CN111863271B - Early warning and prevention and control analysis system for major infectious disease transmission risk of new coronaries - Google Patents
Early warning and prevention and control analysis system for major infectious disease transmission risk of new coronaries Download PDFInfo
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Abstract
The invention discloses a system for early warning, preventing and controlling the transmission risk of a great infectious disease of new coronaries. The system comprises a transmission model building module, a transmission model analysis module and a transmission model analysis module, wherein the transmission model building module is used for building an infectious disease transmission dynamics model; the model solving module is used for solving an infectious disease transmission dynamics model and outputting the change relation of the infectious disease transmission degree along with time; the transmission risk early warning module is used for evaluating the transmission risk of infectious diseases and outputting transmission risk early warning information; and the risk prevention and control analysis module is used for analyzing the sensitivity of different parameter changes to the transmission risk of the infectious disease and outputting the prevention and control measures of the transmission risk of the infectious disease. According to the invention, the dynamic transmission risk of the infectious disease can be effectively estimated according to the dynamic transmission mechanism of the novel coronavirus pneumonia, and the risk early warning information can be timely issued, so that the effect of different parameters on preventing and controlling the transmission risk of the infectious disease can be further effectively analyzed, scientific and reliable risk prevention and control measures can be carried out, the risk of large-scale transmission of the infectious disease can be effectively reduced, and public health safety can be ensured.
Description
Technical Field
The invention belongs to an infectious disease risk and prevention and control system in the technical field of public health safety and epidemiology, and relates to a novel early warning, prevention and control analysis system for the transmission risk of a great infectious disease under the condition of large-scale outbreak of coronavirus pneumonia epidemic situation.
Background
The prior researches show that the COVID-19 has more special transmission characteristics than the prior infectious diseases, so that the spread speed is faster, the spread range is wider and the spread risk is higher.
In the related art, extensive researches on the theory and modeling of the transmission of the serious infectious diseases are carried out, including bin models such as SIR, SEIR and the like, and the models provide important means for describing the transmission mechanism and process of the infectious diseases. However, classical infectious disease models do not take into account the novel transmission characteristics exhibited by covd-19, including: the longer incubation period of the COVID-19, the infectivity in the incubation period, the influence of intervention measures on epidemic spread, the time-varying characteristics of parameters and the like have some limitations when applied to analysis of spread mechanism of the COVID-19. In addition, the current research on the aspect of early warning of the spreading risk of the serious infectious diseases is mostly inconsistent with the dynamic spreading mechanism of the infectious diseases, and is mostly based on qualitative analysis, and the research on prevention and control analysis of the spreading risk of epidemic situations is less due to lack of a quantitative calculation method.
Disclosure of Invention
In order to solve the problems in the background art, the invention aims to provide a high-risk early warning, prevention and control analysis system for the spread of infectious diseases of the COVID-19. According to the invention, the dynamic transmission risk of the infectious disease can be effectively estimated according to the dynamic transmission mechanism of the novel coronavirus pneumonia, and the risk early warning information can be timely issued, so that the effect of different parameters on preventing and controlling the transmission risk of the infectious disease can be further effectively analyzed, scientific and reliable risk prevention and control measures can be carried out, the risk of large-scale transmission of the infectious disease can be effectively reduced, and public health safety can be ensured.
In order to achieve the above purpose, the specific technical scheme adopted by the invention comprises the following steps:
the transmission model building module is used for dividing crowd states in an infectious disease outbreak area, building state transfer relations among different crowd states according to pathological features of infectious disease, and building an infectious disease transmission dynamics model;
the model solving module is used for inputting epidemic situation transmission history data, solving an infectious disease transmission dynamics model through a parameter identification optimizing method and outputting a change relation of the infectious disease transmission degree along with time;
the transmission risk early warning module is used for evaluating the transmission risk in three dimensions of the transmission scale, the transmission peak value and the transmission peak value time in the infection outbreak area according to the change relation of the infection spread degree along with time and outputting transmission risk early warning information;
and the risk prevention and control analysis module is used for analyzing the sensitivity of different parameter changes to the transmission risk of the infectious disease and outputting the prevention and control measures of the transmission risk of the infectious disease.
