method and apparatus for the isolation and control of infectious diseases, comprising: constructing a physical contact network based on the contact behavior between individuals in the control area at the previous moment; estimating the infection probability of each individual in the control area at the current moment based on the physical contact network and the number of newly confirmed cases in the control area at the current moment; and determining the individuals in the control area that need to be isolated at the current moment based on the infection probability. This invention formulates a detailed isolation and control strategy down to the individual level based on the accurate estimation of the infection probability of each individual in the control area, thereby improving the effectiveness of isolation and control of infectious diseases. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0054] Figure 1 This is one of the flowcharts of the method for isolation and control of infectious diseases provided by the present invention;

[0055] Figure 2 This is a schematic diagram of the physical contact network and economic network at the current moment provided by the present invention;

[0056] Figure 3 This is a diagram showing the disease transmission control effect of the present invention compared to other existing methods under the same average number of isolated individuals;

[0057] Figure 4 This is a schematic diagram illustrating the disease infection rate of the present invention and other existing methods;

[0058] Figure 5 This is a schematic diagram illustrating the economic losses of the present invention compared to other existing methods;

[0059] Figure 6 This is a schematic diagram of the structure of the isolation and control device for infectious diseases provided by the present invention;

[0060] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention;

[0061] Figure label:

[0062] 710: Processor; 720: Communication interface; 730: Memory; 740: Communication bus. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0064] Explanation of technical terms in this field: Transmission rate: The probability that a healthy individual will become infected when they come into contact with an infected person.

[0065] Recovery rate: The probability that a given infected person recovers and becomes a recovered person;

[0066] For infectious diseases with an incubation period, individuals in the environment may be in four states: susceptible (S), latent (E), infected (I), and recovered (R); among which:

[0067] Susceptible state S: Individuals in this state are healthy and not infectious, but may enter the latent state E at any time due to contact with an infected person.

[0068] Latent state E: Individuals in susceptible state S enter this state after infection. This state is asymptomatic and therefore difficult to distinguish, but it is infectious. Individuals in this state will enter infectious state I after a period of time.

[0069] Infectious state I: Entered after a period of time from latent state E. This state is symptomatic and contagious. After a period of time, it enters the recovery state R.

[0070] Recovery state R: Entered after a period of time from infected state I, this state will not be reinfected.

[0071] Infected individuals: Individuals in latent state E or infectious state I.

[0072] The following is combined Figures 1-7 This invention describes a method and apparatus for the isolation and control of infectious diseases.

[0073] Firstly, regarding infectious diseases with incubation periods, this invention provides a method for the isolation and control of infectious diseases, such as... Figure 1 As shown, the method includes:

[0074] S11. Construct a physical contact network based on the contact behavior between individuals in the control area at the previous moment;

[0075] S12. Based on the physical contact network and the current number of newly confirmed cases in the prevention and control area, estimate the infection probability of each individual in the prevention and control area at the current moment;

[0076] S13. Based on the infection probability, determine the individuals in the prevention and control area who need to be isolated at the current moment.

[0077] This invention provides a method for isolating and controlling infectious diseases, comprising: constructing a physical contact network based on the contact behavior between individuals in the control area at the previous moment; estimating the infection probability of each individual in the control area at the current moment based on the physical contact network and the number of newly confirmed cases in the control area at the current moment; and determining the individuals in the control area that need to be isolated at the current moment based on the infection probability. This invention formulates detailed isolation and control strategies down to the individual level based on the accurate estimation of the infection probability of each individual in the control area, thereby improving the effectiveness of isolating and controlling infectious diseases.

[0078] Specifically, S11 includes:

[0079] Each individual in the control area is considered as a node, and the physical contact between two individuals in the control area at the previous moment is considered as an edge between the corresponding nodes of the two individuals, thus generating the physical contact network.

[0080] The physical contact network This reflects the physical contact between individuals at the previous time step (t-1). That is, for any individual in the control area at the previous time step, a node on the physical contact network is used to represent them. If the two individuals corresponding to any two nodes on the physical contact network have physical contact at time t-1, then an edge is connected between these two nodes; otherwise, there is no edge connecting these two nodes. This allows us to construct the physical contact network for the previous time step.

[0081] It should be noted that physical contact here refers to being in the same enclosed space or in close proximity.

[0082] This invention uses a physical contact network to represent the contact relationships of individuals in a prevention and control area, laying the foundation for estimating the infection probability of individuals in the prevention and control area.

[0083] Specifically, since the latent state E is asymptomatic but infectious, it is crucial to accurately estimate whether an individual is in this state. Therefore, this invention provides an infection probability estimation algorithm that considers two scenarios: the first scenario is that there are no newly confirmed cases in the control area at the current time, and the second scenario is that there are newly confirmed cases in the control area at the current time; patients who show symptoms are considered as confirmed cases.

