CN112435759A - Epidemic situation data prediction method and device, electronic equipment and storage medium - Google Patents

Epidemic situation data prediction method and device, electronic equipment and storage medium Download PDF

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CN112435759A
CN112435759A CN202011331468.0A CN202011331468A CN112435759A CN 112435759 A CN112435759 A CN 112435759A CN 202011331468 A CN202011331468 A CN 202011331468A CN 112435759 A CN112435759 A CN 112435759A
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梁世浩
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Yidu Cloud Beijing Technology Co Ltd
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Abstract

The disclosure relates to an epidemic situation data prediction method, device, equipment and medium, relates to the technical field of computers, and can be applied to a scene of predicting the number of infectious disease patients. The method comprises the following steps: classifying the infection state of the initial infectious disease model into a first infection state and a second infection state according to the regional risk level; establishing an initial infectious disease population prediction model according to the first infection state and the second infection state, and determining parameters to be estimated of the initial infectious disease population prediction model; acquiring newly increased numbers of people in two infection states in an initial infectious disease number prediction model every day, and determining target parameter values of parameters to be estimated according to the newly increased numbers of people in each infection state every day; and substituting the target parameter value into the initial infectious disease population prediction model to obtain an infectious disease population prediction model, and determining the epidemic situation prediction population according to the infectious disease population prediction model. According to the method and the device, an epidemic situation propagation prediction model suitable for a policy grading regulation and control scene can be established, so that epidemic situation population prediction is more accurate.

Description

Epidemic situation data prediction method and device, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to an epidemic situation data prediction method, an epidemic situation data prediction apparatus, an electronic device, and a computer-readable storage medium.
Background
Epidemic situation prediction is an important part of an epidemic disease prevention and control system, and has very important significance in accurately establishing an epidemic disease evolution dynamic model. A model which can predict and provide reliable and sufficient information for prevention and control is established, and scientific basis can be provided for accurate strategy of disease prevention and control and public defense strategy. Existing epidemic mathematical models typically include: a Susceptible-exposer-infected-Recovered (SEIR) model, a Susceptible-infected (SI) model, a Susceptible-infected-Recovered (SIR) model, and a Susceptible-infected-Recovered-Susceptible (SIRs) model.
It is to be noted that the information disclosed in the above background section is only for enhancement of 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
The invention aims to provide an epidemic situation data prediction method, an epidemic situation data prediction device, electronic equipment and a computer readable storage medium, and further solves the problems that an existing epidemic situation mathematical model is not fit with the structure of epidemic situation population prediction in a policy grading regulation and control scene, and the prediction result is inaccurate to at least a certain extent.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the invention.
According to a first aspect of the present disclosure, there is provided an epidemic situation data prediction method, including: acquiring an initial infectious disease model, and dividing the infection state of the initial infectious disease model into a first infection state and a second infection state according to the regional risk level; establishing an initial infectious disease population prediction model according to the first infection state and the second infection state, and determining parameters to be estimated of the initial infectious disease population prediction model; acquiring the number of newly increased people per day in each infection state in the initial infectious disease number prediction model, and determining a target parameter value of a parameter to be estimated according to the number of newly increased people per day in each infection state; and substituting the target parameter value into the initial infectious disease population prediction model to obtain an infectious disease population prediction model, and determining the epidemic situation prediction population according to the infectious disease population prediction model.
Optionally, the infection status of the initial infectious disease model is divided into a first infection status and a second infection status according to the regional risk classification, comprising: acquiring a region risk level threshold and region risk level values of a plurality of regions to be predicted; determining an area to be predicted with an area risk grade value greater than or equal to an area risk grade threshold value as a first risk area, and determining people in an infection state in the first risk area as a first infection state; and determining the area to be predicted with the area risk level value smaller than the area risk level threshold value as a second risk area, and determining the crowd in the infection state in the second risk area as the second infection state.
Optionally, the initial infectious disease model further comprises a susceptible state, an exposed state and an explanted state; establishing a model for predicting the number of the initial infectious diseases according to the first infection state and the second infection state, wherein the model comprises the following steps: determining the population category in the initial infectious disease population prediction model; wherein the population categories include susceptible populations, exposed populations, populations with a first infection state, populations with a second infection state, and removed populations; determining state transition parameters of state transition among all crowd categories; and establishing an initial infectious disease population prediction model according to the population category and the state conversion parameters.
Optionally, determining a state transition parameter of state transition between the crowd categories includes: determining the number of people in all the areas to be predicted in the first risk area as a first number of people; determining a first transfer ratio by converting the proportion of the exposed population into the proportion of the first infected population; respectively determining a second person number ratio and a second transfer ratio according to the first person number ratio and the first transfer ratio; determining the transfer rate of the infection state population to the removed population as the transfer rate of infection removal; the transfer rate of the exposure population to the infection state population was determined as the exposure infection transfer rate.
Optionally, the parameter to be estimated includes an infection coefficient, and determining the parameter to be estimated of the initial infectious disease population prediction model includes: determining a transmission cycle of the infectious disease to be predicted and time series data corresponding to the transmission cycle; acquiring prevention and control conditions corresponding to the infectious diseases in the transmission cycle; dividing a propagation cycle into a plurality of propagation periods according to the prevention and control conditions, and dividing time sequence data into a corresponding number of sub-time sequence data; and determining the parameters to be estimated under each propagation period according to the plurality of sub time sequence data.
Optionally, the target parameter values include a target parameter mean value and a target parameter confidence interval, and the target parameter values of the parameters to be estimated are determined according to the number of newly added people each day in each infection state, including: acquiring prior distribution of parameters to be estimated; based on the number of newly increased people per day and prior distribution in each infection state, performing iterative computation by adopting a parameter estimation method until the iterative result of the iterative computation is converged; and determining a target parameter mean value and a target parameter confidence interval according to the converged iteration result.
Optionally, determining the epidemic situation prediction population according to the infectious disease population prediction model, including: acquiring a multi-term distribution function of state transition among different states in an infectious disease population prediction model; calculating the transition probability of each state in the infectious disease number prediction model according to the multi-item distribution function, and performing state iterative calculation on different population classes in the infectious disease number prediction model; and determining the epidemic situation prediction population according to the calculation result after the state iterative calculation.