In the propagation model building module, the following processing is performed:
step 1.1, combining with new transmission characteristics shown by the COVID-19, dividing the crowd into 8 states, including: easily infected persons, non-isolated latency infected persons, diagnosed infected persons, asymptomatic infected persons, diagnosed cured persons, asymptomatic cured persons, dead of illness;
wherein, the easily infected person refers to a group which is not infected but lacks immunity and is easily infected by the infected person; non-sequestered latency infected refers to a population that has been infected but has not yet exhibited significant symptoms and is not sequestered; isolated latency infected persons refer to a population that has been infected but has not yet displayed significant symptoms and has been isolated; the infected person is the infected group which is diagnosed after medical detection; asymptomatic infectives refer to a population that is infected but does not exhibit obvious symptoms throughout the period of onset and is capable of self-healing; the diagnosis-confirmed healer refers to a group which is cured after the treatment of the diagnosis-confirmed infected person; asymptomatic healers refer to groups in which asymptomatic infected individuals recover themselves after a period of onset; the patient refers to a group that dies after being infected without being cured.
Step 1.2, based on the crowd states divided in the step 1.1, establishing a state transfer relation between different crowd states according to pathological features of infectious diseases;
the state transition relation between different crowd states specifically comprises: the infected person can be converted into the non-isolated latency infected person according to a certain probability after contacting with the non-isolated latency infected person, the asymptomatic infected person and the diagnosed infected person; the non-isolated latency infected person becomes an isolated latency infected person according to the isolated probability and becomes an asymptomatic infected person or a definite infected person according to a certain probability; the asymptomatic infected person becomes an asymptomatic cured person after the period of onset; the infected person in the isolated latency period becomes a diagnosed infected person after the detection period; after the treatment period, the patient with the infection can be changed into the patient with the cure or the death according to a certain probability; after the cure patient is diagnosed, the patient becomes an easy-to-infect patient according to a certain probability.
Step 1.3, based on the state transfer relation among different crowd states in step 1.2, building the following infectious disease dynamics model:
wherein S (t), E (t), Q (t), I (t), A (t), R (t), F (t), D (t), cI (t) are the susceptible, non-isolated latency infected, and non-isolated latency infected, respectivelyIsolating the number of latency infected persons, existing diagnosed infected persons, asymptomatic infected persons, cumulative diagnosed healed persons, cumulative asymptomatic healed persons, cumulative dead persons, cumulative diagnosed infected persons; n is the total number of people's mouths; t is a time number; beta is the disease transmissibility, i.e., the number of average contacts and infections per unit time Δt for an uninsulated infected person; kappa is the probability that latency infected persons are isolated in advance; alpha 1 A rate of acknowledgement for those infected with non-isolated latency; alpha 2 The rate of quarantine for non-quarantine latency infected persons; alpha 3 A rate of diagnosis for the isolated latency infected person; alpha 4 Transfer rate for the cured person to become a susceptible population; μ is the probability of the healer transitioning to a susceptible population; θ is the probability of a latency infected person to be converted to an asymptomatic infected person; ρ is the cure probability for the infected person; gamma ray A Recovery rate for asymptomatic infected persons; gamma is the rate of treatment for the infected person. the number of existing diagnosed infected persons I (t) at time t differs from the number of accumulated diagnosed infected persons cI (t) at time t in that the number of existing diagnosed infected persons at time t does not include the number of cured or dead persons before time t, and the number of accumulated diagnosed infected persons at time t includes the number of cured or dead persons before time t.
In the model solving module, the method is processed according to the following modes:
step 2.1, establishing a parameter identification optimization model, wherein the aim is that the deviation between the calculated simulation value of the parameter identification optimization model and epidemic propagation history data is minimum:
wherein I is * (t)、R * (t)、D * (t)、cI * (t) represents the number of existing diagnosis cases, the number of accumulated cure cases, the number of accumulated death cases, and the number of accumulated diagnosis cases at time t, respectively;
the calculated simulation values of the parameter identification optimization model comprise the number of the existing identified infected persons I (t), the accumulated identified infected persons cI (t), the accumulated identified healers R (t) and the accumulated dead persons D (t) in the step 1.3; epidemic situation spreading calendarThe history data is known data including the number of existing diagnosis cases I * (t) cumulative number of cases to be diagnosed cI * (t) cumulative cure case count R * (t), number of accumulated death cases D * (t)。
Step 2.2, inputting epidemic situation transmission history data based on the parameter identification optimization model in step 2.1, solving and obtaining indirect parameter values in all transmission stages of the infectious disease by using a Markov chain Monte Carlo algorithm aiming at different transmission stages of the infectious disease, wherein s is the ordinal number of the different transmission stages of the infectious disease, and comprises the following steps: the probability kappa that the infected person is isolated in advance in the non-isolated latency period in each stage of the transmission of the infection, the disease infection rate beta, the treatment rate gamma of the infected person and the cure probability rho of the infected person;
the indirect parameters are parameters which cannot be directly obtained or estimated by experience and are needed to be calculated through a parameter identification optimization model, and the rest parameters are direct parameters and can be obtained by knowing.