[0084] In the first case, S12 includes:

[0085] S12.1: Using the infection probability of each individual at the previous moment and the adjacency matrix of the physical contact network, estimate the probability that each individual was in a latent state at the previous moment, the probability that each individual was in an infected state at the previous moment and is also in an infected state at the current moment, and the probability that each individual was in a susceptible state at the previous moment and is in a latent state at the current moment.

[0086] S12.2: The sum of the probability that each individual was in a latent state at the previous moment, the probability that each individual was in an infected state at the previous moment and is also in an infected state at the current moment, and the probability that each individual was in a susceptible state at the previous moment and is in a latent state at the current moment is taken as the infection probability of each individual at the current moment.

[0087] Preferably, for the i-th individual in the physical contact network, the probability that it was in a latent state at the previous moment is denoted by P[Y]. i t-1 =E] represents the probability that the person was in an infected state at the previous moment and is also in an infected state at the current moment, denoted by P[Y]. i t =I|Y i t-1 =I]P[Y i t-1 =I] represents the probability that the person was in a susceptible state at the previous moment and is in a latent state at the current moment, denoted by P[Y]. i t =E|Y i t-1 =S]P[Y i t-1 =S] represents the probability of the i-th individual being infected at the current time (time t). The expression is:

[0088]

[0089] Simplify the above have:

[0090]

[0091] Given that the risk of infection for any healthy individual after close contact with a confirmed case is very small, therefore... Approximately

[0092] Ultimately, we can obtain:

[0093]

[0094] Where, γ iLet β be the recovery rate of the i-th individual in the control area. i Let i be the transmission rate of the i-th individual in the control area. Let be the infection probability of the i-th individual in the control area at time t. Let be the infection probability of the i-th individual in the control area at time t-1. Let be the probability of infection of the j-th individual in the control area at time t-1. V is the element in the i-th row and j-th column of the adjacency matrix of the physical contact network. t-1 Let be the set of all individuals in the control area at time t-1. This characterizes whether the i-th individual in the control area before time t-1 has been diagnosed; a value of 0 indicates t-1, and a value of 1 indicates t-1. i and β i It was issued by an infectious disease research institution in response to the transmission characteristics of infectious diseases.

[0095] For ease of calculation, the above formula can also be rewritten in vector and matrix form: p t =F(p) t-1 )

[0096] =(1-γ+γ⊙s t-1 )⊙p t-1 +[1-p t-1 ]⊙β⊙s t-1 ⊙(A t-1 ·p t-1 )

[0097] Here, p t p t-1 , γ, s t-1 The i-th element in β and β are respectively γ i , and β i A t-1 Let be the adjacency matrix of the physical contact network.

[0098] In the second scenario, when a new confirmed case appears at time t, it signifies the discovery of new information, allowing for the correction of the previously estimated individual infection probability and the estimation of the current individual infection probability. Therefore, in this case, S12 includes:

[0099] S12-A: Based on the probability distribution of the incubation period of infectious diseases, calculate the probability that each newly diagnosed patient was in the latent state at the previous time and at every time before that time.

[0100] S12-B: Based on the infection probability of each newly confirmed patient at each time before the previous time and the probability that each newly confirmed patient was in a latent state at each time before the previous time, determine the infection probability correction time relative to the control area.

[0101] S12-C: Based on the infection probability correction time, correct the infection probability of each individual in the prevention and control area at the previous time.

[0102] S12-D: Using the infection probability of each individual in the control area at the previous time step and the adjacency matrix of the physical contact network, the infection probability of each individual in the control area at the current time step is estimated.

[0103] Preferably, S12-A includes:

[0104] For a newly diagnosed patient k at time t, the probability that he / she is in a latent state at time t-τ is:

[0105]

[0106] Where g(t) is the probability distribution of the incubation period of an infectious disease, provided by an infectious disease research institution; k∈Φ t Φ t Let be the set of newly diagnosed patients at time t, where 0 ≤ τ ≤ t-1.

[0107] The S12-B includes:

[0108] S12-B-1: Subtract the infection probability of each newly diagnosed patient at the previous time and at each time before from the probability of each newly diagnosed patient being in the latent state at the previous time and at each time before, and take the time when the difference is greater than 0 as the correction time corresponding to each newly diagnosed patient.

[0109] S12-B-2: The minimum time among all the corrected times corresponding to confirmed patients is taken as the infection probability corrected time.

[0110] The formula for S12-B-1 is expressed as follows:

[0111]

[0112] t k =(t-1)-t 0,k

[0113] Among them, t k For the corrected time point corresponding to newly confirmed patient k, t 0,k The time frame for the newly confirmed patient k was adjusted and shifted forward. The probability of infection of newly confirmed patient k at time t-1-τ.