According to a second aspect of the present disclosure, there is provided an epidemic situation data prediction apparatus, comprising: the infection state determining module is used for acquiring an initial infectious disease model and dividing the infection state of the initial infectious disease model into a first infection state and a second infection state according to the regional risk level; the model establishing module is used for establishing an initial infectious disease population prediction model according to the first infection state and the second infection state and determining parameters to be estimated of the initial infectious disease population prediction model; the parameter value determining module is used for acquiring the number of newly increased people per day in each infection state in the initial infectious disease number prediction model and determining a target parameter value of the parameter to be estimated according to the number of newly increased people per day in each infection state; and the people number prediction module is used for substituting the target parameter value into the initial infectious disease people number prediction model to obtain an infectious disease people number prediction model, and determining the epidemic situation prediction people number according to the infectious disease people number prediction model.
Optionally, the infection status determining module includes an infection status determining unit, configured to obtain a threshold value of a regional risk level and regional risk level values of a plurality of regions to be predicted; determining an area to be predicted with an area risk grade value greater than or equal to an area risk grade threshold value as a first risk area, and determining people in an infection state in the first risk area as a first infection state; and determining the area to be predicted with the area risk level value smaller than the area risk level threshold value as a second risk area, and determining the crowd in the infection state in the second risk area as the second infection state.
Optionally, the model building module includes a model building unit, configured to determine a population category in the initial infectious disease population prediction model; wherein the population categories include susceptible populations, exposed populations, populations with a first infection state, populations with a second infection state, and removed populations; determining state transition parameters of state transition among all crowd categories; and establishing an initial infectious disease population prediction model according to the population category and the state conversion parameters.
Optionally, the model establishing unit includes a state parameter determining subunit, configured to determine a number of people in all the areas to be predicted in the first risk area as a first number of people; determining a first transfer ratio by converting the proportion of the exposed population into the proportion of the first infected population; respectively determining a second person number ratio and a second transfer ratio according to the first person number ratio and the first transfer ratio; determining the transfer rate of the infection state population to the removed population as the transfer rate of infection removal; the transfer rate of the exposure population to the infection state population was determined as the exposure infection transfer rate.
Optionally, the model building module further includes a parameter to be estimated determining unit, configured to determine a transmission cycle of the infectious disease to be predicted and time series data corresponding to the transmission cycle; acquiring prevention and control conditions corresponding to the infectious diseases in the transmission cycle; dividing a propagation cycle into a plurality of propagation periods according to the prevention and control conditions, and dividing time sequence data into a corresponding number of sub-time sequence data; and determining the parameters to be estimated under each propagation period according to the plurality of sub time sequence data.
Optionally, the parameter value determining module includes a parameter value determining unit, configured to obtain a prior distribution of the parameter to be estimated; based on the number of newly increased people per day and prior distribution in each infection state, performing iterative computation by adopting a parameter estimation method until the iterative result of the iterative computation is converged; and determining a target parameter mean value and a target parameter confidence interval according to the converged iteration result.
Optionally, the people number prediction module comprises a people number prediction unit, configured to obtain a multi-term distribution function of state transition between different states in the infectious disease people number prediction model; calculating the transition probability of each state in the infectious disease number prediction model according to the multi-item distribution function, and performing state iterative calculation on different population classes in the infectious disease number prediction model; and determining the epidemic situation prediction population according to the calculation result after the state iterative calculation.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, implement the epidemic situation data prediction method according to any one of the above.
According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the epidemic data prediction method according to any one of the above.
The technical scheme provided by the disclosure can comprise the following beneficial effects:
according to the epidemic situation data prediction method in the exemplary embodiment of the disclosure, the infection state of the initial infectious disease model is divided into a first infection state and a second infection state according to the regional risk level; establishing an initial infectious disease population prediction model according to the first infection state and the second infection state, and determining parameters to be estimated of the initial infectious disease population prediction model; acquiring the number of newly increased people per day in each infection state in the initial infectious disease number prediction model, and determining a target parameter value of a parameter to be estimated according to the number of newly increased people per day in each infection state; and substituting the target parameter value into the initial infectious disease population prediction model to obtain an infectious disease population prediction model, and determining the epidemic situation prediction population according to the infectious disease population prediction model. According to the epidemic situation data prediction method, on one hand, according to the regional risk level, the infectious disease population prediction model suitable for different policy grading regulation and control scenes is established, and the epidemic situation population prediction is carried out by adopting the established infectious disease population prediction model, so that the obtained prediction result is more accurate. On the other hand, the parameter to be estimated is estimated based on the prior distribution and the actual epidemic situation data, so that the target parameter value of the obtained parameter to be estimated is more accurate, and the accuracy of the prediction result can be further improved based on the obtained target parameter value.
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.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty. In the drawings:
fig. 1 schematically illustrates a flow chart of an epidemic data prediction method, according to an exemplary embodiment of the present disclosure;
FIG. 2 schematically illustrates a block diagram of an established initial infectious disease population prediction model according to an exemplary embodiment of the present disclosure;
FIG. 3 schematically shows a flow chart for establishing an initial infectious disease population prediction model according to an exemplary embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow chart for determining a target parameter value for a parameter to be estimated, according to an exemplary embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow chart for determining an epidemic predictive population from an infectious disease population prediction model, according to an exemplary embodiment of the present disclosure;
FIG. 6 schematically illustrates a block diagram of an epidemic data prediction apparatus, according to an exemplary embodiment of the present disclosure;
FIG. 7 schematically illustrates a block diagram of an electronic device according to an exemplary embodiment of the present disclosure;
fig. 8 schematically illustrates a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
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 subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in the form of software, or in one or more software-hardened modules, or in different networks and/or processor devices and/or microcontroller devices.
Epidemic situation prediction is an important part of an epidemic disease prevention and control system, and has very important significance in accurately establishing an epidemic disease evolution dynamic model. A model which can predict and provide reliable and sufficient information for prevention and control is established, and scientific basis can be provided for accurate strategy of disease prevention and control and public defense strategy. The existing epidemic mathematical models can generally comprise an SI model, an SIR model, an SIRS model, an SEIR model and the like, however, the existing epidemic models have the problems that the existing epidemic models are not fit in the structure of the scenes under the hierarchical prevention and control of the current policy, the prediction result is not accurate enough and the like.
Based on this, in the present exemplary embodiment, first, an epidemic situation data prediction method is provided, which may be implemented by using a server, or may also be implemented by using a terminal device, where the terminal described in the present disclosure may include a mobile terminal such as a mobile phone, a tablet computer, a notebook computer, a palm computer, a Personal Digital Assistant (PDA), and a fixed terminal such as a desktop computer. Fig. 1 schematically illustrates a schematic diagram of a process flow of an epidemic data prediction method, according to some embodiments of the present disclosure. Referring to fig. 1, the epidemic data prediction method may include the following steps:
step S110, an initial infectious disease model is obtained, and the infection state of the initial infectious disease model is divided into a first infection state and a second infection state according to the regional risk level.