And 2.3, substituting the indirect parameter values in the step 2.2 into the infectious disease dynamics model in the step 1.3, and solving the change relation of the infectious disease diffusion degree with time.
The time-dependent relationship of the infection spread is specifically that the number of people in each group of people changes with time, including: non-isolated latency infected persons, diagnosed infected persons, asymptomatic infected persons, diagnosed cured persons, asymptomatic cured persons, and dead persons.
In the transmission risk early warning module, according to the result of the change relation of the infection disease diffusion degree in the step 2 along with time, adopting the infection disease transmission risk parameters established in the following steps to obtain transmission risk parameters in three dimensions of transmission scale, transmission peak value and transmission peak value time in an infection disease outbreak area, and outputting transmission risk early warning information:
step 3.1, processing according to the accumulated number of the infected persons to be diagnosed at the final moment and the accumulated number of the infected persons to be diagnosed at the current moment to obtain a transmission scale risk parameter R according to the following formula 1 ：
In the method, in the process of the invention,representing t end The number of the infected persons is accumulated and diagnosed correspondingly at the moment; />Representing t now The number of the infected persons is accumulated and diagnosed correspondingly at the moment; t is t end Representing the final moment in the epidemic situation development period; t is t now Representing the current time;
step 3.2, according to the maximum value of the number of the existing infected persons, obtaining a transmission peak risk parameter R according to the following formula 2 ：
R 2 ＝max{I(t)} (11)
Wherein max { I (t) } represents the maximum value of the number of existing diagnosed infected persons in the epidemic development period;
step 3.3, processing according to the corresponding time and the current time when the number of the existing infected persons reaches the maximum value and the following formula to obtain a propagation peak time risk parameter R 3 ：
R 3 ＝t peak -t now (12)
Wherein t is peak Indicating the time when the number of the existing diagnosed infected persons reaches the maximum value.
And 3.4, synthesizing the propagation risk parameters obtained in the steps 3.1 to 3.3, and obtaining a comprehensive propagation risk R by adopting the following formula:
ω 1 +ω 2 +ω 3 ＝1 (14)
in the method, in the process of the invention,and->Respectively, a propagation scale risk parameter R 1 Propagation peak risk parameter R 2 And a propagation peak time risk parameter R 3 Normalized values; omega 1 、ω 2 And omega 3 For propagating scale risk parameter R 1 Propagation peak risk parameter R 2 Risk parameter R of peak time of broadcasting 3 Is a weight of (2).
In the risk prevention and control analysis module, the risk prevention and control analysis module is processed according to the following mode:
step 4.1, obtaining the sensitivity δw of different parameters to the comprehensive propagation risk R according to the following formula:
δw＝ΔR/Δw w∈{β,κ,α 1 ,α 2 ,α 3 } (15)
wherein w represents parameters contained in the infectious disease dynamics model; δw represents the sensitivity of the parameter w to the comprehensive propagation risk R, Δw represents the variation increment of the parameter w, and Δr represents the variation increment of the comprehensive propagation risk R due to the variation increment of the parameter w; e represents one of the fetch sets; beta is the disease transmissibility; kappa is the probability that latency infected persons are isolated in advance; alpha 1 A rate of acknowledgement for those infected with non-isolated latency; alpha 2 The rate of quarantine for non-quarantine latency infected persons; alpha 3 A rate of diagnosis for the isolated latency infected person;
and 4.2, based on the sensitivity result in the step 4.1, aiming at a parameter w with sensitivity larger than a preset sensitivity threshold, pertinently applying intervention measures to control the parameter w, and realizing epidemic situation spreading risk prevention and control.
In the step 1.1: the novel propagation characteristics of the COVID-19 mainly comprise: the COVID-19 has a latency period, and an infected person has no obvious disease symptoms but has infectivity in the latency period; the COVID-19 has a certain proportion of asymptomatic infected persons, has infectivity, and self-heals after the disease period; the spread mode of the COVID-19 is contact spread, and the possibility of infecting medical staff exists in the patients who are confirmed to be treated in the hospital, but the infection rate is smaller; the covd-19 virus may be mutated, and the cured person has a limited immune cycle, and has a low probability of secondary infection after exceeding the immune cycle.
