CN112071437A - Infectious disease trend prediction method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses an infectious disease trend prediction method, an infectious disease trend prediction device, electronic equipment and a storage medium, and relates to the field of epidemic situation trend prediction. The implementation scheme is as follows: acquiring the number of isolated personnel in each historical period in N historical periods before the current period; inputting the number of the isolated personnel in each historical period into a trained trend prediction model; acquiring the number of isolated persons of a trend prediction model in each future period based on the number of isolated persons in each historical period and the number of isolated persons in each future period in M future periods after the current period output by the pre-acquired prior knowledge; and predicting epidemic situation trends of the infectious diseases in the M future periods based on the number of the isolated persons in each future period. The embodiment of the application can improve the accuracy of disease prediction, can provide basic indexes of disease transmission, has strong interpretability, and solves the problem that the generalization capability is difficult to guarantee.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to epidemic situation trend prediction technology, and specifically relates to an infectious disease trend prediction method, an infectious disease trend prediction device, electronic equipment and a storage medium.
Background
The effective prevention and treatment of infectious diseases are common challenges facing all human beings, the spreading tendency and speed of the infectious diseases are accurately predicted through big data, particularly the time-space correlation characteristics of the data, the method is greatly beneficial to controlling the infectious diseases in the human society and guaranteeing the public health safety of the society. The big data technology is hopeful to be applied to assist the transmission prediction and control of infectious diseases so as to construct an infectious disease prediction model and enhance the cognition and control capability of human beings on the infectious diseases.
It is generally desirable to be able to predict future trends in the early stages of an infection, so that government authorities can be helped to take precautions as early as possible, and therefore, the available sample volumes are generally extremely small.
Disclosure of Invention
The disclosure provides a method, an apparatus, an electronic device and a storage medium for infectious disease trend prediction.
In a first aspect, an embodiment of the present application provides an infectious disease trend prediction method, including:
acquiring the number of isolated personnel in each historical period in N historical periods before the current period; wherein the number of isolated persons comprises: the number of deceased persons and the number of recovery persons;
inputting the number of the isolated personnel in each historical period into a trained trend prediction model; acquiring the number of isolated persons of each future period in M future periods after the current period, which are output by the trend prediction model based on the number of isolated persons of each historical period and pre-acquired prior knowledge;
predicting epidemic situation trends of the infectious diseases in the M future periods based on the number of the isolated persons in each future period; wherein N and M are both natural numbers greater than or equal to 1.
In a second aspect, an embodiment of the present application provides an infectious disease trend prediction apparatus, including: the device comprises an acquisition module, an input/output module and a prediction module; wherein,
the acquisition module is used for acquiring the number of isolated personnel in each historical period in N historical periods before the current period; wherein the number of isolated persons comprises: the number of deceased persons and the number of recovery persons;
the input and output module is used for inputting the number of the isolated personnel in each historical period into the trained trend prediction model; acquiring the number of isolated persons of each future period in M future periods after the current period, which are output by the trend prediction model based on the number of isolated persons of each historical period and pre-acquired prior knowledge;
the prediction module is used for predicting epidemic situation trends of the infectious diseases in the M future periods based on the number of the isolated persons in each future period; wherein N and M are both natural numbers greater than or equal to 1.
In a third aspect, an embodiment of the present application provides an electronic device, including:
one or more processors;
a memory for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for training a lightweight detection model according to any embodiment of the present application.
In a fourth aspect, the present application provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for training a lightweight detection model according to any embodiment of the present application.
The techniques according to the present application may improve the accuracy of disease prediction.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a schematic diagram of a first process of an infectious disease trend prediction method according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a trend prediction model provided in an embodiment of the present application;
FIG. 3 is a schematic diagram of a second process of an infectious disease trend prediction method provided in an embodiment of the present application;
FIG. 4 is a schematic diagram of a first structure of an infectious disease trend prediction apparatus provided in an embodiment of the present application;
FIG. 5 is a schematic diagram of a second structure of an infectious disease trend prediction apparatus provided in an embodiment of the present application;
fig. 6 is a block diagram of an electronic device for implementing the infectious disease trend prediction method according to the embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Example one
Fig. 1 is a first flowchart of an infectious disease trend prediction method provided in an embodiment of the present application, where the method may be performed by an infectious disease trend prediction apparatus or an electronic device, which may be implemented by software and/or hardware, and the apparatus or the electronic device may be integrated in any intelligent device with a network communication function. As shown in fig. 1, the infectious disease trend prediction method may include the steps of:
s101, acquiring the number of isolated personnel in each historical period in N historical periods before the current period; wherein, the quantity of isolation personnel includes: the number of dead people and the number of recovery people.