And step S120, establishing an initial infectious disease population prediction model according to the first infection state and the second infection state, and determining parameters to be estimated of the initial infectious disease population prediction model.
And step S130, acquiring the number of newly increased people per day in each infection state in the initial infectious disease number prediction model, and determining a target parameter value of the parameter to be estimated according to the number of newly increased people per day in each infection state.
And step S140, substituting the target parameter value into the initial infectious disease population prediction model to obtain an infectious disease population prediction model, and determining the epidemic situation prediction population according to the infectious disease population prediction model.
According to the epidemic situation data prediction method in the embodiment, on one hand, according to the regional risk level, the epidemic situation population prediction model suitable for different policy grading regulation and control scenes is established, and the epidemic situation population prediction is carried out by adopting the established infectious disease population prediction model, so that the obtained prediction result is more accurate. On the other hand, the parameter to be estimated is estimated based on the prior distribution and the actual epidemic situation data, so that the target parameter value of the obtained parameter to be estimated is more accurate, and the accuracy of the prediction result can be further improved based on the obtained target parameter value.
Next, the epidemic situation data prediction method in the present exemplary embodiment will be further described.
In step S110, an initial infectious disease model is obtained, and the infection status of the initial infectious disease model is classified into a first infection status and a second infection status according to the regional risk classification.
In some exemplary embodiments of the present disclosure, the initial infectious disease model may be a SEIR model, which may include four populations in different states, e.g., susceptible, exposed, sick, explanted; wherein, the infected person is corresponding to the infection state. The regional risk level may be an epidemic risk level determined for different regions according to government epidemic regulation specific measures. For example, in a city, corresponding regional risk levels may be determined for different streets; in a rural area, corresponding regional risk levels may be determined for different villages. The first infection state may be an infection state corresponding to a zone having a zone risk level greater than or equal to a zone risk level threshold. And the infection state corresponding to the area with the second infection state area risk level smaller than the area risk level threshold value.
The policy grading regulation and control measures can be that corresponding risk grades are determined for different regions according to epidemic situations so as to implement different prevention and control policies according to different risk categories of the regions. In order to effectively prevent epidemic propagation, policy grading regulation and control measures can be adopted. In order to adapt to policy grading regulation measures, the infection state in the SEIR model can be divided into a first infection state and a second infection state according to different regional risk levels on the basis of the SEIR model, and the first infection state and the second infection state are respectively represented by I _ h and I _ l. The first infection state may represent the number of infection states corresponding to an area of high risk rating. The second infection state may represent the number of infection states corresponding to an area of low risk classification.
The skilled person in the art can easily understand that in epidemic situation prevention and control scenes in different regions, corresponding regions can be divided according to the actual requirements of local epidemic situation prevention and control to perform epidemic situation prevention and control, and the specific division mode of the regions to be predicted is not limited in any way in the present disclosure.
According to some exemplary embodiments of the present disclosure, an area risk level threshold value and area risk level values of a plurality of areas to be predicted are obtained; determining an area to be predicted with an area risk grade value greater than or equal to an area risk grade threshold value as a first risk area, and determining people in an infection state in the first risk area as a first infection state; and determining the area to be predicted with the area risk level value smaller than the area risk level threshold value as a second risk area, and determining the crowd in the infection state in the second risk area as the second infection state. The regional risk level threshold may be a risk level threshold determined according to factors such as epidemic propagation characteristics. For example, the regional risk level threshold may be 3. The area to be predicted may be an area where an epidemic exists. The region risk level value may be a numerical value of a region risk level corresponding to each of the regions to be predicted. The first risk area may be an area to be predicted, i.e. a high risk area, having an area risk level value greater than or equal to an area risk level threshold. The second risk area may be an area to be predicted, i.e. a low risk area, having an area risk level value smaller than an area risk level threshold.
In the method, the regional risk level values of different regions to be predicted are determined according to epidemic situation propagation characteristics and the number of infected persons in different regions. For example, the regional risk level values may be divided into five levels of 1,2,3,4, 5. Acquiring the region risk level values of a plurality of regions to be predicted, comparing the region risk level values of the regions to be predicted with a region risk level threshold value, and taking the regions to be predicted with the region risk level values being larger than or equal to the region risk level threshold value as first risk regions; and dividing the area to be predicted with the area risk level value smaller than the area risk level threshold value into a second risk area. Determining a population in the first risk area that is in an infectious state as a first infectious state; determining a second infection state for the population in the second risk area that is in the infection state. For example, the area to be predicted with the area risk level value of 3,4,5 may be determined as a first risk area, the population in the first risk area in the infection state may be determined as a first infection state, the area to be predicted with the area risk level value of 1,2 may be determined as a second risk area, and the population in the second risk area in the infection state may be determined as a second infection state.
It is easily understood by those skilled in the art that in practical application scenarios, other infection state structures may be set according to epidemic situation prevention and control requirements, such as determining other numbers of infection states, etc., and the disclosure does not make any special limitation thereto.
In step S120, an initial infectious disease population prediction model is established according to the first infection state and the second infection state, and a parameter to be estimated of the initial infectious disease population prediction model is determined.
In some exemplary embodiments of the present disclosure, the initial infectious disease population prediction model may be an infectious disease mathematical model including a first infection state and a second infection state, referred to as an SEIIR model. After the crowd types included in the initial infectious disease number prediction model are determined, the initial infectious disease number prediction model can be established. After the initial infectious disease number prediction model is established, the parameters to be estimated of the model are determined, so that the parameter values of the parameters to be estimated are further determined.
According to some exemplary embodiments of the present disclosure, a population category in an initial infectious disease population prediction model is determined; wherein the population categories include susceptible populations, exposed populations, populations with a first infection state, populations with a second infection state, and removed populations; determining state transition parameters of state transition among all crowd categories; and establishing an initial infectious disease population prediction model according to the population category and the state conversion parameters. The crowd category can be the crowd in different state categories in the initial infectious disease crowd prediction model; the population categories may include, among others, susceptible populations, exposed populations, populations with a first infection state, populations with a second infection state, and removed populations. The state transition parameters may be parameters corresponding to state transition between different crowd categories, for example, the state transition parameters may include a first people ratio, a second people ratio, a first transfer ratio, a second transfer ratio, an infection transfer rate, an exposure infection transfer rate, and the like. .
