CN113764109B - Infectious disease transmission scale prediction method, device, medium and electronic equipment - Google Patents
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Abstract
The embodiment of the disclosure provides a method, a device, a medium and electronic equipment for predicting the transmission scale of infectious diseases. The method comprises the following steps: determining values of a plurality of parameters of an infectious disease transmission model based on infectious disease data, wherein the infectious disease transmission model corresponds to a target infectious disease, and the plurality of parameters comprise the number of closely-spaced people and the proportion of closely-spaced people in an exposed state; determining an infectious rate for each of a plurality of phases of an infectious disease transmission cycle of the target infectious disease based on values of the plurality of parameters and the infectious disease transmission model; the spread of the target stage of the target infectious disease is predicted based on the infection rates of the target stages of the plurality of stages. According to the technical scheme of the embodiment of the disclosure, the model is more in line with an actual epidemic prevention scene, and the influence of different epidemic prevention policies at each stage of an infectious disease transmission period on the infectious rate is considered, so that the transmission of the infectious disease can be predicted more efficiently and accurately.
Description
Technical Field
The present disclosure relates to the field of big data technology, and more particularly, to a method for predicting an infectious disease transmission scale, an infectious disease transmission scale predicting apparatus, a computer-readable medium, and an electronic device.
Background
The method is an important work for preventing, controlling and predicting infectious diseases by constructing a model for predicting the infectious disease transmission scale based on actual case data from the practical significance of mastering the basic rules of infectious diseases.
In the related technical scheme, based on historical case data of the target infectious disease, a trend of the historical data is learned by using a time sequence algorithm model, and future transmission of the target infectious disease is predicted. However, this technical solution predicts based on only historical case data, and does not consider the influence of the actual infectious disease prevention and control policy on the transmission of infectious diseases, resulting in inaccurate prediction results.
Therefore, how to accurately and efficiently predict the transmission of the target infectious disease is a technical problem to be solved.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of an embodiment of the present disclosure is to provide a method for predicting an infectious disease transmission scale, an apparatus for predicting an infectious disease transmission scale, a computer-readable medium, and an electronic device, so as to accurately and efficiently predict a transmission of a target infectious disease at least to some extent.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to a first aspect of embodiments of the present disclosure, there is provided a method of predicting the scale of transmission of an infectious disease, the method comprising: determining values of a plurality of parameters of an infectious disease transmission model, the infectious disease transmission model corresponding to a target infectious disease, based on infectious disease data, the plurality of parameters including a proportion of closely isolated population and exposed population; determining an infectious rate for each of a plurality of phases of an infectious disease transmission cycle of the target infectious disease based on values of the plurality of parameters and the infectious disease transmission model; the spread of a target stage of the target infectious disease is predicted based on the infection rate of the target stage of the plurality of stages.
According to a first aspect, in some example embodiments, the plurality of parameters further comprises: a susceptible population, an infectious population, and a headcount, the predicting the transmission of the target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages, comprising: determining a change rate of the susceptible population based on the susceptible population, the infected population, the headcount and the infection rate of the target stage; and determining the susceptible people in the next stage of the target stage based on the susceptible people change rate.
According to a first aspect, in some example embodiments, the plurality of parameters further comprises: an exposure population, a latency period duration, a time of exposure to close-proximity isolation, the predicting the transmission of the target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages, comprising: determining a first ratio between the number of exposed persons in the target stage and the time from the exposed state to the closely spaced state; determining a second ratio between the number of exposed persons in the target phase and the duration of the latency period; determining the change rate of the number of people in the exposure state based on the change rate of the number of people in the susceptibility state, the first ratio, the second ratio and the proportion of the number of people in the exposure state in the target stage; and determining the exposure state number of people in the next stage of the target stage based on the exposure state number change rate.
According to a first aspect, in some example embodiments, the plurality of parameters further comprises: closely isolating the time of onset to removal, said predicting the transmission of a target stage of said target infectious disease based on the infection rate of said target stage of said plurality of stages, comprising: determining a third ratio between the number of persons in the close-contact isolation state and the time from onset to removal of the close-contact isolation state at the target stage; determining a rate of change of the number of people in the closely-spaced state based on the first ratio, the third ratio, and the proportion of the number of people in the exposed state in the target stage; and determining the number of closely-spaced persons in the next stage of the target stage based on the change rate of the number of closely-spaced persons.
