CN113161005A - Target area epidemic situation deduction method and target area epidemic situation deduction simulator - Google Patents
Target area epidemic situation deduction method and target area epidemic situation deduction simulator Download PDFInfo
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Abstract
The application discloses an epidemic situation deduction method of a target area and an epidemic situation deduction simulator of the target area, wherein the method comprises the steps of determining a characteristic vector of epidemic situation data of an influence area; the characteristic vector comprises a plurality of characteristics influencing regional epidemic situation data, and the plurality of characteristics at least comprise any one or more of population flowing characteristics of the region, medical resource characteristics of the region, epidemic situation prevention and control characteristics of the region and parameter characteristics of infectious diseases; acquiring an infectious disease prediction model between the feature vector and the regional epidemic situation data; acquiring a vector value of the feature vector of a target area; and predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to an epidemic situation deduction method for a target area and an epidemic situation deduction simulator for a target area.
Background
In the analysis of the spread of infectious diseases, the spread situation of the diseases in the future is predicted according to the existing epidemic situation data, and effective basis and support can be provided for prevention and control.
In the related art, the future epidemic propagation of an area is predicted according to an SIR model or an SEIR model, however, the prediction premises of the two modes are that other random factors such as population flow of the predicted area are not considered, so that the epidemic propagation situation is difficult to accurately describe.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide a new technical solution for deducing epidemic situation of a target area.
According to a first aspect of the present disclosure, there is provided a method for deducing an epidemic situation in a target area, comprising:
determining a characteristic vector of epidemic situation data of an affected area; the characteristic vector comprises a plurality of characteristics influencing regional epidemic situation data, and the plurality of characteristics at least comprise any one or more of population flowing characteristics of the region, medical resource characteristics of the region, epidemic situation prevention and control characteristics of the region and parameter characteristics of infectious diseases;
acquiring an infectious disease prediction model between the feature vector and the regional epidemic situation data;
acquiring a vector value of the feature vector of a target area;
and predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value.
Optionally, the epidemic situation data at least comprises the number of daily accumulated cases, the number of daily accumulated death cases, the number of daily accumulated cured cases, the number of daily newly added death cases and the number of daily newly added cured cases of the region.
Optionally, the step of obtaining an infectious disease prediction model between the feature vector and the regional epidemic situation data includes:
collecting vector values of the feature vectors of different areas as training data;
collecting real epidemic situation data corresponding to the training data;
and training to obtain the infectious disease prediction model according to the training data and the corresponding real epidemic situation data.
Optionally, the training of the infectious disease prediction model according to the training data and the real epidemic situation data corresponding to the training data includes:
generating a training sample according to the training data and the corresponding real epidemic situation data;
an infectious disease prediction model is trained based on the training samples using at least one model training algorithm.
Optionally, the generating a training sample according to the training data and the real epidemic situation data corresponding to the training data includes:
and according to the automatic machine learning technology, after the training data is subjected to feature extraction and feature combination to obtain each target feature, generating a training sample by combining the real epidemic situation data corresponding to the training data.
Optionally, the plurality of features further comprises any one or more of administrative features of the area, demographic features of the area, and environmental features of the area.
Optionally, the population movement characteristics of the area comprise population inflow characteristics of the area and population outflow characteristics of the area, and comprise population inflow and outflow proportion characteristics of the area.
Optionally, the epidemic situation prevention and control characteristics of the region comprise an epidemic situation prevention and control coefficient of the region and an epidemic situation prevention and control means of the region,
and the epidemic situation prevention and control coefficient is inversely proportional to the epidemic situation prevention and control means.
Optionally, the medical resource characteristics of the region include a medical resource shortage level of the region and a medical resource hardware facility of the region.
Optionally, the infectious disease self-parameter characteristics comprise an infection rate of the infectious disease, a latency period of the infectious disease, a morbidity period of the infectious disease, a period during which a target patient is received, a period during which the infectious disease is confirmed, and a period during which the infectious disease is treated,
and the epidemic prevention and control coefficient is in direct proportion to the infection rate,
the period in which the target patient is received, the period in which the infectious disease is diagnosed, and the period in which the infectious disease is treated are inversely proportional to the medical resource shortage level.
Optionally, the method further comprises:
collecting vector values of the feature vectors of the target area as verification data;
acquiring predicted epidemic situation data of each verification data according to the infectious disease prediction model;
comparing the predicted epidemic situation data of each verification data with the corresponding real epidemic situation data to obtain the evaluation value of the infectious disease prediction model;
and under the condition that the evaluation value is less than or equal to a set evaluation threshold value, correcting the vector value of the characteristic vector, and retraining to obtain the infectious disease prediction model.