In the step 3.1: the propagation scale risk parameter R 1 The magnitude of the transmission scale of the infectious disease is reflected, and the larger the transmission scale risk parameter is, the larger the transmission range of the infectious disease is, and the larger the transmission risk of the infectious disease is, compared with the number of newly increased infectious persons caused at the current moment.
In the step 3.2: the propagation peak risk parameter R 2 The magnitude of the transmission peak of the infectious disease is reflected, and the larger the transmission peak risk parameter is, the larger the peak value of the existing number of the infected persons with the confirmed diagnosis caused by the infectious disease is, and the greater the pressure on medical resources is, so that the transmission risk of the infectious disease is larger.
In the step 3.3: the propagation peak time risk parameter R 3 The longer the time that the infectious disease reaches the peak value, the longer the transmission duration is, and other problems such as virus mutation and inter-region transmission are more easily caused, so the larger the transmission risk of the infectious disease is.
In the step 3.4: the comprehensive transmission risk R can give consideration to the transmission risk of infectious diseases in three aspects of transmission scale, transmission peak value and transmission peak value time. The greater the risk of comprehensive transmission, the greater the spread of the infectious disease, the greater the pressure on the medical resources caused by the peaks of the existing diagnosed infected persons, and the longer the transmission duration, and therefore the greater the risk of comprehensive transmission of the infectious disease.
The invention has the following beneficial effects:
the invention provides a system for realizing early warning, prevention and control analysis of the transmission risk of a great infectious disease under the condition of large-scale outbreak of a novel coronavirus pneumonia epidemic situation, and overcomes the defects that the novel transmission characteristics shown by the COVID-19 and the lack of the prevention and control system of the transmission risk of the infectious disease are not considered in the past infectious disease treatment.
The invention can combine the novel propagation characteristics shown by the COVID-19, including: the disease latent period, the infectivity thereof, the asymptomatic infection, the infectivity thereof, the infection possibility of medical staff, the secondary infection possibility of a healer caused by virus variation and the like, a transmission dynamics model which is more in line with the pathological characteristics of infectious diseases is established, and the dynamic transmission mechanism of the COVID-19 is expressed more accurately; establishing infectious disease transmission risk parameters from multiple dimensions, quantitatively evaluating the dynamic transmission risk of infectious disease and timely issuing risk early warning information; by selecting parameters with higher sensitivity to the transmission risk value of the infectious diseases, prevention and control measures are applied in a targeted manner, so that effective epidemic transmission risk prevention and control are formed.
The method can meet the public health safety guarantee requirement of the spread condition of the COVID-19 in a large range, can effectively evaluate the dynamic spread risk of the infectious disease according to the dynamic spread mechanism of the COVID-19 and timely issue risk early warning information, can be used for further effectively analyzing the effect of different parameters on preventing and controlling the spread risk of the infectious disease, and can carry out scientific and reliable risk prevention and control measures, thereby effectively reducing the risk of large-scale spread of the infectious disease and guaranteeing public health safety.
Drawings
FIG. 1 is a schematic diagram of a system according to the present invention;
FIG. 2 is a flow chart of an implementation of the present invention;
FIG. 3 is a graph of crowd state transition relationships;
FIG. 4 is a graph comparing simulation results of a spread of a local COVID-19 using the method of the present invention with official data.
Detailed Description
The invention is further described below with reference to the drawings and examples.
Specific examples of implementation of the complete method according to the present disclosure are as follows:
as shown in fig. 1, the embodied system includes: the system comprises a propagation model building module, a model solving module, a propagation risk early warning module and a risk prevention and control analysis module.
The transmission model building module is used for dividing crowd states in an infectious disease outbreak area, and building state transfer relations among different crowd states according to pathological features of infectious disease so as to build an infectious disease transmission dynamics model; the model solving module is used for inputting epidemic situation transmission history data, solving an infectious disease transmission dynamics model through a parameter identification optimizing method and outputting a change relation of the infectious disease transmission degree along with time; the transmission risk early warning module is used for evaluating the transmission risk in three dimensions of transmission scale, transmission peak value and transmission peak value time in an infectious disease outbreak area according to the output result of the change relation of the infectious disease diffusion degree along with time, and outputting transmission risk early warning information; and the risk prevention and control analysis module is used for analyzing the sensitivity of different parameter changes to the transmission risk of the infectious disease and outputting the prevention and control measures of the transmission risk of the infectious disease.