According to a first aspect, in some example embodiments, the plurality of parameters further comprises: the time from the infectious state to the removal state, the predicting the transmission of the target stage of the target infectious disease based on the infectious rate of the target stage of the plurality of stages, comprising: determining a fourth ratio between the number of infected persons at the target stage and the time from the infected person to the removed person; determining a rate of change of the number of people in an infected state based on the second ratio, the fourth ratio, and the proportion of people in an exposed state that are closely isolated at the target stage; and determining the number of infectious people in the next stage of the target stage based on the change rate of the number of infectious people.
According to a first aspect, in some example embodiments, the predicting the spread of the target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages comprises: determining the rate of change of the number of people in the removed state based on the third ratio and the fourth ratio of the target stage; and determining the removal state number of people in the next stage of the target stage based on the removal state number change rate.
According to a first aspect, in some example embodiments, the predicting the spread of the target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages comprises: predicting the transmission of a target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages by the following formulas (1) to (5):
N＝S t +E t +C t +I t +R t (6)
Wherein S is t Is the number of people in a susceptible state, I t Is the number of people in an infectious state, E t Is the number of people in the exposed state, C t Is closely connected with the isolated people, R t Is the number of people in the removed state, D e Is the duration of the incubation period, D c Is the time from the exposed state to the closely spaced state, D C-R Is the time between the onset and the removal of the close-contact isolation, D I-R The time from the infection state to the removal state, r is the proportion of the population in the exposure state which is closely isolated, beta is the infection rate, and N is the total population.
According to a second aspect of embodiments of the present disclosure, there is provided an infectious disease transmission scale prediction apparatus, the apparatus comprising: a parameter determination module for determining values of a plurality of parameters of an infectious disease transmission model based on infectious disease data, the infectious disease transmission model corresponding to a target infectious disease, the plurality of parameters including a proportion of closely-spaced people and exposed people; an infection rate determination module for determining an infection rate of each of a plurality of phases of an infection transmission cycle of the target infection based on values of the plurality of parameters and the infection transmission model; a prediction module for predicting a spread of a target stage of the target infectious disease based on an infection rate of the target stage of the plurality of stages.
According to a second aspect, in some example embodiments, the plurality of parameters further comprises: susceptible state number, infected state number and total number, the prediction module includes: the susceptible state number determining unit is used for determining the change rate of the susceptible state number based on the susceptible state number, the infected state number and the total number of the target stage and the infection rate of the target stage; and determining the susceptible people in the next stage of the target stage based on the susceptible people change rate.
According to a second aspect, in some example embodiments, the plurality of parameters further comprises: the prediction module further comprises: an exposure state number determining unit for determining a first ratio between the exposure state number of the target stage and a time from the exposure state to the close contact isolation state; determining a second ratio between the number of exposed persons in the target phase and the duration of the latency period; determining the change rate of the number of people in the exposure state based on the change rate of the number of people in the susceptibility state, the first ratio, the second ratio and the proportion of the number of people in the exposure state in the target stage; and determining the exposure state number of people in the next stage of the target stage based on the exposure state number change rate.
According to a second aspect, in some example embodiments, the plurality of parameters further comprises: the close-fitting isolation time from onset to removal, the prediction module further comprising: the close contact isolation person number determining unit is used for determining a third ratio between the close contact isolation person number in the target stage and the time from the close contact isolation attack to the removal state; determining a rate of change of the number of people in the closely-spaced state based on the first ratio, the third ratio, and the proportion of the number of people in the exposed state in the target stage; and determining the number of closely-spaced persons in the next stage of the target stage based on the change rate of the number of closely-spaced persons.
According to a second aspect, in some example embodiments, the plurality of parameters further comprises: the time from the infected state to the removed state, the prediction module further comprises: an infection state population determining unit configured to determine a fourth ratio between the number of infection state population at the target stage and a time from an infection state to a removal state; determining a rate of change of the number of people in an infected state based on the second ratio, the fourth ratio, and the proportion of people in an exposed state that are closely isolated at the target stage; and determining the number of infectious people in the next stage of the target stage based on the change rate of the number of infectious people.
