CN112802603A - Method and device for predicting influenza degree - Google Patents
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Abstract
The invention discloses a method and a device for predicting influenza degree. Wherein, the method comprises the following steps: acquiring at least two candidate sequences, wherein the at least two candidate sequences are sequences formed according to various influenza data and time sequences; calculating correlation according to at least two candidate sequences and a preset influenza prevalence index to obtain corresponding candidate sequence values; screening candidate sequence values corresponding to at least two candidate sequences to obtain screened candidate sequence values; and predicting according to the screened candidate sequence value to obtain a target sequence, wherein the target sequence is used for representing the influenza epidemics. The invention solves the technical problem that the prior art cannot effectively predict the prevalence situation of the future flu.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for predicting flu degree.
Background
In the method for preventing influenza epidemics in cities, the influenza epidemics are usually reported by depending on traditional places and are determined according to generated cases;
in the related art, in the calculation process using the model, a gaussian model, a neural network, ARIMA and xgboost, an autoregressive feature (AR model) and a LASSO model, or a time series method and a polynomial regression method are generally considered.
The above related technologies assume that influenza case data is available, but only ILI (which is a recognized influenza prevalence index) of the whole body of each of north and south is available, and the ILI cannot be specified to cities.
In view of the above-mentioned problem that the prevalence of future flu cannot be effectively predicted by the related art, no effective solution has been proposed so far.
Disclosure of Invention
The embodiment of the invention provides a method and a device for predicting flu degree, which at least solve the technical problem that the prevalence situation of future flu cannot be effectively predicted by related technologies.
According to an aspect of an embodiment of the present invention, there is provided a method of predicting a degree of influenza, including: acquiring at least two candidate sequences, wherein the at least two candidate sequences are sequences formed according to various influenza data and time sequences; calculating correlation according to at least two candidate sequences and a preset influenza prevalence index to obtain corresponding candidate sequence values; screening candidate sequence values corresponding to at least two candidate sequences to obtain screened candidate sequence values; and predicting according to the screened candidate sequence value to obtain a target sequence, wherein the target sequence is used for representing the influenza epidemics.
Optionally, before obtaining at least two candidate sequences, the method further includes: acquiring preset influenza prevalence indexes and local influenza data, wherein the local influenza data comprise: drug sales data and data for searching for flu information.
Optionally, the obtaining at least two candidate sequences includes: screening data of which the integrity of various types of influenza data in the various types of local influenza data is greater than or equal to a first preset threshold value; and generating at least two candidate sequences according to the time sequence and the screened influenza data.
Further, optionally, screening candidate sequence values corresponding to at least two candidate sequences, and obtaining the screened candidate sequence values includes: and calculating the proportion that the candidate sequence values corresponding to the at least two candidate sequences are larger than a second preset threshold value in the influenza outbreak period according to the local influenza data, and screening the candidate sequence values corresponding to the at least two candidate sequences according to the proportion to obtain the screened candidate sequence values.
Optionally, the method further includes: predicting according to the target sequence to obtain a prediction result; and correcting the prediction result according to the updated various influenza data to obtain the corrected prediction result.
According to another aspect of the embodiments of the present invention, there is also provided an apparatus for predicting a degree of influenza, including: the acquisition module is used for acquiring at least two candidate sequences, wherein the at least two candidate sequences are sequences formed according to various influenza data and time sequences; the calculation module is used for calculating correlation according to the at least two candidate sequences and a preset influenza prevalence index to obtain corresponding candidate sequence values; the screening module is used for screening candidate sequence values corresponding to at least two candidate sequences to obtain screened candidate sequence values; and the prediction module is used for predicting according to the screened candidate sequence value to obtain a target sequence, wherein the target sequence is used for expressing the influenza prevalence condition.
Optionally, the apparatus further comprises: before obtaining at least two candidate sequences, obtaining a preset influenza prevalence index and local influenza data, wherein the local influenza data comprise: drug sales data and data for searching for flu information.
Further, optionally, the obtaining module includes: the data screening unit is used for screening data of which the integrity of various types of influenza data in the various types of local influenza data is greater than or equal to a first preset threshold value; and the acquisition unit is used for generating at least two candidate sequences according to the time sequence and the screened various influenza data.