Taking a specific covd-19 burst process as an example, the implementation of the present invention will be described in detail with reference to the technical scheme and the accompanying drawings.
The general population of a certain place is n=1400 tens of thousands of people, the isolation capacity is 1 tens of thousands of people, and the medical capacity is 2.5 tens of thousands of people. The data of the official published daily existing diagnosis cases, accumulated cure cases, accumulated death cases and the like are used as historical transmission data.
The implementation flow of the invention is shown in figure 2, and the specific steps are as follows:
(A) The transmission model building module is used for dividing crowd states in an infectious disease outbreak area, and building state transfer relations among different crowd states according to pathological features of infectious disease so as to build an infectious disease transmission dynamics model;
step 1.1, dividing the crowd into 8 states by combining the actual propagation characteristics of the COVID-19;
step 1.2, based on the crowd states divided in step 1.1, establishing a state transfer relationship between different crowd states according to pathological features of infectious diseases, as shown in fig. 3;
step 1.3, based on the state transfer relation among different crowd states in step 1.2, building the following infectious disease dynamics model:
wherein S (t), E (t), Q (t), I (t), A (t), R (t), F (t), D (t), cI (t) are the number of people who are susceptible to infection at time t, who are not infected with the isolated latency, who are infected with the existing diagnosis, who are asymptomatic, who are cured by the accumulated diagnosis, who are cured by the accumulated asymptomatic, who are cured by the accumulated diagnosis, and who are cured by the accumulated diagnosis; n is the total number of people's mouths; t is a time number; beta is the disease transmissibility, i.e., the number of average contacts and infections per unit time Δt for an uninsulated infected person; kappa is the person with latent infectionProbability of being isolated in advance; alpha 1 A rate of acknowledgement for those infected with non-isolated latency; alpha 2 The rate of quarantine for non-quarantine latency infected persons; alpha 3 A rate of diagnosis for the isolated latency infected person; alpha 4 Transfer rate for the cured person to become a susceptible population; μ is the probability of the healer transitioning to a susceptible population; θ is the probability of a latency infected person to be converted to an asymptomatic infected person; ρ is the cure probability for the infected person; gamma ray A Recovery rate for asymptomatic infected persons; treatment rate for gamma infected persons.
(B) The model solving module is used for inputting epidemic situation transmission history data, solving an infectious disease transmission dynamics model through a parameter identification optimizing method and outputting a change relation of the infectious disease transmission degree along with time;
step 2.1, establishing a parameter identification optimization model, wherein the aim is that the deviation between a calculated simulation value of the model and epidemic propagation history data is minimum:
wherein I is * (t)、R * (t)、D * (t)、cI * (t) represents the number of existing diagnosis cases, the number of accumulated cure cases, the number of accumulated death cases, and the number of accumulated diagnosis cases at time t, respectively;
step 2.2, inputting epidemic situation transmission history data based on the parameter identification optimization model in step 2.1, and solving by using a Markov chain Monte Carlo algorithm to obtain indirect parameter values in each transmission stage of the infectious disease, wherein the method comprises the following steps: the probability kappa that the infected person is isolated in advance in the non-isolated latency period in each stage of the transmission of the infection, the disease infection rate beta, the treatment rate gamma of the infected person and the cure probability rho of the infected person;
the indirect parameter values and the direct parameter values (estimated from epidemic history spread data) in the various spread stages of the infectious disease obtained by solving in step 2.2 are shown in table 1.
TABLE 1 parameter values in various stages of infectious disease transmission
Step 2.3, substituting the indirect parameter calculation result in the step 2.2 into the infectious disease dynamics model in the step 1.3, and solving to obtain the change relation of the infectious disease diffusion degree along with time;
the relationship between the degree of spread of infectious disease and time obtained by the solution in step 2.3 is shown in fig. 4. It can be seen that the simulation result of the transmission process of the infectious diseases can be well matched with the historical transmission data.