According to a second aspect, in some example embodiments, the prediction module further comprises: a removal-state person number determining unit configured to determine a change rate of the removal-state person number based on the third ratio and the fourth ratio of the target stage; and determining the removal state number of people in the next stage of the target stage based on the removal state number change rate.
According to a second aspect, in some example embodiments, the prediction module is further to: predicting the transmission of a target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages by the following formulas (1) to (5):
N＝S t +E t +C t +I t +R t (6)
wherein S is t Is the number of people in a susceptible state, I t Is the number of people in an infectious state, E t Is the number of people in the exposed state, C t Is closely connected with the isolated people, R t Is the number of people in the removed state, D e Is the duration of the incubation period, D c Is the time from the exposed state to the closely spaced state, D C-R Is the time between the onset and the removal of the close-contact isolation, D I-R The time from the infection state to the removal state, r is the proportion of the population in the exposure state which is closely isolated, beta is the infection rate, and N is the total population.
According to a third aspect of embodiments of the present disclosure, there is provided a computer readable medium having stored thereon a computer program which when executed by a processor implements the infectious disease transmission scale prediction method as described in the first aspect of the above embodiments.
According to a fourth aspect of embodiments of the present disclosure, there is provided an electronic device, comprising: one or more processors; and a storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method for predicting the scale of transmission of infectious disease according to the first aspect of the above embodiment.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
in some embodiments of the disclosure, on one hand, closely-spaced related parameters are added to an infectious disease transmission model, so that an infectious disease transmission model considering closely-spaced people can be constructed, and the model is more in line with an actual epidemic prevention scene; on the other hand, the infectious rate of each of the plurality of stages of the infectious disease transmission cycle of the target infectious disease is determined, the transmission of the target stage of the target infectious disease is predicted based on the infectious rate of the target stage, and the influence of the difference in epidemic prevention policies of each stage of the infectious disease transmission cycle on the infectious rate is considered, so that the transmission of the infectious disease can be predicted more efficiently and accurately.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort. In the drawings:
FIG. 1 illustrates a flow diagram of an infectious disease transmission scale prediction method according to some example embodiments of the present disclosure;
FIG. 2 illustrates a schematic diagram of an infectious disease transmission scale prediction model, according to some example embodiments of the present disclosure;
FIG. 3 illustrates a schematic structural diagram of an infectious disease transmission scale prediction apparatus according to some example embodiments of the present disclosure;
fig. 4 shows a schematic structural diagram of an electronic device in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Hereinafter, a method for predicting an infectious disease transmission scale in an exemplary embodiment of the present disclosure will be described in detail with reference to the accompanying drawings.
Fig. 1 illustrates a flow diagram of an infectious disease transmission scale prediction method according to some example embodiments of the present disclosure. The execution subject of the infectious disease transmission scale prediction method provided by the embodiment of the present disclosure may be a computing device having a computing processing function, such as a desktop computer. The method for predicting the scale of transmission of infectious diseases includes steps S110 to S130, and the method for predicting the scale of transmission of infectious diseases in the exemplary embodiment will be described in detail with reference to the accompanying drawings.
Referring to fig. 1, in step S110, values of a plurality of parameters of an infectious disease transmission model are determined based on infectious disease data, the plurality of parameters including a proportion of closely-spaced people and exposed people.
In an example embodiment, the infectious disease transmission model corresponds to a target infectious disease, e.g., the target infectious disease is a coronavirus infectious disease (covd-19 virus), and the infectious disease transmission model is an infectious disease transmission model constructed for the coronavirus infectious disease. The plurality of parameters of the infectious disease transmission model include the number of closely spaced people and the proportion of closely spaced people exposed, and in addition, the infectious disease transmission model further comprises: the parameters of the susceptible state number, the infected state number, the exposed state number, the latency period duration, the time from the exposed state to the closely-connected isolation state, the time from the closely-connected isolation state to the removed state, the time from the infected state to the removed state, the total number and the like.
Further, the infectious disease data may be obtained from a statistical website of infectious disease data, and values of a plurality of parameters of the infectious disease transmission model may be determined according to the obtained infectious disease data, for example, values of parameters such as a susceptible population, an infected population, an exposed population, etc. may be obtained.
In step S120, the infectious rate of each of the plurality of phases of the infectious disease transmission cycle of the target infectious disease is determined based on the values of the plurality of parameters and the infectious disease transmission model.