[0114] The formula for S12-B-2 is expressed as follows:

[0115] Among them, t w This is the timeframe for correcting the infection probability.

[0116] The S12-C includes:

[0117] S12-C-1: Based on the correction value of the infection probability of each individual at the previous time step, re-estimate the infection probability of each individual at the previous time step; wherein, the correction value of the infection probability of each individual at the previous time step is obtained by correcting the infection probability of each individual step by step, starting from the infection probability correction time step;

[0118] S12-C-2: If each of the individuals is not a newly confirmed patient, then the infection probability of each individual at the previous time step is corrected to the maximum value of the infection probability of each individual at the previous time step and the re-estimated infection probability of each individual at the previous time step.

[0119] If each of the individuals is a newly confirmed patient, then the infection probability of each individual at the previous moment is revised to the maximum value among the infection probability of each individual at the previous moment, the re-estimated infection probability of each individual at the previous moment, and the probability that each individual was in the latent state at the previous moment.

[0120] That is, time t is adjusted according to the infection probability. w The values ​​of each individual in the control area are corrected sequentially according to the time sequence in [t]. w The probability of infection at each time step in [t-1], assuming f is [t w If at any time in [t-1], then the probability of infection for the i-th individual in the control area at time f is... The correction can be:

[0121] If i∈Φ t hour,

[0122] like hour,

[0123] in,

[0124]

[0125] The S12-D includes:

[0126] Similar to the first case, the infection probability of the i-th individual in the corrected control area at time t-1 is calculated. Substitute into the formula Estimate the probability of infection of the i-th individual in the control area at time t.

[0127] This invention proposes a method for real-time estimation and updating of the probability of infection of all individuals in a control area (i.e., the probability of being in the latent state E or the infectious state I). This method estimates the probability of infection of individuals at time t+1 based on the individual infection probability at time t, which can help formulate the optimal isolation strategy.

[0128] Specifically, S13 includes:

[0129] S13.1: Construct an economic network based on the current economic exchanges between individuals within the control area;

[0130] S13.2: Define the weighted sum of the probabilities of all individuals in the control area going from healthy to infected at the current moment as the disease transmission loss of the control area, and construct the disease transmission loss based on the infection probability and the unassigned isolation vector;

[0131] S13.3: Define the total number of economic network edge destructions caused by isolation in the current moment in the prevention and control area as the economic loss of the prevention and control area, and construct the economic loss based on the economic network and the isolation vector;

[0132] S13.4: Define the total number of individuals currently isolated in the control area as the isolation loss of the control area, and construct the isolation loss based on the isolation vector;

[0133] S13.5: Solve for the isolation vector with the objective of minimizing the weighted sum of the disease transmission loss, the economic loss, and the isolation loss;

[0134] S13.6: Based on the solution result of the isolation vector, determine the individuals in the prevention and control area who need to be isolated at the current moment;

[0135] The isolation vector is used to characterize whether each individual in the control area should be isolated at the current moment.

[0136] Preferably, S13.1 includes: treating each individual in the prevention and control area as a node, and treating the economic exchange between two individuals in the prevention and control area at the current moment as an edge between the corresponding nodes of the two individuals, thereby generating the economic network.

[0137] The economic network This reflects the economic exchange between individuals at any given moment, that is, the use of economic networks for any individual within the current control area. It is represented by a node on the economic network. If any two individuals corresponding to any two nodes have had an economic interaction at the current moment (for example, if individual A eats at a breakfast shop owned by individual B, then A and B have an economic interaction), then an edge is drawn between these two nodes, and the edge weight is assigned according to the magnitude of the economic interaction between the individuals. Otherwise, there is no edge connecting these two nodes. In this way, the economic network can be established.

[0138] For all individuals within the control area, they are simultaneously within a physical contact network. and economic network middle, Figure 2 This is a schematic diagram of the physical contact network and economic network at the current moment. Points with the same number in the diagram represent the same individual.

[0139] Preferably, the disease transmission loss described in S13.2 The expression is:

[0140]

[0141]

[0142] Where, δ i Let i be the weight of the i-th individual in the prevention and control area. Let be the probability that the i-th individual in the control area changes from healthy to infected at time t. and These are the i-th and j-th elements in the isolation vector, respectively. A value of 1 indicates isolation, and a value of 0 indicates no isolation.

[0143] For ease of calculation, the above formula can also be rewritten as:

[0144]

[0145] Here, δ is a weight vector representing the importance of different individuals. For example, older people in the environment can be given higher weights because they are at higher risk of infection. The i-th element in δ is δ_i. i ,x t This is the isolation vector.