Optionally, the screening module includes: and the screening unit is used for calculating the proportion that the candidate sequence values corresponding to the at least two candidate sequences are larger than a second preset threshold value in the influenza outbreak period according to the local influenza data, and screening the candidate sequence values corresponding to the at least two candidate sequences according to the proportion to obtain the screened candidate sequence values.
Optionally, the apparatus further comprises: the sequence prediction module is used for predicting according to the target sequence to obtain a prediction result; and the correction module is used for correcting the prediction result according to the updated various influenza data to obtain the corrected prediction result.
In the embodiment of the invention, at least two candidate sequences are obtained, wherein at least one candidate sequence is a sequence formed according to various influenza data and time sequences; calculating correlation according to at least two candidate sequences and a preset influenza prevalence index to obtain corresponding candidate sequence values; screening candidate sequence values corresponding to at least two candidate sequences to obtain screened candidate sequence values; and predicting according to the screened candidate sequence value to obtain a target sequence, wherein the target sequence is used for representing the influenza prevalence situation, so that the aim of predicting the influenza prevalence trend of each place according to various influenza data is fulfilled, the technical effect of effectively predicting the prevalence situation of future influenza is realized, and the technical problem that the prevalence situation of the future influenza cannot be effectively predicted due to the related technology is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a schematic flow diagram of a method of predicting influenza severity according to an embodiment of the invention;
fig. 2 is a schematic diagram of an apparatus for predicting a degree of flu according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided a method embodiment of a method of predicting a degree of flu, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a schematic flow chart of a method for predicting the degree of influenza according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S102, at least two candidate sequences are obtained, wherein the at least two candidate sequences are sequences formed according to various influenza data and time sequences;
before obtaining at least two candidate sequences in step S102, the method for predicting the influenza degree provided in the embodiment of the present application further includes: acquiring preset influenza prevalence indexes and local influenza data, wherein the local influenza data comprise: drug sales data and data for searching for flu information.
The predetermined influenza prevalence in the examples of the present application is referred to as ILI%, wherein ILI% in the examples of the present application includes ILI% in the south and ILI% in the north.
The data for searching the influenza information can be all operation data about the influenza searched by each IP of each city when a search engine is used on the premise of obtaining pre-authorization.
The drug sales data may include: on-line drug sales data and/or daily/monthly/quarterly/annual drug runs for each pharmacy (obtained on the premise that pharmacy drug runs are obtained as pre-authorization).
Specifically, the step S102 of acquiring at least two candidate sequences includes: screening data of which the integrity of various types of influenza data in the various types of local influenza data is greater than or equal to a first preset threshold value; and generating at least two candidate sequences according to the time sequence and the screened influenza data.
Step S104, calculating correlation according to at least two candidate sequences and a preset influenza prevalence index to obtain corresponding candidate sequence values;
step S106, screening candidate sequence values corresponding to at least two candidate sequences to obtain screened candidate sequence values;
further, optionally, the screening candidate sequence values corresponding to at least two candidate sequences in step S106 to obtain screened candidate sequence values includes: and calculating the proportion that the candidate sequence values corresponding to the at least two candidate sequences are larger than a second preset threshold value in the influenza outbreak period according to the local influenza data, and screening the candidate sequence values corresponding to the at least two candidate sequences according to the proportion to obtain the screened candidate sequence values.
And S108, predicting according to the screened candidate sequence value to obtain a target sequence, wherein the target sequence is used for expressing the influenza prevalence condition.
Optionally, the method for predicting the influenza degree provided in the embodiment of the present application further includes: predicting according to the target sequence to obtain a prediction result; and correcting the prediction result according to the updated various influenza data to obtain the corrected prediction result.