In a specific embodiment of the application, the electronic device may obtain the number of isolated people in each history period in N history periods before the current period; wherein, the quantity of isolation personnel includes: the number of dead people and the number of recovery people. The recovery person in the embodiment of the present application refers to a person who changes from a diseased state to a healthy state in each historical period. Specifically, the number of recovery persons can be obtained from the number of inpatients and number of outpatients counted in each hospital and the type of illness of each person. The history period and the future period in the embodiment of the present application may be respectively in units of days. In this step, the electronic device may obtain the number of isolated people per day in N days before the current day; for example, when the value of N is 3, the electronic device obtains the number of the people isolated in the previous day, the number of the people isolated in the previous two days, and the number of the people isolated in the previous three days in the target geographic range, based on the date of the day.
S102, inputting the number of the isolated personnel in each historical period into a trained trend prediction model; the acquired trend prediction model is based on the number of isolated people in each historical period and the number of isolated people in each future period in M future periods after the current period output by the pre-acquired prior knowledge.
Fig. 2 is a schematic structural diagram of a trend prediction model provided in an embodiment of the present application. As shown in fig. 2, the trend prediction model in the present application may be an SEIQ model; wherein S represents a susceptible population; e represents an exposure latent population, I represents a diagnosed population; q represents an isolated population; moreover, the susceptible population, the exposed latent population, the confirmed diagnosis population and the isolated population have conversion and restriction relations with each other. The SEIQ model is an infectious disease dynamic prediction model combining an improved infectious disease model and a machine learning parameter fitting technology, and divides the population into Susceptible persons (Susceptible state), exposed persons (exposed state), infected persons (Susceptible state) and isolated persons (isolated state), wherein the Susceptible persons (Susceptible), namely the persons who are not affected but lack the immunity and are Susceptible to infection after being contacted with the affected persons; exponents (exposeds), i.e. patients with latent stage, are suitable for infectious diseases with long latent stage; patients (infections), i.e., persons infected with Infectious disease, may be infected with a susceptible condition that changes it into an exposer or a patient; the isolater is the crowd who is in isolation during the epidemic situation, and the isolater can be the person that the sick person carries out the isolation through a period of time.
In this step, the electronic device may input the number of isolated people per each history period into the trained trend prediction model; acquiring the number of isolated persons of a trend prediction model in each future period based on the number of isolated persons in each historical period and the number of isolated persons in each future period in M future periods after the current period output by the pre-acquired prior knowledge; wherein the a priori knowledge may include: probability of infection b, average number of contacts R0, latent exposure coefficient sigma, death or recovery coefficient R; wherein, the value range of the infection probability b is [0.01-0.07 ]; the average number of people who contact R0 is in the range of [2-7 ]; the value range of the latent period exposure coefficient sigma is [1/14-1/10 ]; the death or recovery coefficient r has a value in the range of [1/10-1 ]. The a priori knowledge may be determined based on user input. For example, the user may input the city size, the initial number of people newly added each day, the control date, etc. in the input interface; the electronic device may determine the probability of infection, the average number of contacts, the latency exposure factor, and the death or recovery factor based on the user input. Preferably, in the embodiment of the present application, the infection probability b may be 0.05249; the average number of people exposed R0 can be 7; the latent exposure factor sigma may take the value 1/7.
Further, after inputting the number of isolated persons in each historical period in N historical periods before the current period into the trend prediction model, the electronic device may acquire the number of isolated persons in each future period in M future periods after the current period, which is output by the trend prediction model based on the number of isolated persons in each historical period and the pre-acquired a priori knowledge. For example, when the value of M is 3, the electronic device obtains the number of the isolated people in the target geographic range on the next day, the number of the isolated people in the next two days, and the number of the isolated people in the next three days, based on the date of the day.