In an example embodiment, the infectious disease transmission period of the target infectious disease is divided into a plurality of stages according to the specific policy implementation time of the infectious disease prevention and control, and the infectious rate of each stage is different due to the implementation of the infectious disease prevention and control policy. For example, the infectious disease transmission cycle may include: the policies of preventing and controlling infectious diseases in each stage are different, for example, policies of limiting population gathering or isolating closely-adhered people in the infectious disease initial transmission stage, and the infectious disease initial transmission stage is different from the infectious disease discovery stage.
Further, values of the acquired plurality of parameters are substituted into the infectious disease transmission model, and the infectious rate of each of the plurality of phases of the infectious disease transmission cycle of the target infectious disease is determined. For example, the infectious rate of each of the plurality of phases of the infectious disease transmission cycle of the target infectious disease is determined by a markov chain monte carlo algorithm, MCMC algorithm, based on the values of the plurality of parameters and the infectious disease transmission model. The MCMC algorithm is used to estimate the posterior distribution of the parameters in probability space by random sampling. The MCMC algorithm may include the steps of: step (1): a priori distribution of the parameter values of the infection rate is determined. Step (2): the value of the first accessed (or sampled) parameter is first determined as the current parameter value. Step (3): the next value of the parameter to be considered for access is presented in a certain way (e.g. Metropolis sampler sampling) based on the value of the currently accessed parameter. Step (4): and comparing the current parameter value with the posterior probability of the parameter value to be considered for access in the form of a ratio, and calculating the posterior probability to refer to the prior probability and the probability density of the posterior probability. And according to the magnitude of the ratio, accepting or rejecting the parameter value to be considered for sampling, and considering the parameter value as the current parameter value after accepting. Step (5): repeating the step (3) and the step (4) until a certain termination condition is met (say 10000 parameter values are accessed).
In step S130, the spread of the target stage of the target infectious disease is predicted based on the infection rates of the target stages among the stages.
In an example embodiment, after the infection rate of each of the plurality of phases of the infection transmission cycle is obtained, if the infection transmission condition of the target phase in the infection transmission cycle of the current infection is to be predicted, the transmission of the target phase of the current infection may be predicted based on the infection rate of the target phase in combination with the infection transmission model.
According to the technical scheme in the example embodiment of fig. 1, on one hand, closely-spaced isolation state related parameters are added into an infectious disease transmission model, so that the infectious disease transmission model considering closely-spaced people can be constructed, and the model is more in line with an actual epidemic prevention scene; on the other hand, the infectious rate of each of the plurality of stages of the infectious disease transmission cycle of the target infectious disease is determined, the transmission of the target stage of the target infectious disease is predicted based on the infectious rate of the target stage, and the influence of the difference in epidemic prevention policies of each stage of the infectious disease transmission cycle on the infectious rate is considered, so that the transmission of the infectious disease can be predicted more efficiently and accurately.
Fig. 2 illustrates a schematic diagram of an infectious disease transmission scale prediction model according to some example embodiments of the present disclosure.
And (one) model structure:
referring to fig. 2, fig. 2 is an overall structure of an infectious disease transmission model including infectious disease transmission states, transfer of states, and transmission routes. Wherein the arrow indicates a state transition and the dashed arrow indicates an infected. The infectious disease transmission model comprises a plurality of state parameters and value parameters, wherein S is an easy-to-infect state, E is an exposed state, C is a closely-spaced state, I is an infected state, R is a removed state, de is a latency period, dc is a time from the exposed state to the closely-spaced state, D I-R The time from the infection state to the removal state, r is the proportion of the population in the exposure state which is closely isolated, D C-R Is the time to close isolation of the onset to the removal state, β is the infectious rate. And the parameter r is the proportion of the exposed state crowd which is closely isolated, 1-r represents the proportion of the exposed state crowd flowing to the infection state, and each state parameter and value parameter can refer to a structural diagram.
In this infectious disease transmission model, setting the C-seal isolation state does not have the ability to transmit infection. Only the I-state of infection can infect S-state populations. Since the packer will not contact other people after isolation, and thus will not spread infection any more, the infection transmission model is more in line with the actual epidemic situation.