[0146] Preferably, in S13.3, the economic loss The expression is:

[0147]

[0148]

[0149] in, Let be the element in the i-th row and j-th column of the adjacency matrix of the economic network.

[0150] For ease of calculation, the above formula can also be rewritten as:

[0151]

[0152] Here, I is a matrix of all ones, and R... t Let be the adjacency matrix of the economic network.

[0153] Preferably, in S13.4, the isolation loss The expression is:

[0154]

[0155] For ease of calculation, the above formula can also be rewritten as:

[0156]

[0157] The total loss is the weighted sum of the three losses, calculated using the following formula:

[0158]

[0159] k1, k2, and k3 are the weights of the three types of loss, respectively. By setting different weights, a trade-off can be made between different losses.

[0160] In step S13.5, the optimal isolation method, i.e., the optimal X, is found. t This causes the total loss L to be... t To minimize the total loss L, this invention will reduce the total loss L. t The minimum optimization problem is transformed into a standard 0-1 quadratic programming problem. Since 0-1 quadratic programming is an NP-hard problem and cannot be solved in polynomial time, it is transformed into a semidefinite programming problem through convex relaxation and then solved using the CVX toolbox.

[0161] S13.6 includes:

[0162] Solve for x t Individuals corresponding to nodes with a value of 1 are isolated.

[0163] This invention establishes a three-objective optimization model to improve disease transmission control, reduce economic losses, and decrease the number of people in quarantine through isolation. Furthermore, this invention employs a convex relaxation approach to solve the problem, thereby improving algorithm performance.

[0164] Given several existing disease transmission control methods—Netshield, Netshield+, Acquaintance, GreedyDrop, SVID, Mod-Centrality, Comm-Centrality, HighDegree, and LowDegree—this paper compares the disease transmission control effectiveness of the method of this invention with that of existing methods, assuming the same average number of isolated nodes. These existing disease transmission control methods are briefly described below:

[0165] Netshield and Netshield+: Determine which individuals should be isolated by measuring the impact of isolating individuals on the network shield value;

[0166] Acquaintance: Isolating individuals by finding acquaintances;

[0167] GreedDrop: Identifying isolated individuals using graph theory;

[0168] SVID: Isolates individuals based on their Shapley value;

[0169] Mod-Centrality and Comm-Centrality: Community-based approaches that determine whether to isolate individuals based on their impact on the community structure;

[0170] HighDegree and LowDegree: Isolate individuals with the highest degree (HighDegree) or the lowest degree (LowDegree).

[0171] Specifically, a simulation was conducted on a scale-free network with 1000 nodes. The average degree of the network was 4. Economic losses were not considered. k2 = 0, and k1 = k3 = 1. The transmission rate / recovery rate of the infectious disease was set to 1.3, and the incubation period was fixed at 15 days. During the simulation, to simulate real-world conditions, the disease was first allowed to spread in the environment. After t... int After a certain time, control measures will be taken. int This is called the intervention time, and it is obtained with the same average number of isolated nodes. Figure 3 The diagram shows the effect of controlling the spread of disease.

[0172] It can be seen that, under the same average number of isolations, the method of the present invention is far superior to other methods in terms of disease transmission control.

[0173] The disease transmission control effect and economic losses of the method of the present invention were then compared with some isolation methods used in reality, namely: isolating symptomatic individuals, isolating symptomatic individuals and their close contacts, and isolating in proportion.

[0174] Specifically, experiments were conducted on a 500-node double scale-free network with an average degree of 4. The parameters were set as k1 = 1, k2 = 0.5, and k3 = 0. The transmission rate / recovery rate of the infectious disease was set to 1.3, and the incubation period was fixed at 15 days. Figure 4 The diagram showing the disease infection rate and Figure 5 The diagram shows the economic losses.

[0175] As can be seen, existing methods require a 60% isolation rate to achieve the same disease control effect, while the method of this invention only requires an average isolation rate of 10%. Therefore, this invention is superior to existing methods in terms of disease control. Furthermore, this invention is superior to existing methods in reducing economic losses.

[0176] In summary, the method of this invention provides a control method that can accurately determine which individuals should be isolated at every moment. Compared with macro-control methods that only give a general isolation ratio, the method of this invention greatly improves efficiency and accuracy.

[0177] The method of this invention not only has a much better control effect on disease transmission than micro-control methods, but also minimizes economic losses while controlling disease transmission, which is of great significance in actual policy-making.

[0178] Secondly, the present invention describes an isolation and control device for infectious diseases, the isolation and control device for infectious diseases described below and the isolation and control method for infectious diseases described above can be referred to in correspondence. Figure 6 A schematic diagram of a device for isolating and controlling infectious diseases is shown in the example. Figure 6 As shown, the device includes:

[0179] Module 21 is used to construct a physical contact network based on the contact behavior between individuals in the control area at the previous moment;

[0180] The prediction module 22 is used to predict the infection probability of each individual in the prevention and control area at the current moment based on the physical contact network and the current number of newly confirmed cases in the prevention and control area.