Optionally, the method further comprises:
and under the condition that the evaluation value is larger than the set evaluation threshold value, predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value.
Optionally, the obtaining a vector value of the feature vector of the target region includes:
providing a simulation system;
providing, by the simulation system, a configuration interface to configure vector values of feature vectors of the target region;
acquiring a vector value of a feature vector configured through the configuration interface; and the number of the first and second groups,
predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value, wherein the epidemic situation data comprises:
and calling the infectious disease prediction model through the simulation system to predict the vector value of the characteristic vector, and acquiring and displaying epidemic situation data of the target area.
Optionally, the method further comprises:
extracting the training data and each sensitive information in the corresponding real epidemic situation data according to an automatic machine learning technology;
and respectively encrypting the sensitive information to obtain ciphertext information corresponding to the sensitive information.
According to a second aspect of the present disclosure, there is also provided an epidemic situation deduction simulator of a target area, comprising:
the determining module is used for determining the characteristic vector of the epidemic situation data of the affected area; the characteristic vector comprises a plurality of characteristics influencing regional epidemic situation data, and the plurality of characteristics at least comprise any one or more of population flowing characteristics of the region, medical resource characteristics of the region, epidemic situation prevention and control characteristics of the region and parameter characteristics of infectious diseases;
the first acquisition module is used for acquiring an infectious disease prediction model between the feature vector and the regional epidemic situation data;
the second acquisition module is used for acquiring the vector value of the feature vector of the target area;
and the prediction module is used for predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value.
Optionally, the epidemic situation data at least comprises the number of daily accumulated cases, the number of daily accumulated death cases, the number of daily accumulated cured cases, the number of daily newly added death cases and the number of daily newly added cured cases of the region.
Optionally, the first obtaining module is specifically configured to:
collecting vector values of the feature vectors of different areas as training data;
collecting real epidemic situation data corresponding to the training data;
and training to obtain the infectious disease prediction model according to the training data and the corresponding real epidemic situation data.
Optionally, the first obtaining module is specifically configured to:
generating a training sample according to the training data and the corresponding real epidemic situation data;
an infectious disease prediction model is trained based on the training samples using at least one model training algorithm.
Optionally, the first obtaining module is specifically configured to:
and according to the automatic machine learning technology, after the training data is subjected to feature extraction and feature combination to obtain each target feature, generating a training sample by combining the real epidemic situation data corresponding to the training data.
Optionally, the plurality of features further comprises any one or more of administrative features of the area, demographic features of the area, and environmental features of the area.
Optionally, the population movement characteristics of the area comprise population inflow characteristics of the area and population outflow characteristics of the area, and comprise population inflow and outflow proportion characteristics of the area.
Optionally, the epidemic situation prevention and control characteristics of the region comprise an epidemic situation prevention and control coefficient of the region and an epidemic situation prevention and control means of the region,
and the epidemic situation prevention and control coefficient is inversely proportional to the epidemic situation prevention and control means.
Optionally, the medical resource characteristics of the region include a medical resource shortage level of the region and a medical resource hardware facility of the region.
Optionally, the infectious disease self-parameter characteristics comprise an infection rate of the infectious disease, a latency period of the infectious disease, a morbidity period of the infectious disease, a period during which a target patient is received, a period during which the infectious disease is confirmed, and a period during which the infectious disease is treated,
and the epidemic prevention and control coefficient is in direct proportion to the infection rate,
the period in which the target patient is received, the period in which the infectious disease is diagnosed, and the period in which the infectious disease is treated are inversely proportional to the medical resource shortage level.
Optionally, the first obtaining module is further configured to:
collecting vector values of the feature vectors of the target area as verification data;
acquiring predicted epidemic situation data of each verification data according to the infectious disease prediction model;
comparing the predicted epidemic situation data of each verification data with the corresponding real epidemic situation data to obtain the evaluation value of the infectious disease prediction model;
and under the condition that the evaluation value is less than or equal to a set evaluation threshold value, correcting the vector value of the characteristic vector, and retraining to obtain the infectious disease prediction model.
Optionally, the prediction module is further configured to:
and predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value under the condition that the evaluation value is larger than the set evaluation threshold value.
Optionally, the second obtaining module is specifically configured to:
providing a simulation system;
providing, by the simulation system, a configuration interface to configure vector values of feature vectors of the target region;
acquiring a vector value of a feature vector configured through the configuration interface; and the number of the first and second groups,
the prediction module is specifically configured to:
and calling the infectious disease prediction model through the simulation system to predict the vector value of the characteristic vector, and acquiring and displaying epidemic situation data of the target area.