In summary, with reference to steps S102 to S108, the method for measuring the flow sensing degree provided in the embodiment of the present application specifically includes:
(1) receiving south-north ILI% (preset influenza prevalence indexes in the embodiment of the application) and outbreak news of influenza in various places as a basis for judging the prediction accuracy;
(2) receiving various related data of flu, such as weather data, medicine sales data and search data;
(3) screening (2) time sequences of data with data integrity higher than a threshold (i.e., a first preset threshold in the embodiment of the present application) as candidate sequences (i.e., at least two candidate sequences in the embodiment of the present application);
(4) filling the missing part of the candidate sequence; (the filling method can be that the numerical numbers at the two ends of the missing interval are taken for linear filling);
(5) calculating the correlation between each candidate sequence and ILI% in (4) (south city calculated and south ILI% and north city calculated and north ILI%), and screening candidate sequence values with the correlation higher than a threshold value (namely, calculating the correlation according to at least two candidate sequences and a preset influenza prevalence index in the embodiment of the application to obtain corresponding candidate sequence values);
(6) after the screening of (5), for each of (1) the influenza news, calculating a proportion (the number of times that the candidate sequence value is higher than the threshold at the time point indicated by the news divided by the number of the news) of the candidate sequence values higher than the threshold (i.e., a second preset threshold in the embodiment of the present application) in the outbreak period of the influenza mentioned by the news, and screening the candidate sequence values with the proportion higher than the threshold;
(7) after the screening in (6), predicting the candidate sequence by using an NNETAR prediction model, and taking the sequence with the minimum MAE as a sequence (namely, a target sequence in the embodiment of the application) for representing the influenza epidemics, wherein the sequence is called an influenza index;
the NNETAR model in the embodiment of the application is a feedforward neural network model and is composed of three layers:
the first layer is an input layer and receives an original value of the influenza index sequence;
the second layer is an intermediate layer;
the third layer, the output layer, contains 1 neuron and is the predicted influenza index.
(8) And after establishing and predicting the influenza index, continuously tracking the influenza outbreak news, and if the influenza index is lower during the outbreak, manually correcting the index.
Specifically, after obtaining an actual value (i.e., an actual influenza index in the embodiment of the present application) by using the candidate sequence, the user may correct the actual value based on the actual influenza condition experienced by the user, where the correction method may be directly adjusting the numerical value (i.e., the influenza index in the embodiment of the present application) or giving feedback that the influenza index is too high or too low; after receiving the feedback, a machine learning method can be used for inputting historical actual values, actual values before adjustment in the current period and other characteristics (date, weather and the like) in the current period, the actual values after adjustment by the user are used as fitting objects to carry out regression, and a regression model is trained to correct the actual values in the future so as to enable the actual values to be in line with the real flu situation perceived by the user.
It should be noted that, in the method for predicting the influenza degree provided in the embodiment of the present application, only the NNETAR model is taken as an example for description as a preferred example, and the method for predicting the influenza degree provided in the embodiment of the present application is implemented without specific limitation.
In the embodiment of the invention, at least two candidate sequences are obtained, wherein at least one candidate sequence is a sequence formed according to various influenza data and time sequences; calculating correlation according to at least two candidate sequences and a preset influenza prevalence index to obtain corresponding candidate sequence values; screening candidate sequence values corresponding to at least two candidate sequences to obtain screened candidate sequence values; and predicting according to the screened candidate sequence value to obtain a target sequence, wherein the target sequence is used for representing the influenza prevalence situation, so that the aim of predicting the influenza prevalence trend of each place according to various influenza data is fulfilled, the technical effect of effectively predicting the prevalence situation of future influenza is realized, and the technical problem that the prevalence situation of the future influenza cannot be effectively predicted due to the related technology is solved.
Example 2
According to another aspect of the embodiments of the present invention, there is also provided an apparatus for predicting a degree of influenza, and fig. 2 is a schematic view of the apparatus for predicting a degree of influenza according to the embodiments of the present invention, as shown in fig. 2, including: an obtaining module 22, configured to obtain at least two candidate sequences, where the at least two candidate sequences are sequences formed according to various types of influenza data and time sequences; a calculating module 24, configured to calculate correlations according to at least two candidate sequences and a preset influenza prevalence index, so as to obtain corresponding candidate sequence values; a screening module 26, configured to screen candidate sequence values corresponding to at least two candidate sequences to obtain screened candidate sequence values; and the predicting module 28 is configured to predict according to the screened candidate sequence value to obtain a target sequence, where the target sequence is used to indicate an influenza prevalence situation.
Optionally, the apparatus for predicting a degree of influenza provided in the embodiment of the present application further includes: before obtaining at least two candidate sequences, obtaining a preset influenza prevalence index and local influenza data, wherein the local influenza data comprise: drug sales data and data for searching for flu information.