Further, the above-described infectious disease transmission model can be represented by the following formulas (1) to (5):
N＝S t +E t +C t +I t +R t (6)
wherein S is t Is the number of people in a susceptible state, I t Is the number of people in an infectious state, E t Is the number of people in the exposed state, C t Is closely connected with the isolated people, R t Is the number of people in the removed state, D e Is the duration of the incubation period, D c Is the time from the exposed state to the closely spaced state, D C-R Is the time between the onset and the removal of the close-contact isolation, D I-R The time from the infection state to the removal state, r is the proportion of the population in the exposure state which is closely isolated, beta is the infection rate, and N is the total population.
Further, predicting the transmission of the target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages, comprising: the transmission of the target stage of the target infectious disease is predicted by the above formulas (1) to (5) based on the infection rate of the target stage among the stages.
For example, referring to formula (1), the plurality of parameters of the infectious disease transmission model further include: predicting the transmission of a target stage of a target infectious disease based on the infection rate of the target stage in a plurality of stages, including: determining a rate of change of the susceptible population based on the susceptibility population at the target stage, the infectious population, the headcount, and the infection rate at the target stage, e.g And determining the susceptible people in the next stage of the target stage based on the change rate of the susceptible people.
Referring to formula (2), the plurality of parameters of the infectious disease transmission model further include: the number of people in the exposed state, the duration of the incubation period and the time from the exposed state to the closely-spaced state are based on a plurality ofThe infection rate of a target stage in a stage, predicting the transmission of said target stage of a target infectious disease, comprising: determining a first ratio between the number of exposed persons in the target phase and the time between the exposed persons and the closely spaced persons, e.gDetermining a second ratio between the number of exposed persons in the target phase and the length of the incubation period, e.g. +.>Determining the change rate of the number of people in the exposure state based on the change rate of the number of people in the susceptibility state, the first ratio, the second ratio and the proportion of the number of people in the exposure state in the target stage; based on the rate of change of the exposure population, the exposure population for the next phase of the target phase is determined.
Referring to formula (3), the plurality of parameters of the infectious disease transmission model further include: predicting the transmission of a target stage of a target infectious disease based on the infection rate of the target stage in the plurality of stages at a time when the close proximity isolation is incident to a removal state, comprising: determining a third ratio between the number of persons in the sealed and isolated state and the time of onset of the sealed and isolated state to the removed state of the target stage, e.g Determining a rate of change of the number of people in the closely-spaced state based on the first ratio, the third ratio, and the proportion of the people in the exposed state in the closely-spaced state at the target stage; and determining the number of closely-spaced persons in the next stage of the target stage based on the change rate of the number of closely-spaced persons.
Referring to equation (4), the plurality of parameters of the infectious disease transmission model further include: predicting the transmission of the target stage of the target infectious disease based on the infection rate of the target stage in the plurality of stages at a time from the infection state to the removal state, comprising: determining a fourth ratio of the number of infectious agents at the target stage to the time from the infectious agent to the removal agentBased on the second ratio and the fourth ratio of the target stageThe proportion of the exposure state population to be closely connected and isolated is used for determining the change rate of the infection state population; based on the rate of change of the number of infectious people, the number of infectious people in the next stage of the target stage is determined.
Referring to equation (5), predicting the transmission of the target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages in the infectious disease transmission model, including: determining the change rate of the number of people in the removed state based on the third ratio and the fourth ratio of the target stage; and determining the removal state number of people in the next stage of the target stage based on the removal state number change rate.
And (II) parameter estimation:
in the infectious disease transmission model, other parameters except the infectious rate parameter beta need to be estimated, and other parameters can be calculated and set from actual data, for example, can be calculated and set according to infectious disease statistical data.
Further, in an exemplary embodiment, the infectious disease transmission period is divided into several segments according to the implementation time of the epidemic prevention policy, one infectious rate β parameter is used for each segment, and then the MCMC algorithm is used for parameter estimation to calculate the value of the infectious rate β for each segment.
MCMC, a monte-carlo markov sampling algorithm, is used to estimate the posterior distribution of parameters by random sampling in probability space. The method can comprise the following steps: (1) The initial values of the various state parameters of the infectious disease transmission model are determined, and the values of other known parameters are used. The values of the set of corresponding unknown parameters, i.e. the values of the infectious rate beta parameter at each stage of the infectious disease transmission cycle, are obtained by randomly sampling from the a priori distribution, e.g. equally distributed (0, 5), and substituted into the infectious disease transmission model. (2) And (5) carrying out iterative computation to obtain new daily cases estimated by the infectious disease transmission model. And then compared with the actual daily new cases. If the two are relatively close, the set of parameters is accepted, otherwise the set of parameters is rejected. Then the next sampling calculation follows. (3) Many sets of parameter values are obtained by many, e.g. 18, sampling calculations. The mean and confidence intervals are calculated for these parameters, i.e. the values of the infectious rate β parameter for each phase of the infectious disease transmission cycle to be estimated.