[0181] The determination module 23 is used to determine, based on the infection probability, the individuals in the prevention and control area who need to be isolated at the current moment.

[0182] This invention provides an isolation and control device for infectious diseases, comprising: constructing a physical contact network based on the contact behavior between individuals in the control area at the previous moment; estimating the infection probability of each individual in the control area at the current moment based on the physical contact network and the current number of newly confirmed cases in the control area; and determining the individuals in the control area that need to be isolated at the current moment based on the infection probability. This invention formulates a detailed isolation and control strategy down to the individual level based on the accurate estimation of the infection probability of each individual in the control area, thereby improving the effectiveness of isolation and control of infectious diseases.

[0183] Based on the above embodiments, as an optional embodiment, the construction module 21 is used for:

[0184] Each individual in the control area is considered as a node, and the physical contact between two individuals in the control area at the previous moment is considered as an edge between the corresponding nodes of the two individuals, thus generating the physical contact network.

[0185] Based on the above embodiments, as an optional embodiment, the prediction module includes: a first prediction unit;

[0186] The first prediction unit includes:

[0187] The first prediction module is used to, in the case that there are no new confirmed cases in the current prevention and control area, use the infection probability of each individual at the previous moment and the adjacency matrix of the physical contact network to predict the probability that each individual was in a latent state at the previous moment, the probability that each individual was in an infected state at the previous moment and is also in an infected state at the current moment, and the probability that each individual was in a susceptible state at the previous moment and is in a latent state at the current moment.

[0188] A submodule is configured to use the sum of the probability that each individual was in a latent state at the previous moment, the probability that each individual was in an infected state at the previous moment and is also in an infected state at the current moment, and the probability that each individual was in a susceptible state at the previous moment and is in a latent state at the current moment as the infection probability of each individual at the current moment.

[0189] Based on the above embodiments, as an optional embodiment, the formula for calculating the infection probability of each individual at the current moment is as follows:

[0190]

[0191] Where t is the current time, t-1 is the previous time, and γ i Let β be the recovery rate of the i-th individual in the control area. i Let i be the transmission rate of the i-th individual in the control area. Let be the infection probability of the i-th individual in the control area at time t. Let be the infection probability of the i-th individual in the control area at time t-1. Let be the probability of infection of the j-th individual in the control area at time t-1. V is the element in the i-th row and j-th column of the adjacency matrix of the physical contact network. t-1 Let be the set of all individuals in the control area at time t-1. The value represents whether the i-th individual in the control area before time t-1 has been diagnosed. If so, the value is 0; otherwise, the value is 1.

[0192] Based on the above embodiments, as an optional embodiment, the prediction module includes: a second prediction unit;

[0193] The second prediction unit includes:

[0194] The calculation submodule is used to calculate the probability that each newly confirmed patient was in the latent state at the previous moment and every moment before that, based on the probability distribution of the incubation period of the infectious disease, when there are newly confirmed patients in the current moment in the prevention and control area.

[0195] The determination submodule is used to determine the infection probability correction time relative to the control area based on the infection probability of each newly confirmed patient at each time before the previous time and the probability that each newly confirmed patient was in the latent state at each time before the previous time.

[0196] The correction submodule is used to correct the infection probability of each individual in the prevention and control area at the previous time based on the infection probability correction time.

[0197] The second prediction submodule is used to predict the infection probability of each individual in the prevention and control area at the current moment by using the infection probability of each individual in the prevention and control area at the previous moment after correction and the adjacency matrix of the physical contact network.

[0198] Based on the above embodiments, as an optional embodiment, the determining submodule includes:

[0199] The first setting subunit is used to subtract the infection probability of each newly diagnosed patient at the previous time and at each time before the previous time from the probability of each newly diagnosed patient being in the latent state at the previous time and at each time before the previous time, and take the time when the difference is greater than 0 as the correction time corresponding to each newly diagnosed patient.

[0200] The second setting subunit is used as the minimum time among all the corrected times corresponding to confirmed patients as the infection probability corrected time.

[0201] Based on the above embodiments, as an optional embodiment, the correction submodule includes:

[0202] The re-prediction sub-unit is used to re-predict the infection probability of each individual at the previous time step based on the correction value of the infection probability of each individual at the previous time step.

[0203] The third setting subunit is used to, if each individual is not a newly diagnosed patient, modify the infection probability of each individual at the previous moment to the maximum value of the infection probability of each individual at the previous moment and the re-estimated infection probability of each individual at the previous moment.