Referring to fig. 2, fig. 2 schematically illustrates a block diagram of an established initial communicant population prediction model according to an exemplary embodiment of the present disclosure. The model for predicting the number of initial infectious diseases may include susceptible population (S)210, exposed population 220(E), and first infectious population (I)h)231, second infectious disease state (I)l)232 and a removed population (R) 240. Specifically, the susceptible population 210 may be a naive population, but lacking in immunological competence, susceptible to infection upon contact with an infected population; the exposed population 220 may be those who have been exposed to infection, but who have had a temporary inability to transmit to others; the infected people can be people infected with infectious diseases, and can be transmitted to S class members to become people of E class or I class members; wherein the first population 231 is a population in an area of high risk, the second population 232 is a population in an area of low risk, and both the first population and the second population belong to the population category of infectious states; the removed population 240 may be isolated or immune to the disease.
Referring to fig. 3, fig. 3 schematically shows a flow chart for establishing an initial infectious disease population prediction model according to an exemplary embodiment of the present disclosure. In step S310, the population categories included in the initial infectious disease population prediction model may be determined. In step S320, after all the crowd categories are determined, state transition parameters for state transition between the crowd categories may be determined. Due to the mutual conversion among the crowd existing states in different states, the state conversion parameters of the mutual conversion among different crowd types in the initial infectious disease people number prediction model can be defined.
According to some exemplary embodiments of the present disclosure, the number of people in all the areas to be predicted in the first risk area is determined as the first number of people; determining a first transfer ratio by converting the proportion of the exposed population into the proportion of the first infected population; respectively determining a second person number ratio and a second transfer ratio according to the first person number ratio and the first transfer ratio; determining the transfer rate of the infection state population to the removed population as the transfer rate of infection removal; the transfer rate of the exposure population to the infection state population was determined as the exposure infection transfer rate. The number of people in all the areas to be predicted may be the total number of people contained in the plurality of areas to be detected, which may be noted as N. The first people ratio can be the ratio of the total number of people in the first risk area (high risk area) to the total number of people in all the areas to be predicted, N, and can be recorded as p. The first transfer ratio may be a ratio of the number of infected persons in the high-risk street when the exposed person (E) transfers to the infected state, and may be denoted as r. The second occupancy may be a ratio of the total number of people in the second risk area (low risk area) to the total number of people N in all the areas to be predicted. The second transfer proportion may be a proportion of the infected population of the low-risk street when the exposed population (E) is transferred to the infected state. The infection shift-out rate may be the rate of shift from state I (infected population) to state R (shifted-out population) daily. The exposure infection transfer rate may be the transfer rate of a daily state E (exposed population) to a state I (infected population). The effective exposure rate may be based on the number of exposures of the disease-free population to the disease-infected population per unit time and the probability of infection per exposure.
After the first person ratio and the first transfer ratio are determined, a second person ratio and a second transfer ratio can be determined according to the first person ratio and the first transfer ratio. Wherein, the second people ratio can be marked as 1-p, and the second transfer ratio can be marked as 1-r. Referring to fig. 3, in step S330, an initial infectious disease population prediction model may be established according to the population class and the state transition parameter. After the state transition parameters are defined, an initial infectious disease population prediction model can be established according to the population classes and the state transition parameters. Specifically, the partial differential equations corresponding to the initial infectious disease population prediction model are shown in formula 1 to formula 6.
Figure BDA0002795942130000111
Figure BDA0002795942130000112
Figure BDA0002795942130000113
Figure BDA0002795942130000114
Figure BDA0002795942130000115
N＝St+Et+Iht+Ilt+Rt(formula 6)
Wherein N is the total population, namely the total number of all the areas to be predicted. St、Et、Iht、Ilt、RtThe number of people respectively corresponds to the susceptible people, the exposed people, the high risk area infected people, the low risk area infected people and the removal people at the moment t. Parameter betahMay be the effective contact rate for the high risk area; parameter betalMay be the effective contact rate for the low risk area. The parameter p can be the proportion of the total population of the high-risk area to the total population N of all the areas to be predicted, namely the first person ratio; 1-p can be the proportion of the total population of the low risk area to the total population N of all areas to be predicted, namely the second population ratio; the fundamental difference between susceptible populations accessible to two infectious states can be simulated. The parameter r can be the proportion of the infection state of the high-risk area when the exposed population E is transferred to the infection state, namely the first transfer proportion; the parameters 1-r can be the proportion of the infectious state in the low risk area when the exposed population E is transferred to the infectious state, i.e. the proportion of the second transfer. The parameter gamma is the rate of transfer of state I (infected population) to state R (removed population) daily,i.e. the rate of infection migration transfer; the parameter σ is the rate of transfer of daily state E (exposed population) to state I (infected population), i.e. the rate of transfer of exposure infection.
It should be noted that, in the actual epidemic situation prediction scenario, the infection status can be divided into a corresponding number of infection status categories according to specific requirements, so as to establish a corresponding infectious disease population prediction model. The present disclosure does not impose any particular limitation on the number of infectious states divided into a plurality of different classes of infectious states.
According to some exemplary embodiments of the present disclosure, a transmission cycle of an infectious disease to be predicted and time series data corresponding to the transmission cycle are determined; acquiring prevention and control conditions corresponding to the infectious diseases in the transmission cycle; dividing a propagation cycle into a plurality of propagation periods according to the prevention and control conditions, and dividing time sequence data into a corresponding number of sub-time sequence data; and determining the parameters to be estimated under each propagation period according to the plurality of sub time sequence data. The transmission cycle may be a disease course corresponding to a certain infectious disease, and for example, the transmission cycle of the infectious disease is determined to be 21 days by analyzing the number of infectious diseases. The time-series data may be data of a new number of people who are newly added each day in each state corresponding to a certain transmission cycle of the infectious disease. The control condition may be a disease control policy set by a certain region for the infectious disease. The propagation period may be a plurality of propagation periods formed by dividing the propagation cycle according to the prevention and control condition. The sub-time series data may be series data composed of the number of newly added people for each day in each state in each propagation period. The sub time-series data may be sequence data obtained by dividing the sub time-series. The parameter to be estimated may be the corresponding infection coefficients in different propagation periods and a proportional relationship representing the corresponding infection coefficients in each propagation period in different infection states.