Because the whole course infectious rate beta value is not changed in the conventional dynamics model, namely only a fixed value is estimated, which is different from the actual transmission situation, compared with the conventional dynamics model, the technical scheme of the embodiment of the disclosure considers the influence of different epidemic prevention policies at each stage of the infectious disease transmission period on the infectious rate, thereby being capable of more effectively and accurately predicting the transmission of the infectious disease.
(III) model application:
after the infection rate beta parameter is estimated, an infection disease transmission model can be operated iteratively to predict the infection disease transmission of a future target stage. The setting of the infectious disease transmission parameters can be manually adjusted according to a specific epidemic prevention policy, so that the optimization under some scenes can be performed.
According to the technical scheme in the example embodiment of fig. 2, on one hand, the model structure of the infectious disease transmission model is more fit to the actual epidemic prevention scene, and the model fitting and prediction effects are better; on the other hand, the influence of different epidemic prevention policies at each stage of an infectious disease transmission period on the infectious rate is considered, so that the transmission of the infectious disease can be predicted more efficiently and accurately; on the other hand, the influence of epidemic prevention policies such as close contact isolation and the like on the model prediction result is considered, and the accuracy of model prediction is improved.
It is noted that the above-described figures are merely schematic illustrations of processes involved in a method according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
The following describes apparatus embodiments of the present disclosure that may be used to perform the methods of infection transmission scale prediction described above in the present disclosure.
Fig. 3 illustrates a schematic structure diagram of an infectious disease transmission scale prediction apparatus according to some example embodiments of the present disclosure.
Referring to fig. 3, there is provided an infectious disease transmission scale prediction apparatus 300, the apparatus 300 including: a parameter determination module 310 for determining values of a plurality of parameters of an infectious disease transmission model, based on infectious disease data, the infectious disease transmission model corresponding to a target infectious disease, the plurality of parameters including a proportion of closely spaced people and exposed people; an infection rate determination module 320 for determining an infection rate of each of a plurality of phases of an infection transmission cycle of the target infection based on the values of the plurality of parameters and the infection transmission model; a prediction module 330 for predicting the transmission of a target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages.
According to a second aspect, in some example embodiments, the plurality of parameters further comprises: the prediction module 330 includes: the susceptible state number determining unit is used for determining the change rate of the susceptible state number based on the susceptible state number, the infected state number and the total number of the target stage and the infection rate of the target stage; and determining the susceptible people in the next stage of the target stage based on the susceptible people change rate.
According to a second aspect, in some example embodiments, the plurality of parameters further comprises: the prediction module 330 further includes: an exposure state number determining unit for determining a first ratio between the exposure state number of the target stage and a time from the exposure state to the close contact isolation state; determining a second ratio between the number of exposed persons in the target phase and the duration of the latency period; determining the change rate of the number of people in the exposure state based on the change rate of the number of people in the susceptibility state, the first ratio, the second ratio and the proportion of the number of people in the exposure state in the target stage; and determining the exposure state number of people in the next stage of the target stage based on the exposure state number change rate.
According to a second aspect, in some example embodiments, the plurality of parameters further comprises: the prediction module 330 further includes: the close contact isolation person number determining unit is used for determining a third ratio between the close contact isolation person number in the target stage and the time from the close contact isolation attack to the removal state; determining a rate of change of the number of people in the closely-spaced state based on the first ratio, the third ratio, and the proportion of the number of people in the exposed state in the target stage; and determining the number of closely-spaced persons in the next stage of the target stage based on the change rate of the number of closely-spaced persons.
According to a second aspect, in some example embodiments, the plurality of parameters further comprises: the prediction module 330 further includes: an infection state population determining unit configured to determine a fourth ratio between the number of infection state population at the target stage and a time from an infection state to a removal state; determining a rate of change of the number of people in an infected state based on the second ratio, the fourth ratio, and the proportion of people in an exposed state that are closely isolated at the target stage; and determining the number of infectious people in the next stage of the target stage based on the change rate of the number of infectious people.