(C) The transmission risk early warning module is used for evaluating the transmission risk in three dimensions of transmission scale, transmission peak value and transmission peak value time in an infectious disease outbreak area according to the output result of the change relation of the infectious disease diffusion degree along with time, and outputting transmission risk early warning information;
step 3.1, processing according to the accumulated number of the infected persons to be diagnosed at the final moment and the accumulated number of the infected persons to be diagnosed at the current moment to obtain a transmission scale risk parameter R according to the following formula 1 ：
In the method, in the process of the invention,representing t end The number of the infected persons is accumulated and diagnosed correspondingly at the moment; />Representing t now The number of the infected persons is accumulated and diagnosed correspondingly at the moment; t is t end Representing the final moment in the epidemic situation development period; t is t now Representing the current time;
step 3.2, according to the maximum value of the number of the existing infected persons, obtaining a transmission peak risk parameter R according to the following formula 2 ：
R 2 ＝max{I(t)} (11)
Wherein max { I (t) } represents the maximum value of the number of existing diagnosed infected persons in the epidemic development period;
step 3.3, processing according to the corresponding time and the current time when the number of the existing infected persons reaches the maximum value and the following formula to obtain a propagation peak time risk parameter R 3 ：
R 3 ＝t peak -t now (12)
Wherein t is peak The corresponding time when the number of the existing diagnosed infected persons reaches the maximum value is indicated;
and 3.4, synthesizing the propagation risk parameters obtained in the steps 3.1 to 3.3, and obtaining a comprehensive propagation risk R by adopting the following formula:
ω 1 +ω 2 +ω 3 ＝1 (14)
in the method, in the process of the invention,and->Respectively, a propagation scale risk parameter R 1 Propagation peak risk parameter R 2 And a propagation peak time risk parameter R 3 Normalized values; omega 1 、ω 2 And omega 3 For propagating scale risk parameter R 1 Propagation peak risk parameter R 2 Risk parameter R of peak time of broadcasting 3 Weights of (2);
and 3.5, calculating the infectious disease transmission risk parameters established in the steps 3.1-3.3 and the comprehensive transmission risk established in the step 3.4 according to the output result of the change relation of the infectious disease transmission degree in the step 2 along with time, obtaining the transmission risk in the infectious disease outbreak area, and outputting transmission risk early warning information.
The calculated spread risk values in the infection outbreak area in step 3.5 are shown in table 2.
TABLE 2 spread risk values in an infection outbreak area
It can be seen that the risk of transmission of infectious diseases at each stage gradually decreases in the course of the transmission and development of epidemic situations at a certain place. The highest risk values in the 1 st stage and the 2 nd stage indicate that epidemic situation is rapidly spread, and early warning information needs to be issued in time; the comprehensive transmission risk R of the 3 rd stage is obviously reduced, and the transmission scale risk R 1 And a propagation peak risk R 2 Also significantly reduced compared to the first two stages, but the propagation scale time risk R 3 Still higher, epidemic situation can spread widely and last long time if no measures are taken; the comprehensive transmission risk in the 4 th stage is reduced to a very small value, and 3 risk parameters are obviously reduced, so that the epidemic situation is effectively controlled; stage 5R 1 And R is 3 All fall to 0, indicating that epidemic situation has passed the peak period and the spreading trend begins to decline.
(D) And the risk prevention and control analysis module is used for analyzing the sensitivity of different parameter changes to the transmission risk of the infectious disease and outputting the prevention and control measures of the transmission risk of the infectious disease.
Step 4.1, obtaining the sensitivity of different parameters to the comprehensive propagation risk R according to the following formula:
δw＝ΔR/Δw w∈{β,κ,α 1 ,α 2 ,α 3 } (15)
wherein w represents parameters contained in the infectious disease dynamics model; δw represents the sensitivity of the parameter w to the comprehensive propagation risk R, Δw represents the variation increment of the parameter w, and Δr represents the variation increment of the comprehensive propagation risk R due to the variation increment of the parameter w; e represents one of the fetch sets; beta is the disease transmissibility; kappa is the probability that latency infected persons are isolated in advance; alpha 1 A rate of acknowledgement for those infected with non-isolated latency; alpha 2 The rate of quarantine for non-quarantine latency infected persons; alpha 3 The rate of definitive diagnosis for those infected with isolated latency.
The sensitivity results of the different parameters calculated in step 4.1 to the integrated propagation risk R are shown in table 3. Wherein, a negative sensitivity indicates that an increase in the parameter value has a decreasing effect on the epidemic risk.