Further, optionally, the obtaining module 22 includes: the data screening unit is used for screening data of which the integrity of various types of influenza data in the various types of local influenza data is greater than or equal to a first preset threshold value; and the acquisition unit is used for generating at least two candidate sequences according to the time sequence and the screened various influenza data.
Optionally, the screening module 26 includes: and the screening unit is used for calculating the proportion that the candidate sequence values corresponding to the at least two candidate sequences are larger than a second preset threshold value in the influenza outbreak period according to the local influenza data, and screening the candidate sequence values corresponding to the at least two candidate sequences according to the proportion to obtain the screened candidate sequence values.
Optionally, the apparatus for predicting a degree of influenza provided in the embodiment of the present application further includes: the sequence prediction module is used for predicting according to the target sequence to obtain a prediction result; and the correction module is used for correcting the prediction result according to the updated various influenza data to obtain the corrected prediction result.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method of predicting the extent of influenza, comprising:
acquiring at least two candidate sequences, wherein the at least two candidate sequences are sequences formed according to various influenza data and time sequences;
calculating correlation according to the at least two candidate sequences and a preset influenza prevalence index to obtain corresponding candidate sequence values;
screening candidate sequence values corresponding to the at least two candidate sequences to obtain screened candidate sequence values;
and predicting according to the screened candidate sequence value to obtain a target sequence, wherein the target sequence is used for representing the influenza epidemics.
2. The method of claim 1, wherein prior to said obtaining at least two candidate sequences, the method further comprises:
acquiring the preset influenza prevalence index and local influenza data, wherein the local influenza data comprises: drug sales data and data for searching for flu information.
3. The method of claim 1 or 2, wherein the obtaining at least two candidate sequences comprises:
screening data of which the integrity of various types of influenza data in the various types of local influenza data is greater than or equal to a first preset threshold value;
and generating the at least two candidate sequences according to the time sequence and the screened various influenza data.
4. The method of claim 3, wherein the screening the candidate sequence values corresponding to the at least two candidate sequences to obtain the screened candidate sequence values comprises:
calculating the proportion that the candidate sequence values corresponding to the at least two candidate sequences are larger than a second preset threshold value in the influenza outbreak period according to the local influenza data, and screening the candidate sequence values corresponding to the at least two candidate sequences according to the proportion to obtain the screened candidate sequence values.
5. The method of claim 1, further comprising:
predicting according to the target sequence to obtain a prediction result;
and correcting the prediction result according to the updated various influenza data to obtain a corrected prediction result.
6. An apparatus for predicting a degree of influenza, comprising:
the acquisition module is used for acquiring at least two candidate sequences, wherein the at least two candidate sequences are sequences formed according to various influenza data and time sequences;
the calculation module is used for calculating correlation according to the at least two candidate sequences and a preset influenza prevalence index to obtain corresponding candidate sequence values;
the screening module is used for screening candidate sequence values corresponding to the at least two candidate sequences to obtain screened candidate sequence values;
and the prediction module is used for predicting according to the screened candidate sequence value to obtain a target sequence, wherein the target sequence is used for representing the influenza prevalence condition.
7. The apparatus of claim 6, further comprising:
before the obtaining of the at least two candidate sequences, obtaining the preset influenza prevalence index and local influenza data, wherein the local influenza data includes: drug sales data and data for searching for flu information.
8. The apparatus of claim 6 or 7, wherein the obtaining module comprises:
the data screening unit is used for screening data of which the integrity of various types of influenza data in the various types of local influenza data is greater than or equal to a first preset threshold value;
and the acquisition unit is used for generating the at least two candidate sequences according to the time sequence and the screened various influenza data.
9. The apparatus of claim 8, wherein the screening module comprises:
and the screening unit is used for calculating the proportion that the candidate sequence values corresponding to the at least two candidate sequences are larger than a second preset threshold value in the influenza outbreak period according to the local influenza data, and screening the candidate sequence values corresponding to the at least two candidate sequences according to the proportion to obtain the screened candidate sequence values.
10. The apparatus of claim 6, further comprising:
the sequence prediction module is used for predicting according to the target sequence to obtain a prediction result;
and the correction module is used for correcting the prediction result according to the updated various influenza data to obtain a corrected prediction result.
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