S103, predicting epidemic situation trends of the infectious diseases in M future periods based on the number of the isolation personnel in each future period; wherein N and M are both natural numbers greater than or equal to 1.
In a specific embodiment of the present application, the electronic device may predict an epidemic trend of the infectious disease in M future cycles based on the number of isolated persons for each future cycle; wherein N and M are both natural numbers greater than or equal to 1. Specifically, the electronic device may mark the number of isolated people in each future period in the historical cumulative graph and the daily newly added graph respectively; and then predicting epidemic situation trends of the infectious diseases in M future periods based on the historical cumulative graph and the daily new graph. Specifically, the electronic device can predict epidemic situation trends in the stage of natural onset and the stage of city management and control respectively.
The infectious disease trend prediction method provided by the embodiment of the application comprises the steps of firstly obtaining the number of isolated persons in each historical period in N historical periods before the current period; wherein, the quantity of isolation personnel includes: the number of deceased persons and the number of recovery persons; then inputting the number of the isolated personnel in each historical period into a trained trend prediction model; then acquiring the number of isolated persons of a trend prediction model in each future period based on each historical period and the number of isolated persons of each future period in M future periods after the current period output by the pre-acquired prior knowledge; and predicting epidemic situation trends of the infectious diseases in M future periods based on the number of the isolated persons in each future period. That is to say, the present application optimizes the existing SEIR model, and the number of the dead people and the number of the recovery people are uniformly summarized as the number of the isolated people, so that the parameters associated with the dead people can be reduced, the parameter leap is small, and the convergence speed is high. Because the technical means of uniformly summarizing the number of dead people and the number of recovery people into the number of isolation people is adopted, the technical problems that an SQIR model in the prior art is very sensitive to parameters, difficult to adjust parameters, poor in interpretability and difficult to guarantee generalization ability are solved, the technical scheme provided by the application not only can improve the accuracy of disease prediction, but also can provide basic indexes of disease propagation, is strong in interpretability and solves the problem that the generalization ability is difficult to guarantee; moreover, the technical scheme of the embodiment of the application is simple and convenient to implement, convenient to popularize and wide in application range.
Example two
Fig. 3 is a second flowchart of an infectious disease trend prediction method according to an embodiment of the present application. As shown in fig. 3, the infectious disease trend prediction method may include the steps of:
s301, extracting one data sample from the data samples in the stage of natural onset as a current natural sample.
In a specific embodiment of the present application, the electronic device may extract one data sample from data samples in a stage of a natural onset as a current natural sample; wherein, the data sample of the natural onset stage includes: the number of people in the target geographical range in a predetermined morbidity state in a natural morbidity stage; the predetermined morbidity state may include: susceptible, exposed, diseased, and isolated states.
S302, if the trend prediction model does not meet the preset convergence condition corresponding to the natural morbidity stage, inputting the current natural sample into the trend prediction model, and training the trend prediction model by using the current natural sample to obtain at least one natural parameter of the trend prediction model; and taking the next natural sample of the current natural sample as the current natural sample, and repeatedly executing the operations until the trend prediction model meets the convergence condition corresponding to the natural morbidity stage.
In a specific embodiment of the application, if the trend prediction model does not meet the preset convergence condition corresponding to the natural morbidity stage, the electronic device may input the current natural sample to the trend prediction model, and train the trend prediction model using the current natural sample to obtain at least one natural parameter of the trend prediction model; and taking the next natural sample of the current natural sample as the current natural sample, and repeatedly executing the operations until the trend prediction model meets the convergence condition corresponding to the natural morbidity stage. Specifically, the electronic device may calculate a loss function value corresponding to the current natural sample by using a root mean square logarithmic error method according to a prediction result of the trend prediction model for the current natural sample and a real result of the trend prediction model for the current natural sample; a trend prediction model is then trained based on the loss function values.
The purpose of pre-training the trend prediction model in the natural morbidity stage is to train and obtain a set of natural parameters [ R0, b, sigma, R ] of the infectious disease by utilizing the natural development stage of the infectious disease, namely the stage that the early government has not managed and controlled. At this stage, the application uses two ways, namely greedy method and gradient descent: loss is newly increased rmsle every day and accumulated rmsle every day; the subsequent learning of the control parameters is the same training mode.