TABLE 3 sensitivity values for the comprehensive spread risk of infectious diseases with different parameter variations
And 4.2, based on the sensitivity result in the step 4.1, aiming at a parameter w with sensitivity larger than a preset sensitivity threshold, pertinently applying intervention measures to control the parameter w, and realizing epidemic situation spreading risk prevention and control.
As can be seen from table 3, if the preset sensitivity threshold is 1, the sensitivity of the disease infection rate β and the isolation probability κ to the transmission risk of the disease is the highest, and the prevention and control effects are the most obvious; rate of definitive diagnosis alpha for persons with non-isolated latency infections 1 And quarantine Rate alpha for non quarantine latency infected persons 2 The increase of the number of the components also has good effect on preventing and controlling the transmission risk of the infectious diseases; rate of definitive diagnosis alpha for persons with isolated latency infections 3 The risk of transmission of the infectious disease is least sensitive, and the value change has little influence on the risk value.
Therefore, the contact infection rate of the infected person and the susceptible person should be preferentially reduced, and the isolation probability of the infected person is improved.
According to the method disclosed by the invention, the dynamic transmission mechanism of the COVID-19 can be accurately disclosed, epidemic situation transmission risks are comprehensively estimated, early warning information is timely issued, and simultaneously, the sensitivity of different parameter changes to epidemic situation transmission risk prevention and control is quantitatively estimated, so that a scientific and reliable risk prevention and control strategy is formed, the risk of large-scale spread of infectious diseases is effectively reduced, and public health safety is ensured.
Claims (4)
1. A system for early warning, prevention and control analysis of the risk of transmission of a significant infectious disease of a new coronaries pneumonia, comprising:
the transmission model building module is used for dividing crowd states in an infectious disease outbreak area, building state transfer relations among different crowd states according to pathological features of infectious disease, and building an infectious disease transmission dynamics model;
the model solving module is used for inputting epidemic situation transmission history data, solving an infectious disease transmission dynamics model through a parameter identification optimizing method and outputting a change relation of the infectious disease transmission degree along with time;
the transmission risk early warning module is used for evaluating the transmission risk in three dimensions of the transmission scale, the transmission peak value and the transmission peak value time in the infection outbreak area according to the change relation of the infection spread degree along with time and outputting transmission risk early warning information;
the risk prevention and control analysis module is used for analyzing the sensitivity of different parameter changes to the transmission risk of the infectious disease and outputting prevention and control measures of the transmission risk of the infectious disease;
in the propagation model building module, the following processing is performed:
step 1.1, combining with new transmission characteristics shown by the COVID-19, dividing the crowd into 8 states, including: easily infected persons, non-isolated latency infected persons, diagnosed infected persons, asymptomatic infected persons, diagnosed cured persons, asymptomatic cured persons, dead of illness;
step 1.2, based on the crowd states divided in the step 1.1, establishing a state transfer relation between different crowd states according to pathological features of infectious diseases;
step 1.3, based on the state transfer relation among different crowd states in step 1.2, building the following infectious disease dynamics model:
wherein S (t), E (t), Q (t), I (t), A (t), R (t), F (t), D (t), cI (t) are the number of people who are susceptible to infection at time t, who are not infected with the isolated latency, who are infected with the existing diagnosis, who are asymptomatic, who are cured by the accumulated diagnosis, who are cured by the accumulated asymptomatic, who are cured by the accumulated diagnosis, and who are cured by the accumulated diagnosis; n is the total number of people's mouths; t is a time number; b is the disease transmissibility, i.e. the number of average contact and infection per unit time Δt of a non-isolated infected person; kappa is the probability that latency infected persons are isolated in advance; a, a 1 A rate of acknowledgement for those infected with non-isolated latency; a, a 2 The rate of quarantine for non-quarantine latency infected persons; a, a 3 A rate of diagnosis for the isolated latency infected person; a, a 4 Transfer rate for the cured person to become a susceptible population; μ is the probability of the healer transitioning to a susceptible population; θ is the probability of a latency infected person to be converted to an asymptomatic infected person; ρ is the cure probability for the infected person; g A Recovery rate for asymptomatic infected persons; g is the rate of treatment for the infected person.