Optionally, the first obtaining module is further configured to:
extracting the training data and each sensitive information in the corresponding real epidemic situation data according to an automatic machine learning technology;
and respectively encrypting the sensitive information to obtain ciphertext information corresponding to the sensitive information.
According to a third aspect of the present disclosure, there is also provided an electronic device comprising at least one computing device and at least one storage device, wherein the at least one storage device is configured to store instructions for controlling the at least one computing device to perform the method according to the above first aspect; alternatively, the apparatus implements the simulator according to the above second aspect through the computing means and the storage means.
According to a fourth aspect of the present disclosure, there is also provided a computer readable storage medium, wherein a computer program is stored thereon, which when executed by a processor, implements the method as described above in the first aspect.
The beneficial effect of the present disclosure lies in that, according to the method and the simulator of the embodiment of the present disclosure, the determined feature vector of the affected area epidemic situation data includes the population flow feature of the area, the medical resource feature of the area, the epidemic situation prevention and control feature of the area and the parameter feature of the infectious disease itself, so that the epidemic situation data of the target area can be predicted according to the vector value of the target area to the feature vector and the infectious disease prediction model between the trained feature vector and the epidemic situation data, that is, the features of the population flow, the medical resource, the epidemic situation prevention and control, and the like of the target area are fully considered, and the development trend of the actual and accurate predicted epidemic situation can be fitted.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a hardware architecture diagram of an electronic device according to an embodiment of the disclosure;
fig. 2 is a schematic flow chart diagram of a method for inferring an epidemic situation in a target area according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart diagram of a method for inferring an epidemic situation in a target area according to another embodiment of the present disclosure;
fig. 4 is a flowchart illustrating a method for deducing an epidemic situation in a target area according to a third embodiment of the present disclosure;
FIG. 5 is a schematic flow chart diagram of a method for inferring an epidemic situation in a target area according to an example of the present disclosure;
FIG. 6 is a functional block diagram of an epidemic situation deduction simulator for a target area according to an embodiment of the present disclosure;
FIG. 7 is a functional block diagram of an electronic device according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
< hardware configuration >
The method of the embodiments of the present disclosure may be implemented by at least one electronic device, i.e. the simulator 6000 for implementing the method may be arranged on the at least one electronic device. Fig. 1 shows a hardware structure of an arbitrary electronic device. The electronic device shown in fig. 1 may be a portable computer, a desktop computer, a workstation, a server, or the like, or may be any other device having a computing device such as a processor and a storage device such as a memory, and is not limited herein.
As shown in fig. 1, the electronic device 1000 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, and the like. Wherein the processor 1100 is adapted to execute computer programs. The computer program may be written in an instruction set of an architecture such as x86, Arm, RISC, MIPS, SSE, etc. The memory 1200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, a USB interface, a headphone interface, and the like. The communication device 1400 is capable of wired or wireless communication, for example, and may specifically include Wifi communication, bluetooth communication, 2G/3G/4G/5G communication, and the like. The display device 1500 is, for example, a liquid crystal display panel, a touch panel, or the like. The input device 1600 may include, for example, a touch screen, a keyboard, a somatosensory input, and the like. The electronic device 1000 may output voice information through the speaker 1700, and may collect voice information through the microphone 1800, and the like.
The electronic device shown in fig. 1 is merely illustrative and is in no way meant to limit the invention, its application, or uses. In an embodiment of the present disclosure, the memory 1200 of the electronic device 1000 is configured to store instructions for controlling the processor 1100 to operate so as to execute the epidemic situation deduction method of the target area according to the embodiment of the present disclosure. The skilled person can design the instructions according to the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
In one embodiment, an electronic device is provided that includes at least one computing device and at least one storage device for storing instructions for controlling the at least one computing device to perform a method according to any embodiment of the present disclosure.
The apparatus may include at least one electronic device 1000 as shown in fig. 1 to provide at least one computing device, such as a processor, and at least one storage device, such as a memory, without limitation.
< method examples >
Fig. 2 is a flowchart illustrating a method for deducing an epidemic situation of a target area according to an embodiment of the present disclosure, where the method is executed by an electronic device 1000, and as shown in fig. 2, the method may include the following steps S2100 to S2400:
step S2100, determining the characteristic vector of the epidemic situation data of the affected area.
The regional epidemic data at least comprises the number of daily accumulated cases, the number of daily accumulated death cases, the number of daily accumulated cured cases, the number of daily newly added death cases and the number of daily newly added cured cases of the region.