In the actual prevention and control process, different prevention and control policies (i.e., prevention and control conditions) are set in a certain region within a certain transmission period of the infectious disease, so that the transmission period can be divided into a plurality of transmission periods in the certain region according to the corresponding different prevention and control conditions within the transmission period. After the transmission period of the infectious disease to be predicted is determined, corresponding time series data in the transmission period, namely the number of newly added people per day in each state can be determined. After different prevention and control conditions formulated for infectious diseases in the propagation period are obtained, the propagation period can be divided into a plurality of propagation periods according to the formulated different prevention and control conditions, and the time sequence data is divided into a plurality of sub-time sequence data according to the propagation periods. And determining parameters to be estimated corresponding to each propagation period according to the divided sub-time sequence data.
For the infectious disease transmission condition in a certain area, because the corresponding infection coefficients are different under different prevention and control conditions, for example, a new infection case is found in a certain area, and the prevention and control measures are strengthened to the area so as to prevent the further transmission of the disease. In an actual scene, corresponding prevention and control measures can be made according to the disease development condition of a certain region. Therefore, the propagation cycle can be segmented according to the prevention and control condition of a certain area to form a plurality of propagation periods. The corresponding infection coefficients are the same in each propagation period and different in different propagation periods.
For example, in a practical application scenario, a propagation cycle may be divided into three propagation periods according to the prevention and control conditions, the infection coefficient parameters of the high-risk infection state and the low-risk infection state in the first period are the same, and the infection state parameters in the first period may be denoted as beta 0. In the second and third periods, the infection factor for the high-risk infection state may be an equal proportional multiple of the factor for the low-risk infection state of the same period. For example, the infection factors corresponding to the low risk region in the second and third periods can be denoted as beta1 and beta2, respectively, and the infection factors corresponding to the high risk region in the second and third periods can be denoted as beta1 × alpha and beta2 × alpha, respectively. Therefore, the parameters to be estimated of the initial infectious disease population prediction model include beta0, beta1, beta2 and alpha. After the parameters to be estimated are determined, the parameters to be estimated can be determined by adopting a parameter estimation method.
In addition, for different regions to be predicted, the corresponding propagation periods, such as the number of the propagation periods and the segmentation time points of the propagation periods, may be determined according to the region characteristics of the regions to be predicted and the epidemic propagation characteristics, which is not limited in any way by this disclosure.
In step S130, the new number of people per day in each infection state in the initial infectious disease number prediction model is obtained, and the target parameter value of the parameter to be estimated is determined according to the new number of people per day in each infection state.
In some exemplary embodiments of the present disclosure, the new population per day may be new population corresponding to the population category in each of different states in each day during the epidemic situation propagation period, for example, the new population per day may include new susceptible population per day, new exposed population per day, first infected population per day, second infected population per day, and removed population per day; wherein the newly increased number of people in each infection state per day comprises the newly increased number of people in the first infection state per day and the newly increased number of people in the second infection state per day. The target parameter value may be a parameter value corresponding to the parameter to be estimated.
After the number of newly increased people per day in each state is obtained, the target parameter value of the parameter to be estimated can be determined based on the initial infectious disease number prediction model and the number of newly increased people per day in each infection state, so that the target parameter value is substituted into the initial infectious disease number prediction model to generate the infectious disease number prediction model.
According to some exemplary embodiments of the present disclosure, a prior distribution of a parameter to be estimated is obtained; based on the number of newly increased people per day and prior distribution in each infection state, performing iterative computation by adopting a parameter estimation method until the iterative result of the iterative computation is converged; and determining a target parameter mean value and a target parameter confidence interval according to the converged iteration result. The prior distribution of the parameter to be estimated may be a distribution obtained from other relevant parameters and experience before statistical tests are performed on the parameter to be estimated. The parameter estimation method may be a method of calculating a target parameter value of a parameter to be estimated, and for example, the parameter estimation method may include a Markov Chain Monte Carlo (Markov Chain Monte Carlo, MCMC) method or the like. The target parameter mean may be a mean of the parameters to be estimated. The target parameter confidence interval may be a confidence interval of the parameter to be estimated.
Referring to fig. 4, fig. 4 schematically shows a flow chart for determining a target parameter value for a parameter to be estimated according to an exemplary embodiment of the present disclosure. In step S410, when calculating the parameter to be estimated, prior distribution of the parameter to be estimated may be obtained first, in this embodiment, based on experience, uniform distribution U (0, 2) with parameter beta obeying upper and lower bounds being 0 and 2, respectively, and uniform distribution U (0, 1) with parameter alpha obeying, may be set, and the above distribution is taken as prior distribution of the parameter to be estimated. In step S420, after the prior distribution of the parameters to be estimated is determined, iterative computation may be performed on the parameters to be estimated by using the MCMC algorithm in combination with the number of newly-increased people each day in each infection state until the iterative result of the iterative computation converges. In step S430, after the iteration result of the MCMC method converges, a target parameter mean and a target parameter confidence interval corresponding to the parameters to be estimated, beta0, beta1, beta2, and alpha, respectively, may be determined, that is, the target parameter value is determined.
It is easily understood by those skilled in the art that when calculating the target parameter value of the parameter to be estimated, in other application scenarios, other distribution functions may also be used as the prior distribution of the parameter beta and the parameter alpha. For example, the prior distributions of the parameter beta and the parameter alpha may be determined as Gamma distributions (Gamma distributions) to which the parameter beta and the parameter alpha respectively obey. The present disclosure does not impose any particular limitation on the distribution function to which the parameters beta and alpha are subject.
In step S140, the target parameter value is substituted into the initial infectious disease population prediction model to obtain an infectious disease population prediction model, and the epidemic situation prediction population is determined according to the infectious disease population prediction model.
In some exemplary embodiments of the present disclosure, the infectious disease population prediction model may be an infectious disease mathematical model obtained by substituting target parameter values into an initial infectious disease population prediction model. The epidemic situation prediction population can be newly increased population corresponding to different population classes in the period to be predicted in each day, wherein the newly increased population is contained in the infectious disease population model.
And substituting the target parameter value of the parameter to be estimated, which is obtained by calculation, into the initial infectious disease population prediction model to obtain the infectious disease population prediction model, and predicting the epidemic situation population according to the obtained infectious disease population prediction model to obtain the epidemic situation prediction population. Specifically, the number of newly added people can be predicted by updating and iterating in units of days, and the number of newly added people in each state every day can be calculated.