2. The system for early warning, prevention and control analysis of the risk of transmission of a significant infectious disease of a new coronal pneumonia according to claim 1, wherein: in the model solving module, the method is processed according to the following modes:
step 2.1, establishing a parameter identification optimization model, wherein the aim is that the deviation between the calculated simulation value of the parameter identification optimization model and epidemic propagation history data is minimum:
wherein I is * (t)、R * (t)、D * (t)、cI * (t) represents the number of existing diagnosis cases, the number of accumulated cure cases, the number of accumulated death cases, and the number of accumulated diagnosis cases at time t, respectively;
step 2.2, inputting epidemic situation transmission history data based on the parameter identification optimization model in step 2.1, and solving by using a Markov chain Monte Carlo algorithm to obtain indirect parameter values in different transmission stages of the infectious disease aiming at different transmission stages of the infectious disease, wherein the method comprises the following steps: the probability kappa that the infected person is isolated in advance in the non-isolated latency period in each stage of the transmission of the infection, the disease infection rate b, the treatment rate g of the infected person and the cure probability rho of the infected person;
and 2.3, substituting the indirect parameter values in the step 2.2 into the infectious disease dynamics model in the step 1.3, and solving the change relation of the infectious disease diffusion degree with time.
3. The system for early warning, prevention and control analysis of the risk of transmission of a significant infectious disease of a new coronal pneumonia according to claim 1, wherein: in the transmission risk early warning module, according to the result of the change relation of the infection disease diffusion degree in the step 2 along with time, adopting the infection disease transmission risk parameters established in the following steps to obtain transmission risk parameters in three dimensions of transmission scale, transmission peak value and transmission peak value time in an infection disease outbreak area, and outputting transmission risk early warning information:
step 3.1, processing according to the accumulated number of the infected persons to be diagnosed at the final moment and the accumulated number of the infected persons to be diagnosed at the current moment to obtain a transmission scale risk parameter R according to the following formula 1 ：
In the method, in the process of the invention,representing t end The number of the infected persons is accumulated and diagnosed correspondingly at the moment; />Representing t now The number of the infected persons is accumulated and diagnosed correspondingly at the moment; t is t end Representing the final moment in the epidemic situation development period; t is t now Representing the current time;
step 3.2, according to the maximum value of the number of the existing infected persons, obtaining a transmission peak risk parameter R according to the following formula 2 ：
R 2 ＝max{I(t)} (11)
Wherein max { I (t) } represents the maximum value of the number of existing diagnosed infected persons in the epidemic development period;
step 3.3, processing according to the corresponding time and the current time when the number of the existing infected persons reaches the maximum value and the following formula to obtain a propagation peak time risk parameter R 3 ：
R 3 ＝t peak -t now (12)
Wherein t is peak The corresponding time when the number of the existing diagnosed infected persons reaches the maximum value is indicated;
and 3.4, synthesizing the propagation risk parameters obtained in the steps 3.1 to 3.3, and obtaining a comprehensive propagation risk R by adopting the following formula:
ω 1 +ω 2 +ω 3 ＝1 (14)
in the method, in the process of the invention,and->Respectively, a propagation scale risk parameter R 1 Propagation peak risk parameter R 2 And a propagation peak time risk parameter R 3 Normalized values; omega 1 、ω 2 And omega 3 For propagating scale risk parameter R 1 Propagation peak risk parameter R 2 Risk parameter R of peak time of broadcasting 3 Is a weight of (2).
4. The system for early warning, prevention and control analysis of the risk of transmission of a significant infectious disease of a new coronal pneumonia according to claim 1, wherein: in the risk prevention and control analysis module, the risk prevention and control analysis module is processed according to the following mode:
step 4.1, obtaining the sensitivity dw of different parameters to the comprehensive propagation risk R according to the following formula:
dw＝ΔR/Δw w∈{b,κ,a 1 ,a 2 ,a 3 } (15)
wherein w represents parameters contained in the infectious disease dynamics model; dw represents the sensitivity of the parameter w to the comprehensive propagation risk R, Δw represents the variation increment of the parameter w, and Δr represents the variation increment of the comprehensive propagation risk R due to the variation increment of the parameter w; e represents one of the fetch sets; b is the disease transmissibility; kappa is the probability that latency infected persons are isolated in advance; a, a 1 Rapid diagnosis for those infected without isolated latencyA rate; a, a 2 The rate of quarantine for non-quarantine latency infected persons; a, a 3 A rate of diagnosis for the isolated latency infected person;
step 4.2, based on the sensitivity result in step 4.1, aiming at the parameters with the sensitivity larger than the preset sensitivity threshold w Targeted intervention measures are applied to realize parameter matching w And the epidemic situation spreading risk prevention and control are realized.
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