The region is a predetermined range, and may be a region defined by a minimum unit of epidemic situation data statistics. For example, if the city is used as the minimum unit to perform statistics of epidemic situation data, the area is a certain city; for another example, if the statistics of epidemic situation data is performed with the administrative area of a certain city as the minimum unit, the area is a certain administrative area of a certain city.
The feature vector X comprises a plurality of features X of the epidemic situation data of the affected areajJ takes a natural number from 1 to p, and p represents the total number of features of the feature vector X. The plurality of characteristics includes at least any one or more of population movement characteristics of the region, medical resource characteristics of the region, epidemic prevention and control characteristics of the region, and parameter characteristics of the infectious disease itself. The plurality of features may further include any one or more of administrative features of the area, demographic features of the area, and environmental features of the area.
The epidemic situation prevention and control characteristics of the above regions can include an epidemic situation prevention and control coefficient of the region and an epidemic situation prevention and control means of the region, and the epidemic situation prevention and control coefficient is inversely proportional to the epidemic situation prevention and control means, that is, the higher the epidemic situation prevention and control means is, the smaller the corresponding epidemic situation prevention and control coefficient is. For example, the epidemic situation prevention and control of a region belongs to a high degree of control, i.e., the traffic between the region and other regions is completely cut off, and the traffic in the region is greatly increased, at this time, the corresponding prevention and control coefficient may be 0.0001. For another example, the epidemic prevention and control of the area belongs to medium control, that is, the number of people migrating between the area and other areas becomes one tenth of the original number, and the control in the area is slightly enhanced, and at this time, the corresponding prevention and control coefficient may be 0.05. For another example, the epidemic situation prevention and control of the area belongs to no control, that is, the number of people migrating between the area and other areas is not changed, and there is no control in the area, and at this time, the corresponding prevention and control coefficient may be 1.
The population flowing characteristics of the above areas comprise the population flowing-in characteristics of the areas and the population flowing-out characteristics of the areas, and comprise the population flowing-in and flowing-out proportion characteristics of the areas.
The medical resource characteristics of the above regions include medical resource shortage levels of the regions and medical resource hardware facilities of the regions. For example, if the medical resources in the area are sufficient, such as the number of hospitals, beds, and doctors, the corresponding medical resource shortage level may be 0. For another example, if medical resources in the area are short, for example, hospitals, the number of beds, and the number of doctors are insufficient, the corresponding medical resource shortage level may be 1.
The above infectious disease self-parameter characteristics include an infection rate of the infectious disease, a latent period of the infectious disease, an attack period of the infectious disease, a period in which a target patient is received, a diagnosis period of the infectious disease, and a treatment period of the infectious disease. Wherein, the target disease is: among the infectious diseases recorded in the infectious disease system, the patient selected as the infectious disease corresponding to the infectious disease requiring the epidemic situation deduction, i.e., the target patient has a specific infectious disease category, for example, the target patient is ncov-19 infectious disease.
The epidemic prevention and control coefficient is in direct proportion to the infection rate of the infectious disease, namely, the larger the epidemic prevention and control coefficient is, the smaller the infection rate of the infectious disease in the area is.
For ncov-19 infectious disease, the target patient will have several states: normal, infected, accepted, confirmed and cured (or dead). The period of time the target patient is received, the period of time the infectious disease is diagnosed, and the period of time the infectious disease is treated are inversely proportional to the medical resource shortage level. For example, in the case where medical resources are sufficient, that is, the level of shortage of medical resources is lower, the target patient is ill 3 to 7 days after infection and is immediately received by the hospital, while a diagnosis is confirmed within 3 to 5 days, and 14 days of mild disease are cured, 30 days of severe disease are cured, and 3 to 5 days of severe disease may die.
The administrative characteristics of the above region may include the distance of the region from the affected area, the GDP of the region, the area of the region, etc.
The demographic characteristics of the above area may include the current general population of the area, population density of the area, gender ratio of the area, and demographics of various age groups of the area, among others.
The environmental characteristics of the above area may include the temperature, humidity, etc. of the area.
Exemplarily, the feature vector may have 7 features, i.e., p ═ 7 above, and in this case, the feature vector may be represented as X ═ (X)1,x2,x3,x4,x5,x6,x7) Wherein x is1,x2,x3,x4,x5,x6,x7The epidemic situation prevention and control coefficient, the epidemic situation prevention and control means, the medical resource hardware facilities, the medical resource shortage grade, the population inflow characteristic, the population outflow characteristic and the infection rate can be respectively set, wherein the epidemic situation prevention and control coefficient and the epidemic situation prevention and control means are the epidemic situation prevention and control characteristic, the medical resource hardware facilities and the medical resource shortage grade are the medical resource characteristic, the population inflow characteristic and the population outflow characteristic are the population inflow and outflow characteristic, and the infection rate is the parameter characteristic of the infectious disease. Of course, other features affecting regional epidemic data may also be included in the feature vector X.