According to a second aspect, in some example embodiments, the prediction module 330 further comprises: a removal-state person number determining unit configured to determine a change rate of the removal-state person number based on the third ratio and the fourth ratio of the target stage; and determining the removal state number of people in the next stage of the target stage based on the removal state number change rate.
According to a second aspect, in some example embodiments, the prediction module 330 is further configured to: predicting the transmission of a target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages by the following formulas (1) to (5):
N＝S t +E t +C t +I t +R t (6)
wherein S is t Is the number of people in a susceptible state, I t Is the number of people in an infectious state, E t Is the number of people in the exposed state, C t Is closely connected with the isolated people, R t Is the number of people in the removed state, D e Is the duration of the incubation period, D c Is the time from the exposed state to the closely spaced state, D C-R Is the time between the onset and the removal of the close-contact isolation, D I-R The time from the infection state to the removal state, r is the proportion of the population in the exposure state which is closely isolated, beta is the infection rate, and N is the total population.
Since each functional module of the infection transmission scale prediction apparatus of the exemplary embodiment of the present disclosure corresponds to a step of the exemplary embodiment of the above-described infection transmission scale prediction method, for details not disclosed in the embodiment of the apparatus of the present disclosure, please refer to the embodiment of the above-described infection transmission scale prediction method of the present disclosure.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer storage medium capable of implementing the above method is also provided. On which a program product is stored which enables the implementation of the method described above in the present specification. In some possible embodiments, the various aspects of the present disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.
The program product may take the form of a portable compact disc read-only memory (CD-ROM) and comprises program code and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product described above may take the form of any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 400 according to such an embodiment of the present disclosure is described below with reference to fig. 4. The electronic device 400 shown in fig. 4 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 4, the electronic device 400 is embodied in the form of a general purpose computing device. The components of electronic device 400 may include, but are not limited to: the at least one processing unit 410, the at least one memory unit 420, and a bus 430 connecting the various system components, including the memory unit 420 and the processing unit 410.
Wherein the storage unit stores program code that is executable by the processing unit 410 such that the processing unit 410 performs steps according to various exemplary embodiments of the present disclosure described in the "exemplary method" section of the present specification. For example, the processing unit 410 may perform the operations as shown in fig. 1: step S110: determining values of a plurality of parameters of the infectious disease transmission model based on the infectious disease data, the plurality of parameters including a proportion of closely spaced people and exposed people; step S120, based on the values of the plurality of parameters and the infectious disease transmission model, determining the infectious rate of each of a plurality of stages of the infectious disease transmission cycle of the target infectious disease; step S130, predicting the transmission of the target stage of the target infectious disease based on the infection rate of the target stage in the plurality of stages.
Illustratively, the processing unit 410 may also perform the method for predicting the infectious disease transmission scale in the embodiments described above.
The storage unit 420 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 4201 and/or cache memory 4202, and may further include Read Only Memory (ROM) 4203.
The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 430 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 400 may also communicate with one or more external devices 490 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 400, and/or with any device (e.g., router, modem, etc.) that enables the electronic device 400 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 450. Also, electronic device 400 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 over the bus 430. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 400, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Furthermore, the above-described figures are only schematic illustrations of processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (9)
1. A method for predicting the spread of an infectious disease, the method comprising:
determining values of a plurality of parameters of an infectious disease transmission model, the infectious disease transmission model corresponding to a target infectious disease, the plurality of parameters including a closely spaced population, a proportion of an exposed population that is closely spaced, a susceptible population, an infected population, an exposed population, a latency period, a time from an exposed state to a closely spaced state, a time from a closely spaced onset to a removed state, a time from an infected state to a removed state, and a population;
determining an infectious rate for each of a plurality of phases of an infectious disease transmission cycle of the target infectious disease based on values of the plurality of parameters and the infectious disease transmission model;
predicting the transmission of a target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages by the following formulas (1) to (5):
N＝S t +E t +C t +I t +R t (6)
wherein S is t Is the number of people in a susceptible state, I t Is the number of people in an infectious state, E t Is the number of people in the exposed state, C t Is closely connected with the isolated people, R t Is the number of people in the removed state, D e Is the duration of the incubation period, D c Is the time from the exposed state to the closely spaced state, D C-R Is the time between the onset and the removal of the close-contact isolation, D I-R The time from the infection state to the removal state, r is the proportion of the population in the exposure state which is closely isolated, beta is the infection rate, and N is the total population.