According to some exemplary embodiments of the present disclosure, a multi-term distribution function of state transitions between different states in an infectious disease population prediction model is obtained; calculating the transition probability of each state in the infectious disease number prediction model according to the multi-item distribution function, and performing state iterative calculation on different population classes in the infectious disease number prediction model; and determining the epidemic situation prediction population according to the calculation result after the state iterative calculation. The multiple distribution functions are used in the prediction process to calculate the distribution functions used in the population transfer between states of different population classes. The transition probability may be the probability of a transition between different states in the infectious disease population prediction model. The state iterative computation can be a process of iteratively computing the number of people in each state in the infectious disease number prediction model. The calculation result after the state iterative computation can be the number of newly increased people per day corresponding to each state of the infectious disease people number prediction model obtained after one iterative computation is finished.
Referring to fig. 5, fig. 5 schematically illustrates a flow chart for determining an epidemic predictive population from an infectious disease population prediction model according to an exemplary embodiment of the present disclosure. When the epidemic population number prediction model is adopted to predict the population number of the epidemic situation, the updating process of parameter estimation of the infectious disease population number prediction model is different from the updating process of parameter estimation of the initial infectious disease population number prediction model, and a plurality of distribution functions can be introduced when the population number transfer between different states is calculated in the prediction process. In step S510, a multi-term distribution function of state transitions between different states in the infectious disease population prediction model is obtained, for example, the multi-term distribution function may be a Multinom distribution function in R language. In step S520, transition probabilities between states in the infectious disease population prediction model are calculated according to the obtained multiple distribution functions, and state iteration calculation is performed on different population categories in the infectious disease population prediction model according to the transition probabilities. The transition probability of each state is calculated through a plurality of distribution functions, and certain randomness can be introduced into the state iterative calculation process. In step S530, through the daily iterative update, a calculation result after the state iterative calculation can be obtained, where the calculation result after the state iterative calculation includes the number of newly increased people each day in each state. In the process of predicting the epidemic situation population, the iteration time can be set to a certain time period in the future, and the epidemic situation population in the future time period is predicted to obtain the epidemic situation prediction population.
It should be noted that the terms "first", "second", "third", etc. are used in this disclosure only to distinguish different infection states, different risk areas, different infection state populations, different population ratios, different transfer ratios, etc., and should not limit the disclosure in any way.
In summary, the epidemic situation data prediction method of the present disclosure obtains the initial infectious disease model, and classifies the infection status of the initial infectious disease model into the first infection status and the second infection status according to the regional risk level; establishing an initial infectious disease population prediction model according to the first infection state and the second infection state, and determining parameters to be estimated of the initial infectious disease population prediction model; acquiring the number of newly increased people per day in each infection state in the initial infectious disease number prediction model, and determining a target parameter value of a parameter to be estimated according to the number of newly increased people per day in each infection state; and substituting the target parameter value into the initial infectious disease population prediction model to obtain an infectious disease population prediction model, and determining the epidemic situation prediction population according to the infectious disease population prediction model. On one hand, according to the regional risk level, an infectious disease population prediction model suitable for different policy grading regulation and control scenes is established, and the epidemic situation population prediction is carried out by adopting the established infectious disease population prediction model, so that the obtained prediction result can be more accurate. On the other hand, the parameter to be estimated is estimated based on the prior distribution and the actual epidemic situation data, so that the target parameter value of the obtained parameter to be estimated is more accurate, and the accuracy of the prediction result can be further improved based on the obtained target parameter value. In another aspect, the infectious disease population prediction model disclosed by the disclosure adopts a period segmentation method, so that the population change conditions of different population categories caused by policy changes in a real epidemic situation can be better fitted, and the accuracy of the model prediction result is improved.
It is noted that although the steps of the methods of the present invention are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
In addition, in the present exemplary embodiment, an epidemic situation data prediction apparatus is also provided. Referring to fig. 6, the epidemic data prediction apparatus 600 can include: an infection status determination module 610, a model building module 620, a parameter value determination module 630, and a population prediction module 640.
Specifically, the infection status determining module 610 is configured to obtain an initial infectious disease model, and divide the infection status of the initial infectious disease model into a first infection status and a second infection status according to the regional risk level; the model establishing module 620 is configured to establish an initial infectious disease population prediction model according to the first infection state and the second infection state, and determine parameters to be estimated of the initial infectious disease population prediction model; the parameter value determining module 630 is configured to obtain the number of newly increased people each day in each infection state in the initial infectious disease number prediction model, and determine a target parameter value of the parameter to be estimated according to the number of newly increased people each day in each infection state; the people number prediction module 640 is used for substituting the target parameter value into the initial infectious disease people number prediction model to obtain an infectious disease people number prediction model, and determining epidemic situation prediction people number according to the infectious disease people number prediction model.
The epidemic situation data prediction device 600 establishes an epidemic situation population prediction model suitable for different policy grading regulation and control scenes according to the regional risk grade, and predicts the epidemic situation population by adopting the established infectious disease population prediction model, so that the obtained prediction result is more accurate. In addition, the parameter to be estimated is estimated based on the prior distribution and the actual epidemic situation data, so that the obtained target parameter value of the parameter to be estimated is more accurate, the accuracy of the prediction result can be further improved based on the obtained target parameter value, and the device is an effective epidemic situation data prediction device.
In an exemplary embodiment of the present disclosure, the infection status determining module includes an infection status determining unit for obtaining a region risk level threshold and region risk level values of a plurality of regions to be predicted; determining an area to be predicted with an area risk grade value greater than or equal to an area risk grade threshold value as a first risk area, and determining people in an infection state in the first risk area as a first infection state; and determining the area to be predicted with the area risk level value smaller than the area risk level threshold value as a second risk area, and determining the crowd in the infection state in the second risk area as the second infection state.
In an exemplary embodiment of the present disclosure, the model building module includes a model building unit for determining a population category in the initial infectious disease population prediction model; wherein the population categories include susceptible populations, exposed populations, populations with a first infection state, populations with a second infection state, and removed populations; determining state transition parameters of state transition among all crowd categories; and establishing an initial infectious disease population prediction model according to the population category and the state conversion parameters.
In an exemplary embodiment of the present disclosure, the model building unit includes a state parameter determining subunit for determining a number of people in all the areas to be predicted of the number of people in the first risk area as a first number of people; determining a first transfer ratio by converting the proportion of the exposed population into the proportion of the first infected population; respectively determining a second person number ratio and a second transfer ratio according to the first person number ratio and the first transfer ratio; determining the transfer rate of the infection state population to the removed population as the transfer rate of infection removal; the transfer rate of the exposure population to the infection state population was determined as the exposure infection transfer rate.