[0204] If each of the individuals is a newly confirmed patient, then the infection probability of each individual at the previous moment is corrected to the maximum value among the infection probability of each individual at the previous moment, the re-estimated infection probability of each individual at the previous moment, and the probability that each individual was in the latent state at the previous moment.

[0205] The correction value of the infection probability of each individual at the previous time step is obtained by correcting the infection probability of each individual step by step, starting from the infection probability correction time step.

[0206] Based on the above embodiments, as an optional embodiment, the determining module includes:

[0207] The economic network construction unit is used to construct an economic network based on the current economic exchanges between individuals in the prevention and control area.

[0208] The disease transmission loss construction unit is used to define the disease transmission loss of the prevention and control area as the weighted sum of the probabilities of all individuals in the prevention and control area going from healthy to infected at the current moment, and to construct the disease transmission loss based on the infection probability and the unassigned isolation vector.

[0209] An economic loss construction unit is used to define the total number of economic network edge destructions caused by isolation in the current moment as the economic loss of the prevention and control area, and to construct the economic loss based on the economic network and the isolation vector.

[0210] An isolation loss construction unit is used to define the total number of individuals currently isolated in the control area as the isolation loss of the control area, and to construct the isolation loss based on the isolation vector.

[0211] The solution unit is used to solve for the isolation vector with the objective of minimizing the weighted sum of the disease transmission loss, the economic loss, and the isolation loss;

[0212] The determining unit is used to determine, based on the solution result of the isolation vector, the individuals that need to be isolated in the prevention and control area at the current moment;

[0213] The isolation vector is used to characterize whether each individual in the control area should be isolated at the current moment.

[0214] Based on the above embodiments, as an optional embodiment, the disease transmission loss... The expression is:

[0215]

[0216]

[0217] The economic losses The expression is:

[0218]

[0219]

[0220] The isolation loss The expression is:

[0221]

[0222] Where t is the current time, t+1 is the next time, and δ i Let i be the weight of the i-th individual in the prevention and control area. V represents the probability that the i-th individual in the control area will change from healthy to infected at time t. t Let t be the set of all individuals in the control area at time t. and These are the i-th and j-th elements in the isolation vector, respectively, where a value of 1 indicates isolation and a value of 0 indicates no isolation; β i Let i be the transmission rate of the i-th individual in the control area. Let be the infection probability of the i-th individual in the control area at time t. Let be the probability of infection of the j-th individual in the control area at time t. Let be the element in the i-th row and j-th column of the adjacency matrix of the physical contact network at time t. The element in the i-th row and j-th column of the adjacency matrix of the economic network. The value represents whether the i-th individual in the control area before time t has been diagnosed. If so, the value is 0; otherwise, the value is 1.

[0223] Thirdly, Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7 As shown, the electronic device may include a processor 710, a communications interface 720, a memory 730, and a communication bus 740, wherein the processor 710, communications interface 720, and memory 730 communicate with each other via the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute a method for isolating and controlling infectious diseases. This method includes: constructing a physical contact network based on the contact behavior between individuals in the control area at the previous moment; estimating the infection probability of each individual in the control area at the current moment based on the physical contact network and the current number of newly confirmed cases in the control area; and determining the individuals in the control area that need to be isolated at the current moment based on the infection probability.

[0224] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0225] Fourthly, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute a method for isolating and controlling infectious diseases provided by the methods described above. The method includes: constructing a physical contact network based on the contact behavior between individuals in the control area at the previous moment; estimating the infection probability of each individual in the control area at the current moment based on the physical contact network and the number of newly confirmed cases in the control area at the current moment; and determining the individuals in the control area that need to be isolated at the current moment based on the infection probability.

[0226] Fifthly, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform a method for isolating and controlling infectious diseases provided by the methods described above. The method includes: constructing a physical contact network based on the contact behavior between individuals in the control area at the previous moment; estimating the infection probability of each individual in the control area at the current moment based on the physical contact network and the current number of newly confirmed cases in the control area; and determining, based on the infection probability, the individuals in the control area who need to be isolated at the current moment.