2. The method of claim 1, wherein the plurality of parameters further comprises: a susceptible population, an infectious population, and a headcount, the predicting the transmission of the target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages, comprising:
determining a change rate of the susceptible population based on the susceptible population, the infected population, the headcount and the infection rate of the target stage;
and determining the susceptible people in the next stage of the target stage based on the susceptible people change rate.
3. The method of claim 2, wherein the plurality of parameters further comprises: an exposure population, a latency period duration, a time of exposure to close-proximity isolation, the predicting the transmission of the target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages, comprising:
determining a first ratio between the number of exposed persons in the target stage and the time from the exposed state to the closely spaced state;
Determining a second ratio between the number of exposed persons in the target phase and the duration of the latency period;
determining the change rate of the number of people in the exposure state based on the change rate of the number of people in the susceptibility state, the first ratio, the second ratio and the proportion of the number of people in the exposure state in the target stage;
and determining the exposure state number of people in the next stage of the target stage based on the exposure state number change rate.
4. The method of claim 3, wherein the plurality of parameters further comprises: closely isolating the time of onset to removal, said predicting the transmission of a target stage of said target infectious disease based on the infection rate of said target stage of said plurality of stages, comprising:
determining a third ratio between the number of persons in the close-contact isolation state and the time from onset to removal of the close-contact isolation state at the target stage;
determining a rate of change of the number of people in the closely-spaced state based on the first ratio, the third ratio, and the proportion of the number of people in the exposed state in the target stage;
and determining the number of closely-spaced persons in the next stage of the target stage based on the change rate of the number of closely-spaced persons.
5. The method of claim 4, wherein the plurality of parameters further comprises: the time from the infectious state to the removal state, the predicting the transmission of the target stage of the target infectious disease based on the infectious rate of the target stage of the plurality of stages, comprising:
Determining a fourth ratio between the number of infected persons at the target stage and the time from the infected person to the removed person;
determining a rate of change of the number of people in an infected state based on the second ratio, the fourth ratio, and the proportion of people in an exposed state that are closely isolated at the target stage;
and determining the number of infectious people in the next stage of the target stage based on the change rate of the number of infectious people.
6. The method of claim 5, wherein predicting the transmission of the target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages comprises:
determining the rate of change of the number of people in the removed state based on the third ratio and the fourth ratio of the target stage;
and determining the removal state number of people in the next stage of the target stage based on the removal state number change rate.
7. An infectious disease transmission scale prediction apparatus, comprising:
a parameter determination module for determining values of a plurality of parameters of an infectious disease transmission model based on infectious disease data, the infectious disease transmission model corresponding to a target infectious disease, the plurality of parameters including a closely-spaced population, a proportion of an exposed population that is closely-spaced, a susceptible population, an infected population, an exposed population, a latency period duration, a time from an exposed state to a closely-spaced state, a time from a closely-spaced onset to a removed state, a time from an infected state to a removed state, and a total population;
An infection rate determination module for determining an infection rate of each of a plurality of phases of an infection transmission cycle of the target infection based on values of the plurality of parameters and the infection transmission model;
a prediction module for predicting the spread of a target stage of the target infectious disease based on the infection rate of the target stage of the plurality of stages by the following formulas (1) to (5):
N＝S t +E t +C t +I t +R t (6)
wherein S is t Is the number of people in a susceptible state, I t Is the number of people in an infectious state, E t Is the number of people in the exposed state, C t Is closely connected with the isolated people, R t Is the number of people in the removed state, D e Is the duration of the incubation period, D c Is the time from the exposed state to the closely spaced state, D C-R Is the time between the onset and the removal of the close-contact isolation, D I-R The time from the infection state to the removal state, r is the time that the exposed state crowd isProportion of close-coupled isolation, β is the infection rate and N is the total number of people.
8. A computer-readable medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the infectious disease transmission scale prediction method according to any one of claims 1 to 6.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the infectious disease transmission scale prediction method as claimed in any one of claims 1 to 6.
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