In an exemplary embodiment of the present disclosure, the model building module further includes a parameter to be estimated determination unit for determining a propagation cycle of the infectious disease to be predicted and time-series data corresponding to the propagation cycle; acquiring prevention and control conditions corresponding to the infectious diseases in the transmission cycle; dividing a propagation cycle into a plurality of propagation periods according to the prevention and control conditions, and dividing time sequence data into a corresponding number of sub-time sequence data; and determining the parameters to be estimated under each propagation period according to the plurality of sub time sequence data.
In an exemplary embodiment of the present disclosure, the parameter value determining module includes a parameter value determining unit, configured to obtain a prior distribution of a parameter to be estimated; based on the number of newly increased people per day and prior distribution in each infection state, performing iterative computation by adopting a parameter estimation method until the iterative result of the iterative computation is converged; and determining a target parameter mean value and a target parameter confidence interval according to the converged iteration result.
In an exemplary embodiment of the present disclosure, the people number prediction module includes a people number prediction unit for obtaining a multi-term distribution function of state transition between different states in the infectious disease people number prediction model; calculating the transition probability of each state in the infectious disease number prediction model according to the multi-item distribution function, and performing state iterative calculation on different population classes in the infectious disease number prediction model; and determining the epidemic situation prediction population according to the calculation result after the state iterative calculation.
The specific details of each virtual epidemic situation data prediction device module are described in detail in the corresponding epidemic situation data prediction method, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the epidemic data prediction apparatus are mentioned, this 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, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 700 according to such an embodiment of the invention is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, electronic device 700 is embodied in the form of a general purpose computing device. The components of the electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, a bus 730 connecting different system components (including the memory unit 720 and the processing unit 710), and a display unit 740.
Wherein the memory unit stores program code that is executable by the processing unit 710 such that the processing unit 710 performs the steps according to various exemplary embodiments of the present invention as described in the above section "exemplary method" of the present specification.
The storage unit 720 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)721 and/or a cache memory unit 722, and may further include a read only memory unit (ROM) 723.
The memory unit 720 may include a program/utility 724 having a set (at least one) of program modules 725, such program modules 725 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 730 may represent one or more of any 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 a local bus using any of a variety of bus architectures.
The electronic device 700 may also communicate with one or more external devices 770 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 700, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 700 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 750. Also, the electronic device 700 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 760. As shown, the network adapter 760 communicates with the other modules of the electronic device 700 via the bus 730. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, 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 (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when said program product is run on the terminal device.
Referring to fig. 8, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present 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 may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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 for aspects of the present invention 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 and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, 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., through the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple 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 variations, 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.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.

Claims (10)

1. An epidemic situation data prediction method is characterized by comprising the following steps:
acquiring an initial infectious disease model, and dividing the infection state of the initial infectious disease model into a first infection state and a second infection state according to the regional risk level;
establishing an initial infectious disease population prediction model according to the first infection state and the second infection state, and determining parameters to be estimated of the initial infectious disease population prediction model;
acquiring the number of newly increased people per day in each infection state in the initial infectious disease number prediction model, and determining a target parameter value of the parameter to be estimated according to the number of newly increased people per day in each infection state;
and substituting the target parameter value into the initial infectious disease people number prediction model to obtain an infectious disease people number prediction model, and determining epidemic situation prediction people number according to the infectious disease people number prediction model.
2. The method of claim 1, wherein the classifying the infection status of the initial infectious disease model into a first infection status and a second infection status according to a regional risk classification comprises:
acquiring a region risk level threshold and region risk level values of a plurality of regions to be predicted;
determining an area to be predicted with the area risk level value being greater than or equal to the area risk level threshold value as a first risk area, and determining people in an infection state in the first risk area as the first infection state;
and determining the area to be predicted with the area risk level value smaller than the area risk level threshold value as a second risk area, and determining the crowd in the second risk area in the infection state as the second infection state.
3. The method of claim 1, wherein the initial infectious disease model further comprises a susceptible state, an exposed state, and an explanted state; the establishing of the initial infectious disease population prediction model according to the first infection state and the second infection state comprises the following steps:
determining a population class in the initial infectious disease population prediction model; wherein the population categories include susceptible populations, exposed populations, populations with a first infection state, populations with a second infection state, and removed populations;
determining state transition parameters of state transition among all crowd categories;
and establishing the initial infectious disease population prediction model according to the population category and the state conversion parameters.
4. The method of claim 3, wherein determining state transition parameters for state transitions between the respective crowd categories comprises:
determining the number of people in all the areas to be predicted in the first risk area as a first number of people;
determining a first transfer ratio from said exposed population to said first infected population;
respectively determining a second person number ratio and a second transfer ratio according to the first person number ratio and the first transfer ratio;
determining a transfer rate of infection removal from said population of infected individuals to said population of removed individuals;
and determining the transfer rate of the exposure population to the infection state population as the transfer rate of the exposure infection.
5. The method of claim 1, wherein the parameter to be estimated comprises an infection coefficient, and wherein determining the parameter to be estimated for the initial infectious disease population prediction model comprises:
determining a transmission cycle of an infectious disease to be predicted and time series data corresponding to the transmission cycle;
acquiring prevention and control conditions corresponding to the infectious diseases in the transmission period;
dividing the propagation cycle into a plurality of propagation periods according to the prevention and control conditions, and dividing the time sequence data into a corresponding number of sub-time sequence data;
and determining the parameters to be estimated under each propagation period according to the plurality of sub time sequence data.
6. The method of claim 1, wherein the target parameter values comprise a target parameter mean and a target parameter confidence interval, and the determining the target parameter values of the parameters to be estimated according to the number of new daily increases in each infection state comprises:
acquiring prior distribution of the parameters to be estimated;
based on the newly increased number of people per day and the prior distribution in each infection state, performing iterative computation by adopting a parameter estimation method until the iterative result of the iterative computation is converged;
and determining the target parameter mean value and the target parameter confidence interval according to the converged iteration result.
7. The method of claim 1, wherein determining a predictive population for an epidemic based on the predictive model of population for an infectious disease comprises:
acquiring a multi-term distribution function of state transition among different states in the infectious disease population prediction model;
calculating the transition probability of each state in the infectious disease number prediction model according to the multi-item distribution function, and performing state iterative calculation on different population classes in the infectious disease number prediction model according to the transition probability;
and determining the epidemic situation prediction population according to the calculation result after the state iterative calculation.