And S303, extracting one data sample from the data samples in the city management and control stage as a current management and control sample.
In a specific embodiment of the application, the electronic device may extract one data sample from data samples in a city management and control stage as a current management and control sample; wherein, the data sample of city management and control stage includes: the number of crowds in the target geographic range in a preset disease state in the city management and control stage; the predetermined morbidity state may include: susceptible, exposed, diseased, and isolated states.
S304, if the trend prediction model does not meet the preset convergence condition corresponding to the city management and control stage, inputting the current management and control sample to the trend prediction model, and training the trend prediction model by using the current management and control sample to obtain at least one management and control parameter of the trend prediction model; and taking the next control sample of the current control sample as the current control sample, and repeatedly executing the operations until the trend prediction model meets the convergence condition corresponding to the city control stage.
In a specific embodiment of the application, if the trend prediction model does not meet a preset convergence condition corresponding to the city management and control stage, the electronic device may input a current management and control sample to the trend prediction model, and train the trend prediction model using the current management and control sample to obtain at least one management and control parameter of the trend prediction model; and taking the next control sample of the current control sample as the current control sample, and repeatedly executing the operations until the trend prediction model meets the convergence condition corresponding to the city control stage. Specifically, the electronic device may calculate a loss function value corresponding to the current control sample by using a root mean square logarithmic error method according to a prediction result of the trend prediction model for the current control sample and a real result of the trend prediction model for the current control sample; a trend prediction model is then trained based on the loss function values.
Preferably, in an embodiment of the present application, when a population within the target geographic range migrates to the outside of the target geographic range or a population outside of the target geographic range migrates to the inside of the target geographic range, the electronic device may extract a new data sample from the new data samples at the natural onset stage as a current new natural sample; if the trend prediction model does not meet the new convergence condition corresponding to the preset natural morbidity stage, the electronic equipment can input a current new natural sample into the trend prediction model, and train the trend prediction model by using the current new natural sample to obtain at least one new natural parameter of the trend prediction model; and taking the next new natural sample of the current new natural sample as the current new natural sample, and repeatedly executing the operation until the trend prediction model meets the new convergence condition corresponding to the natural morbidity stage.
Preferably, in a specific embodiment of the present application, when a population within the target geographic range migrates outside the target geographic range or a population outside the target geographic range migrates inside the target geographic range, the electronic device may further extract a new data sample from the new data samples in the city management and control stage as a current new management and control sample; if the trend prediction model does not meet the new convergence condition corresponding to the preset city management and control stage, the electronic device may input a current new management and control sample to the trend prediction model, train the trend prediction model by using the current new management and control sample, and obtain at least one new management and control parameter of the trend prediction model; and taking the next new control sample of the current new control sample as the current new control sample, and repeatedly executing the operations until the trend prediction model meets the new convergence condition corresponding to the city control stage.
In the specific embodiment of the application, along with the exposure of infectious diseases, the government can perform corresponding strong control measures such as city closing and the like to avoid the spread of epidemic situations; the model parameter R0 shows a downward trend with time, and this trend is a downward trend with increasing velocity. Therefore, the application uses two exponential functions with the descending controlled by parameters, and the training results are b _ c1, b _ c2 and b _ c 3; using these three parameters, a sharp downward trend of increasing speed can be shown.
S305, acquiring the number of isolated personnel in each historical period in N historical periods before the current period; wherein, the quantity of isolation personnel includes: the number of dead people and the number of recovery people.
S306, inputting the number of the isolated personnel in each historical period into a trained trend prediction model; the acquired trend prediction model is based on the number of isolated people in each historical period and the number of isolated people in each future period in M future periods after the current period output by the pre-acquired prior knowledge.
S307, predicting epidemic situation trends of the infectious diseases in M future periods based on the number of the isolation personnel in each future period; wherein N and M are both natural numbers greater than or equal to 1.