After determining the characteristic vector of the epidemic situation data of the affected area, entering:
step S2200 is to obtain an infectious disease prediction model between the feature vector and the regional epidemic situation data.
The input of the infectious disease prediction model is a characteristic vector X, and the output is regional epidemic situation data determined by the characteristic vector X.
In this embodiment, the step S2200 of obtaining the infectious disease prediction model between the feature vector and the regional epidemic situation data may further include the following steps S2210 to S2230:
in step S2210, vector values of the feature vectors of different regions are collected as training data.
The more the number of training data, the more accurate the training result is, but after the training data reaches a certain number, the more slowly the accuracy of the training result increases until the orientation stabilizes. Here, the number of training data required for the determination of the accuracy of the training result and the data processing cost can be considered.
Step S2220, collecting real epidemic situation data corresponding to the training data.
The real epidemic situation data corresponding to the training data is a real label of the training data, and may be, for example, the real number of daily accumulated cases, the number of daily accumulated death cases, the number of daily accumulated cured cases, the number of daily newly added death cases, the number of daily newly added cured cases, and the like of the region.
And S2230, training to obtain an infectious disease prediction model according to the training data and the corresponding real epidemic situation data.
In this embodiment, the step S2230 of training the infectious disease prediction model according to the training data and the real epidemic situation data corresponding thereto may further include the following steps S2231 to S2232:
step S2231, generating training sample according to training data and corresponding real epidemic situation data
In step S2231, after feature extraction and feature combination are performed on the training data according to the automatic machine learning technique to obtain each target feature, a training sample may be generated by combining the real epidemic situation data corresponding to the training data.
At step S2232, an infectious disease prediction model is trained based on the training samples using at least one model training algorithm.
In this step S2232, the model training algorithm may be at least one of an LR (logistic regression) algorithm, a GBDT (gradient boosting decision tree) algorithm, and an HE-TreeNet (high-dimensional discrete embedded tree network and NN (neural network) algorithm, for example.
After an infectious disease prediction model between the feature vector and the regional epidemic situation data is obtained, the following steps are carried out:
in step S2300, a vector value of the feature vector of the target region is obtained.
In this embodiment, the step of obtaining the vector value of the feature vector of the target region in step S2300 may further include the following steps S2310 to S2320:
in step S2310, a simulation system is provided.
Step S2320, a configuration interface for configuring vector values of the feature vectors of the target area is provided through the simulation system.
The configuration interface may be any entry capable of allowing a user to input information, such as an input box, a drop-down list, a voice input entry, and the like, and is not limited herein.
Exemplarily, the feature vector may have 7 features, i.e., p ═ 7 above, and in this case, the feature vector may be represented as X ═ (X)1,x2,x3,x4,x5,x6,x7) Wherein x is1,x2,x3,x4,x5,x6,x7The epidemic situation prevention and control coefficient, the epidemic situation prevention and control means, the medical resource hardware facilities, the medical resource shortage grade, the population inflow characteristic, the population outflow characteristic and the infection rate can be respectively set, wherein the epidemic situation prevention and control coefficient and the epidemic situation prevention and control means are the epidemic situation prevention and control characteristic, the medical resource hardware facilities and the medical resource shortage grade are the medical resource characteristic, the population inflow characteristic and the population outflow characteristic are the population inflow and outflow characteristic, and the infection rate is the parameter characteristic of the infectious disease. Vector values of the epidemic situation prevention and control coefficient, the epidemic situation prevention and control means, the medical resource hardware facilities, the medical resource shortage level, the population inflow characteristic, the population outflow characteristic and the infection rate can be respectively A, B, C, D, E, F and G, wherein A, B, C, D, E, F and G are only exemplary.
After the vector value of the feature vector of the target area is obtained, the following steps are entered:
and S2400, predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value.
The target area is an area needing epidemic situation data prediction, after the epidemic situation data of the target area is predicted, any one or more of characteristics influencing the regional epidemic situation data, such as an epidemic situation prevention and control means, an epidemic situation prevention and control coefficient, medical resource hardware facilities and the like of the target area are adjusted according to the predicted epidemic situation data of the target area.
In this embodiment, in step S2400, the predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value may further include: and calling an infectious disease prediction model through a simulation system to predict the vector value of the characteristic vector, and acquiring and displaying epidemic situation data of the target area.