8. An epidemic situation data prediction apparatus, comprising:
the infection state determining module is used for acquiring an initial infectious disease model and dividing the infection state of the initial infectious disease model into a first infection state and a second infection state according to the regional risk level;
the model establishing module is used for establishing an initial infectious disease people number prediction model according to the first infection state and the second infection state and determining parameters to be estimated of the initial infectious disease people number prediction model;
the parameter value determining module is used for acquiring the number of newly increased people per day in each infection state in the initial infectious disease number prediction model and determining a target parameter value of the parameter to be estimated according to the number of newly increased people per day in each infection state;
and the people number prediction module is used for substituting the target parameter value into the initial infectious disease people number prediction model to obtain an infectious disease people number prediction model, and determining epidemic situation prediction people number according to the infectious disease people number prediction model.
9. An electronic device, comprising:
a processor; and
a memory having stored thereon computer readable instructions which, when executed by the processor, implement the epidemic data prediction method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the epidemic data prediction method according to any one of claims 1-7.
CN202011331468.0A 2020-11-24 2020-11-24 Epidemic situation data prediction method and device, electronic equipment and storage medium Pending CN112435759A (en)

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113257426A (en) * 2021-06-30 2021-08-13 杭州华网信息技术有限公司 Aggregated group flu prediction system, storage medium and device
CN113611429A (en) * 2021-05-12 2021-11-05 中国人民解放军军事科学院军事医学研究院 Infectious disease propagation deduction method and device and electronic equipment
CN113707336A (en) * 2021-08-26 2021-11-26 平安国际智慧城市科技股份有限公司 Infectious disease prevention and treatment early warning method, device, equipment and medium based on data analysis
CN113838580A (en) * 2021-09-03 2021-12-24 医渡云（北京）技术有限公司 Infectious disease infection scale prediction method, infectious disease infection scale prediction device, storage medium, and electronic device
CN114388138A (en) * 2022-01-11 2022-04-22 平安科技（深圳）有限公司 Artificial intelligence based epidemic situation prediction method, device, equipment and storage medium
CN114512245A (en) * 2022-02-17 2022-05-17 四川省疾病预防控制中心 New infectious disease model building method and computer device
CN115831388A (en) * 2023-02-17 2023-03-21 南京市疾病预防控制中心 Infectious disease simulation early warning method and system based on big data
CN113611429B (en) * 2021-05-12 2024-06-07 中国人民解放军军事科学院军事医学研究院 Infectious disease transmission deduction method and device and electronic equipment

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101777092A (en) * 2009-12-18 2010-07-14 中国人民解放军防化指挥工程学院 Method for predicting epidemic situation by spatial heterogeneity-based infectious disease propagation model
US20130275160A1 (en) * 2010-12-15 2013-10-17 Michal Lev System and method for analyzing and controlling epidemics
CN110993119A (en) * 2020-03-04 2020-04-10 同盾控股有限公司 Epidemic situation prediction method and device based on population migration, electronic equipment and medium
CN111611297A (en) * 2020-05-21 2020-09-01 中南大学 Propagation model establishing method considering parameter time-varying property and prediction method thereof
CN111768875A (en) * 2020-06-23 2020-10-13 深圳市城市公共安全技术研究院有限公司 Infectious disease epidemic situation prediction method, system, device and storage medium
CN111883260A (en) * 2020-07-27 2020-11-03 西北工业大学 Epidemic situation population evolution prediction method based on improved SEIR model
CN111933295A (en) * 2020-07-05 2020-11-13 成都吱吖科技有限公司 Latent-period infectious disease area early warning method based on big data
CN112002434A (en) * 2020-10-30 2020-11-27 平安科技（深圳）有限公司 Data processing method and device, electronic equipment and storage medium

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101777092A (en) * 2009-12-18 2010-07-14 中国人民解放军防化指挥工程学院 Method for predicting epidemic situation by spatial heterogeneity-based infectious disease propagation model
US20130275160A1 (en) * 2010-12-15 2013-10-17 Michal Lev System and method for analyzing and controlling epidemics
CN110993119A (en) * 2020-03-04 2020-04-10 同盾控股有限公司 Epidemic situation prediction method and device based on population migration, electronic equipment and medium
CN111611297A (en) * 2020-05-21 2020-09-01 中南大学 Propagation model establishing method considering parameter time-varying property and prediction method thereof
CN111768875A (en) * 2020-06-23 2020-10-13 深圳市城市公共安全技术研究院有限公司 Infectious disease epidemic situation prediction method, system, device and storage medium
CN111933295A (en) * 2020-07-05 2020-11-13 成都吱吖科技有限公司 Latent-period infectious disease area early warning method based on big data
CN111883260A (en) * 2020-07-27 2020-11-03 西北工业大学 Epidemic situation population evolution prediction method based on improved SEIR model
CN112002434A (en) * 2020-10-30 2020-11-27 平安科技（深圳）有限公司 Data processing method and device, electronic equipment and storage medium

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113611429A (en) * 2021-05-12 2021-11-05 中国人民解放军军事科学院军事医学研究院 Infectious disease propagation deduction method and device and electronic equipment
CN113611429B (en) * 2021-05-12 2024-06-07 中国人民解放军军事科学院军事医学研究院 Infectious disease transmission deduction method and device and electronic equipment
CN113257426A (en) * 2021-06-30 2021-08-13 杭州华网信息技术有限公司 Aggregated group flu prediction system, storage medium and device
CN113257426B (en) * 2021-06-30 2021-09-21 杭州华网信息技术有限公司 Aggregated group flu prediction system, storage medium and device
CN113707336A (en) * 2021-08-26 2021-11-26 平安国际智慧城市科技股份有限公司 Infectious disease prevention and treatment early warning method, device, equipment and medium based on data analysis
CN113838580A (en) * 2021-09-03 2021-12-24 医渡云（北京）技术有限公司 Infectious disease infection scale prediction method, infectious disease infection scale prediction device, storage medium, and electronic device
CN113838580B (en) * 2021-09-03 2024-04-12 医渡云（北京）技术有限公司 Infectious disease infection scale prediction method and device, storage medium and electronic equipment
CN114388138A (en) * 2022-01-11 2022-04-22 平安科技（深圳）有限公司 Artificial intelligence based epidemic situation prediction method, device, equipment and storage medium
CN114388138B (en) * 2022-01-11 2024-05-10 平安科技（深圳）有限公司 Epidemic situation prediction method, device, equipment and storage medium based on artificial intelligence
CN114512245A (en) * 2022-02-17 2022-05-17 四川省疾病预防控制中心 New infectious disease model building method and computer device
CN115831388A (en) * 2023-02-17 2023-03-21 南京市疾病预防控制中心 Infectious disease simulation early warning method and system based on big data

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