[0227] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0228] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0229] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for isolation and control of infectious diseases, characterized in that, The method includes: Construct a physical contact network based on the contact behavior between individuals in the control area at the previous moment; Based on the physical contact network and the current number of newly confirmed cases in the prevention and control area, the probability of infection for each individual in the prevention and control area at the current moment is estimated. Based on the infection probability, determine the individuals in the control area who need to be isolated at the current moment; the step of constructing a physical contact network based on the contact behavior between individuals in the control area at the previous moment includes: Each individual in the control area is considered a node, and the physical contact between two individuals in the control area at the previous moment is considered an edge between the corresponding nodes of the two individuals, thus generating the physical contact network; if there are no new confirmed cases in the control area at the current moment, the probability of infection for each individual in the control area at the current moment is estimated based on the physical contact network and the current number of new confirmed cases in the control area, including: Using the infection probability of each individual at the previous moment and the adjacency matrix of the physical contact network, the probability that each individual was in a latent state at the previous moment, the probability that each individual was in an infected state at the previous moment and is also in an infected state at the current moment, and the probability that each individual was in a susceptible state at the previous moment and is in a latent state at the current moment are estimated. The probability that each individual was in a latent state at the previous moment, the probability that each individual was in an infected state at the previous moment and is also in an infected state at the current moment, and the probability that each individual was in a susceptible state at the previous moment and is in a latent state at the current moment are summed as the infection probability of each individual at the current moment. In the event that there are newly confirmed cases in the prevention and control area at the current moment, the estimation of the infection probability of each individual in the prevention and control area at the current moment, based on the physical contact network and the current number of newly confirmed cases in the prevention and control area, includes: Based on the probability distribution of the incubation period of infectious diseases, calculate the probability that each newly diagnosed patient was in the latent state at the previous moment and at every moment before that. Based on the infection probability of each newly confirmed patient at each time before the previous time and the probability that each newly confirmed patient was in a latent state at each time before the previous time, the infection probability correction time relative to the control area is determined. Based on the infection probability correction time, the infection probability of each individual in the prevention and control area at the previous time is corrected; Using the corrected infection probability of each individual in the control area at the previous time and the adjacency matrix of the physical contact network, the infection probability of each individual in the control area at the current time is estimated. The determination of the infection probability correction time relative to the control area, based on the infection probability of each newly confirmed patient at every time before the previous time and the probability of each newly confirmed patient being in a latent state at every time before the previous time, includes: The probability of infection of each newly diagnosed patient at the previous time and at each time before that time is subtracted from the probability of each newly diagnosed patient being in the latent state at the previous time and at each time before that time, and the time when the difference is greater than 0 is taken as the correction time corresponding to each newly diagnosed patient. The minimum time among all the corrected times corresponding to confirmed patients is taken as the infection probability corrected time. The step of correcting the infection probability of each individual in the prevention and control area at the previous time step based on the infection probability correction time step includes: Based on the corrected value of the infection probability of each individual at the previous time step, the infection probability of each individual at the previous time step is re-estimated; If each of the individuals is not a newly confirmed patient, then the infection probability of each individual at the previous time step is corrected to the maximum value of the infection probability of each individual at the previous time step and the re-estimated infection probability of each individual at the previous time step. If each of the individuals is a newly confirmed patient, then the infection probability of each individual at the previous moment is corrected to the maximum value among the infection probability of each individual at the previous moment, the re-estimated infection probability of each individual at the previous moment, and the probability that each individual was in the latent state at the previous moment. The correction value of the infection probability of each individual at the previous time step is obtained by correcting the infection probability of each individual step by step, starting from the infection probability correction time step.

2. The method for isolation and control of infectious diseases according to claim 1, characterized in that, The probability of infection for each individual at the current moment is calculated as follows: ； in, For the current moment, For the previous moment, For the first in the prevention and control area The recovery rate of each individual For the first in the prevention and control area The rate of transmission to an individual For the first in the prevention and control area Individuals The probability of infection at any given time. For the first in the prevention and control area Individuals The probability of infection at any given time. For the first in the prevention and control area Individuals The probability of infection at any given time. The adjacency matrix of the physical contact network is the first... Line number Column elements, for The set of all individuals in the control area at any given moment. Characterization The first time in the prevention and control area mentioned before the time Whether an individual has been diagnosed is determined by a value of 0 if so, and 1 otherwise.

3. The method for isolation and control of infectious diseases according to any one of claims 1 to 2, characterized in that, The process of determining the individuals who need to be quarantined in the control area at the current moment based on the infection probability includes: An economic network is constructed based on the current economic exchanges between individuals in the prevention and control area. The economic network is constructed by treating each individual in the prevention and control area as a node, treating the economic exchanges between two individuals in the prevention and control area at the current moment as edges between the corresponding nodes of the two individuals, and assigning edge weights according to the magnitude of the economic exchanges between individuals. The weighted sum of the probabilities of all individuals in the control area going from healthy to infected at the current moment is defined as the disease transmission loss of the control area. The disease transmission loss is constructed based on the infection probability and the unassigned isolation vector. The total number of economic network edge disruptions caused by isolation in the current moment in the control area is defined as the economic loss of the control area, and the economic loss is constructed based on the economic network and the isolation vector. The total number of individuals currently isolated in the control area is defined as the isolation loss of the control area, and the isolation loss is constructed based on the isolation vector; The isolation vector is solved with the objective of minimizing the weighted sum of the disease transmission loss, the economic loss, and the isolation loss. Based on the solution results of the isolation vector, determine the individuals in the prevention and control area who need to be isolated at the current moment; The isolation vector is used to characterize whether each individual in the control area should be isolated at the current moment.