Specifically, the simulation system inputs the vector value of the target region relative to the feature vector into the infectious disease prediction model, so as to predict the epidemic situation data of the target region by using the infectious disease prediction model, for example, the number of daily accumulated cases, the number of daily accumulated death cases, the number of daily accumulated cured cases, the number of daily newly added death cases, and the number of daily newly added cured cases of the target region can be predicted.
According to the method disclosed by the embodiment of the disclosure, the determined characteristic vector of the epidemic situation data of the affected area comprises population flow characteristics of the area, medical resource characteristics of the area, epidemic situation prevention and control characteristics of the area and parameter characteristics of the infectious disease, so that the epidemic situation data of the target area can be predicted according to the vector value of the target area relative to the characteristic vector and the trained infectious disease prediction model between the characteristic vector and the epidemic situation data, namely, the characteristics of population flow, medical resources, epidemic situation prevention and control and the like of the target area are fully considered, and the development trend of the actual and accurate epidemic situation prediction can be fitted.
In one embodiment, after obtaining the infectious disease prediction model between the feature vector and the epidemic situation data, the epidemic situation deduction method for the target area of the present disclosure may further include the following steps S3100 to S3400:
in step S3100, the vector values of the feature vectors of the target region are collected as verification data.
In step S3100, a small amount of data may be selected to perform artificial tagging on epidemic situation data, and the tagged data may be used as real epidemic situation data of verification data.
Step S3200, according to the infectious disease prediction model, obtaining prediction epidemic situation data of each verification data.
In step S3200, epidemic situation data corresponding to each verification data may be obtained as predicted epidemic situation data according to the infectious disease prediction model, so as to compare the predicted epidemic situation data of each verification data with the real epidemic situation data of each verification data, and further obtain the evaluation value of the infectious disease model.
And step S3300, comparing the predicted epidemic situation data of each verification data with the corresponding real epidemic situation data to obtain the evaluation value of the infectious disease prediction model.
The evaluation value of the infectious disease prediction model is used for judging the quality of the infectious disease prediction model. The above evaluation values include at least one of Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R2, AUC, KS, Recall, Precision, Accuracy, f1, and Logloss.
And step S3400, under the condition that the judgment value is less than or equal to the set judgment threshold value, correcting the vector value of the characteristic vector, and retraining to obtain the infectious disease prediction model.
In step S3400, a judgment threshold may be set to judge whether the infectious disease prediction model is valid according to the judgment threshold. For example, the infectious disease prediction model may be determined to be valid when the evaluation value exceeds the evaluation threshold value, or the infectious disease prediction model may be determined to be invalid when the evaluation value does not exceed the evaluation threshold value.
In the case that the infectious disease prediction model is judged to be invalid according to the judgment value, the infectious disease prediction model can be obtained by retraining through adjusting any one or more of the characteristics of the epidemic situation data of the affected area, such as the epidemic situation prevention and control means, the epidemic situation prevention and control coefficient, the medical resource hardware facility and the like of the target area, so that the predicted epidemic situation data are more and more accurate.
And step S3500, when the evaluation value is larger than the set evaluation threshold value, executing the step of predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value.
After the infectious disease prediction model is obtained, verification data are selected, the predicted epidemic situation of each verification data is compared with the real epidemic situation data of the corresponding vehicle to obtain an evaluation value, and then the step of predicting the epidemic situation data of the target area according to the infectious disease prediction model and the vector value is executed under the condition that the infectious disease prediction model is judged to be effective according to the evaluation value, so that the accuracy of the predicted epidemic situation data can be ensured.
In one embodiment, the epidemic situation deduction method for the target area of the present disclosure may further include the following steps S4100 to S4200:
step S4100, extracting the training data and each sensitive information in the corresponding real epidemic situation data according to the automatic machine learning technology.
The sensitive information may be predefined information that needs to be kept secret.
Step S4200, encrypting each sensitive information respectively to obtain ciphertext information corresponding to each sensitive information.
According to the method disclosed by the embodiment of the disclosure, sensitive information can be desensitized, and the safety of information transfer is ensured.
< example >
Next, another example of the epidemic situation deduction method for the target area is shown, in this example, in combination with fig. 5, the epidemic situation deduction method for the target area may include the following steps:
and step S5010, determining the characteristic vector of the epidemic situation data of the affected area.
The feature vector at least comprises population flow features of the region, medical resource features of the region, epidemic situation prevention and control features of the region and parameter features of the infectious disease.
Step S5020, vector values of the feature vectors of different areas are collected to serve as training data.
Step S5030, collecting real epidemic situation data corresponding to the training data.
Step S5040, after each target feature is obtained by feature extraction and feature combination of training data according to the automatic machine learning technology, a training sample is generated by combining real epidemic situation data corresponding to the training data.