4. The method for isolation and control of infectious diseases according to claim 3, characterized in that, The loss of disease transmission The expression is: ； ； The economic losses The expression is: ； ； The isolation loss The expression is: ； in, For the current moment, For the next moment, For the first in the prevention and control area The weight of each individual, For the first in the prevention and control area Individuals The probability of going from healthy to infected at any moment. for The set of all individuals in the control area at any given moment. and The isolation vectors are respectively the first one. The element and the first There are 1 elements, where a value of 1 indicates isolation and a value of 0 indicates no isolation. For the first in the prevention and control area The rate of transmission to an individual For the first in the prevention and control area Individuals The probability of infection at any given time. For the first in the prevention and control area Individuals The probability of infection at any given time. for The adjacency matrix of the physical contact network at time t is the first Line number Column elements, The adjacency matrix of the economic network is the first... Line number Column elements, Characterization The first time in the prevention and control area mentioned before the time Whether an individual has been diagnosed is determined by a value of 0 if so, and 1 otherwise.

5. A device for isolating and controlling infectious diseases, characterized in that, The device includes: The module is used to construct a physical contact network based on the contact behavior between individuals in the control area at the previous moment; The prediction module is used to predict the infection probability of each individual in the prevention and control area at the current moment, based on the physical contact network and the current number of newly confirmed cases in the prevention and control area. The determination module is used to determine, based on the infection probability, the individuals in the control area who need to be isolated at the current moment; The building module is used for: Each individual in the control area is regarded as a node, and the physical contact between two individuals in the control area at the previous moment is regarded as the edge between the corresponding nodes of the two individuals, thus generating the physical contact network. The prediction module includes: a first prediction unit; The first prediction unit includes: The first prediction submodule is used to, in the case that there are no new confirmed cases in the prevention and control area at the current time, use the infection probability of each individual at the previous time and the adjacency matrix of the physical contact network to predict the probability that each individual was in the latent state at the previous time, the probability that each individual was in the infected state at the previous time and is also in the infected state at the current time, and the probability that each individual was in the susceptible state at the previous time and is in the latent state at the current time. The setting submodule is used to sum the probability that each individual was in a latent state at the previous moment, the probability that each individual was in an infected state at the previous moment and is also in an infected state at the current moment, and the probability that each individual was in a susceptible state at the previous moment and is in a latent state at the current moment as the infection probability of each individual at the current moment. The prediction module includes: a second prediction unit; The second prediction unit includes: The calculation submodule is used to calculate the probability that each newly confirmed patient was in the latent state at the previous moment and every moment before that, based on the probability distribution of the incubation period of the infectious disease, when there are newly confirmed patients in the current moment in the prevention and control area. The determination submodule is used to determine the infection probability correction time relative to the control area based on the infection probability of each newly confirmed patient at each time before the previous time and the probability that each newly confirmed patient was in the latent state at each time before the previous time. The correction submodule is used to correct the infection probability of each individual in the prevention and control area at the previous time based on the infection probability correction time. The second prediction submodule is used to predict the infection probability of each individual in the prevention and control area at the current moment by using the infection probability of each individual in the prevention and control area at the previous moment after correction and the adjacency matrix of the physical contact network. The determining submodule includes: The first setting subunit is used to subtract the infection probability of each newly diagnosed patient at the previous time and at each time before the previous time from the probability of each newly diagnosed patient being in the latent state at the previous time and at each time before the previous time, and take the time when the difference is greater than 0 as the correction time corresponding to each newly diagnosed patient. The second setting subunit is used as the minimum time among all the corrected times corresponding to confirmed patients as the infection probability correction time. The correction submodule includes: The re-prediction sub-unit is used to re-predict the infection probability of each individual at the previous time step based on the correction value of the infection probability of each individual at the previous time step. The third setting subunit is used to, if each individual is not a newly diagnosed patient, modify the infection probability of each individual at the previous moment to the maximum value of the infection probability of each individual at the previous moment and the re-estimated infection probability of each individual at the previous moment. If each of the individuals is a newly confirmed patient, then the infection probability of each individual at the previous moment is corrected to the maximum value among the infection probability of each individual at the previous moment, the re-estimated infection probability of each individual at the previous moment, and the probability that each individual was in the latent state at the previous moment. The correction value of the infection probability of each individual at the previous time step is obtained by correcting the infection probability of each individual step by step, starting from the infection probability correction time step.

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