Step S5050, an infectious disease prediction model is trained based on the training samples using at least one model training algorithm.
In step S5060, vector values of the feature vectors of the target region are obtained.
When the feature vector at least comprises the population flow feature of the region, the medical resource feature of the region, the epidemic situation prevention and control feature of the region and the infectious disease self-parameter feature, the vector value of the feature vector at least comprises the vector value of the population flow feature of the region, the vector value of the medical resource feature of the region, the vector value of the epidemic situation prevention and control feature of the region and the vector value of the infectious disease self-parameter feature.
Step S5070, predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value.
In step S5070, the number of cumulative cases per day, cumulative number of dead cases per day, cumulative number of cured cases per day, number of newly added dead cases per day, and number of newly added cured cases per day can be predicted for the ncov-19 infectious disease region in the target region.
< simulator embodiment >
In this embodiment, an epidemic situation deduction simulator 6000 of a target area is further provided, as shown in fig. 6, the epidemic situation deduction simulator 6000 of a target area includes a determining module 6100, a first obtaining module 6200, a second obtaining module 6300, and a predicting module 6400, and is configured to implement the epidemic situation deduction method of a target area provided in this embodiment, each module of the epidemic situation deduction simulator 6000 of a target area may be implemented by software, or by hardware, which is not limited herein.
A determining module 6100, configured to determine a feature vector of the epidemic situation data in the affected area; wherein, the feature vector includes a plurality of characteristics of influence regional epidemic situation data, a plurality of characteristics include at least that the regional population of the characteristic of floating, regional medical resources characteristic, regional epidemic situation prevention and control characteristic and infectious disease self parameter characteristic in any one or more.
A first obtaining module 6200, configured to obtain an infectious disease prediction model between the feature vector and the regional epidemic situation data.
A second obtaining module 6300, configured to obtain a vector value of the feature vector of the target region.
And the prediction module 6400 is used for predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value. In one embodiment, the feature vector includes at least one of a first type of environmental feature, a second type of environmental feature, a consumption subject feature, and a content attribute feature.
In one embodiment, the epidemic data includes at least the cumulative number of cases per day, cumulative number of cases died per day, cumulative number of cases cured per day, number of newly added cases per day, and number of newly added cases per day for the region.
In one embodiment, the first obtaining module 6200 is specifically configured to: collecting vector values of the feature vectors of different areas as training data; collecting real epidemic situation data corresponding to the training data; and training to obtain the infectious disease prediction model according to the training data and the corresponding real epidemic situation data.
In one embodiment, the first obtaining module 6200 is specifically configured to: generating a training sample according to the training data and the corresponding real epidemic situation data; an infectious disease prediction model is trained based on the training samples using at least one model training algorithm.
In one embodiment, the first obtaining module 6200 is specifically configured to: and according to the automatic machine learning technology, after the training data is subjected to feature extraction and feature combination to obtain each target feature, generating a training sample by combining the real epidemic situation data corresponding to the training data.
In one embodiment, the plurality of features further comprises any one or more of administrative features of the area, demographic features of the area, and environmental features of the area.
In one embodiment, the population movement characteristics of the area comprise inflow characteristics of the area and outflow characteristics of the area, and comprise inflow and outflow proportion characteristics of the area.
In one embodiment, the epidemic prevention and control characteristics of the region comprise an epidemic prevention and control coefficient of the region and an epidemic prevention and control means of the region,
and the epidemic situation prevention and control coefficient is inversely proportional to the epidemic situation prevention and control means.
In one embodiment, the medical resource characteristics of the region include a medical resource shortage level of the region and a medical resource hardware facility of the region.
In one embodiment, the infectious disease self-parameter characteristics include an infection rate of the infectious disease, a latency period of the infectious disease, a morbidity period of the infectious disease, a period in which a target patient is received, a period in which the infectious disease is diagnosed, and a period in which the infectious disease is treated,
and the epidemic prevention and control coefficient is in direct proportion to the infection rate,
the period in which the target patient is received, the period in which the infectious disease is diagnosed, and the period in which the infectious disease is treated are inversely proportional to the medical resource shortage level.
In one embodiment, the first obtaining module 6200 is further configured to: collecting vector values of the feature vectors of the target area as verification data; acquiring predicted epidemic situation data of each verification data according to the infectious disease prediction model; comparing the predicted epidemic situation data of each verification data with the corresponding real epidemic situation data to obtain the evaluation value of the infectious disease prediction model; and under the condition that the evaluation value is less than or equal to a set evaluation threshold value, correcting the vector value of the characteristic vector, and retraining to obtain the infectious disease prediction model.
In one embodiment, the prediction module 6400, is further to: and predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value under the condition that the evaluation value is larger than the set evaluation threshold value.
In an embodiment, the second obtaining module 6300 is specifically configured to: providing a simulation system; providing, by the simulation system, a configuration interface to configure vector values of feature vectors of the target region; and acquiring the vector value of the feature vector configured through the configuration interface.
The prediction module 6400 is specifically configured to: and calling the infectious disease prediction model through the simulation system to predict the vector value of the characteristic vector, and acquiring and displaying epidemic situation data of the target area.
In one embodiment, the first obtaining module 6100 is further configured to: extracting the training data and each sensitive information in the corresponding real epidemic situation data according to an automatic machine learning technology; and respectively encrypting the sensitive information to obtain ciphertext information corresponding to the sensitive information.
< apparatus embodiment >
Corresponding to the above method embodiments, in this embodiment, an electronic device is further provided, as shown in fig. 7, which may include an epidemic situation deduction simulator 6000 of a target area according to any embodiment of the present disclosure, for implementing the epidemic situation deduction method of the target area according to any embodiment of the present disclosure.
As shown in fig. 8, the electronic device 7000 may further comprise a processor 7200 and a memory 7100, the memory 7100 for storing executable instructions; the processor 7200 is configured to operate the electronic device according to the control of the instructions to perform a method for inferring an epidemic situation of a target area according to any embodiment of the present disclosure.
The various modules of simulator 6000 above may be implemented by processor 7200 executing the instructions to perform a method according to any embodiment of the present disclosure.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (10)
1. An epidemic situation deduction method for a target area comprises the following steps:
determining a characteristic vector of epidemic situation data of an affected area; the characteristic vector comprises a plurality of characteristics influencing regional epidemic situation data, and the plurality of characteristics at least comprise any one or more of population flowing characteristics of the region, medical resource characteristics of the region, epidemic situation prevention and control characteristics of the region and parameter characteristics of infectious diseases;
acquiring an infectious disease prediction model between the feature vector and the regional epidemic situation data;
acquiring a vector value of the feature vector of a target area;
and predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value.
2. The method of claim 1, wherein the epidemic data comprises at least a cumulative number of cases per day, a cumulative number of cases of death per day, a cumulative number of cases of cure per day, a number of cases of new cases per day, a number of cases of new death per day, and a number of cases of new cure per day for the region.
3. The method of claim 1, wherein the step of obtaining a predictive model of infectious disease between the feature vectors and regional epidemic data comprises:
collecting vector values of the feature vectors of different areas as training data;
collecting real epidemic situation data corresponding to the training data;
and training to obtain the infectious disease prediction model according to the training data and the corresponding real epidemic situation data.
4. The method of claim 3, wherein the training the infectious disease prediction model according to the training data and the corresponding real epidemic data comprises:
generating a training sample according to the training data and the corresponding real epidemic situation data;
an infectious disease prediction model is trained based on the training samples using at least one model training algorithm.
5. The method of claim 4, wherein the generating training samples from the training data and its corresponding real epidemic data comprises:
and according to the automatic machine learning technology, after the training data is subjected to feature extraction and feature combination to obtain each target feature, generating a training sample by combining the real epidemic situation data corresponding to the training data.
6. The method of claim 1, wherein the plurality of characteristics further comprises any one or more of administrative characteristics of the area, demographic characteristics of the area, and environmental characteristics of the area.
7. The method of claim 1, wherein the demographic flow characteristics of the area include an influx of the area and an efflux of the area, and include an influx of the area and an efflux ratio of the area.
8. An epidemic situation deduction simulator of a target area, comprising:
the determining module is used for determining the characteristic vector of the epidemic situation data of the affected area; the characteristic vector comprises a plurality of characteristics influencing regional epidemic situation data, and the plurality of characteristics at least comprise any one or more of population flowing characteristics of the region, medical resource characteristics of the region, epidemic situation prevention and control characteristics of the region and parameter characteristics of infectious diseases;
the first acquisition module is used for acquiring an infectious disease prediction model between the feature vector and the regional epidemic situation data;
the second acquisition module is used for acquiring the vector value of the feature vector of the target area;
and the prediction module is used for predicting epidemic situation data of the target area according to the infectious disease prediction model and the vector value.
9. An electronic device comprising at least one computing device and at least one storage device, wherein the at least one storage device is to store instructions for controlling the at least one computing device to perform the method of any of claims 1 to 7; alternatively, the apparatus implements the simulator according to claim 8 through the computing means and the storage means.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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