The infectious disease trend prediction method provided by the embodiment of the application comprises the steps of firstly obtaining the number of isolated persons in each historical period in N historical periods before the current period; wherein, the quantity of isolation personnel includes: the number of deceased persons and the number of recovery persons; then inputting the number of the isolated personnel in each historical period into a trained trend prediction model; then acquiring the number of isolated persons of a trend prediction model in each future period based on each historical period and the number of isolated persons of each future period in M future periods after the current period output by the pre-acquired prior knowledge; and predicting epidemic situation trends of the infectious diseases in M future periods based on the number of the isolated persons in each future period. That is to say, the present application optimizes the existing SEIR model, and the number of the dead people and the number of the recovery people are uniformly summarized as the number of the isolated people, so that the parameters associated with the dead people can be reduced, the parameter leap is small, and the convergence speed is high. Because the technical means of uniformly summarizing the number of dead people and the number of recovery people into the number of isolation people is adopted, the technical problems that an SQIR model in the prior art is very sensitive to parameters, difficult to adjust parameters, poor in interpretability and difficult to guarantee generalization ability are solved, the technical scheme provided by the application not only can improve the accuracy of disease prediction, but also can provide basic indexes of disease propagation, is strong in interpretability and solves the problem that the generalization ability is difficult to guarantee; moreover, the technical scheme of the embodiment of the application is simple and convenient to implement, convenient to popularize and wide in application range.
EXAMPLE III
Fig. 4 is a schematic view of a first structure of an infectious disease trend prediction apparatus provided in an embodiment of the present application. As shown in fig. 4, the apparatus 400 includes: an acquisition module 401, an input-output module 402 and a prediction module 403; wherein,
the obtaining module 401 is configured to obtain the number of isolated people in each history period in N history periods before the current period; wherein the number of isolated persons comprises: the number of deceased persons and the number of recovery persons;
the input and output module 402 is configured to input the number of isolated people in each history period into a trained trend prediction model; acquiring the number of isolated persons of each future period in M future periods after the current period, which are output by the trend prediction model based on the number of isolated persons of each historical period and pre-acquired prior knowledge;
the prediction module 403 is configured to predict epidemic situation trends of infectious diseases in the M future cycles based on the number of isolated persons in each future cycle; wherein N and M are both natural numbers greater than or equal to 1.
Further, the a priori knowledge includes: probability of infection, average exposure, latent exposure, mortality or recovery; wherein the value range of the infection probability is [0.01-0.07 ]; the value range of the average number of the contact persons is [2-7 ]; the value range of the latent period exposure coefficient is [1/14-1/10 ]; the death or recovery coefficient has a value range of [1/10-1 ].
Fig. 5 is a schematic diagram of a second structure of an infectious disease trend prediction apparatus according to an embodiment of the present application. As shown in fig. 5, the apparatus 500 further includes: a training module 404, configured to extract a data sample from data samples in a natural onset stage as a current natural sample; if the trend prediction model does not meet the preset convergence condition corresponding to the natural morbidity stage, inputting the current natural sample into the trend prediction model, and training the trend prediction model by using the current natural sample to obtain at least one natural parameter of the trend prediction model; and taking the next natural sample of the current natural sample as the current natural sample, and repeatedly executing the operations until the trend prediction model meets the convergence condition corresponding to the natural morbidity stage.
Further, the training module 404 is further configured to extract a data sample from the data samples in the city management and control stage as a current management and control sample; if the trend prediction model does not meet the preset convergence condition corresponding to the city management and control stage, inputting the current management and control sample to the trend prediction model, and training the trend prediction model by using the current management and control sample to obtain at least one management and control parameter of the trend prediction model; and taking the next control sample of the current control sample as the current control sample, and repeatedly executing the operations until the trend prediction model meets the convergence condition corresponding to the city control stage.
Further, the data samples of the stage of natural onset include: the number of populations within the target geographic range in the natural onset phase at a predetermined onset state; wherein the predetermined morbidity state comprises: susceptible, exposed, diseased, and isolated states.
Further, the data samples of the city management and control phase include: the number of people within the target geographic range in the city management and control stage under the predetermined morbidity state; wherein the predetermined morbidity state comprises: susceptible, exposed, diseased, and isolated states.
Further, the training module 404 is further configured to extract a new data sample from the new data samples in the natural onset stage as a current new natural sample when the population in the target geographic range moves out of the target geographic range or the population outside the target geographic range moves into the target geographic range; if the trend prediction model does not meet a preset new convergence condition corresponding to the natural morbidity stage, inputting the current new natural sample into the trend prediction model, and training the trend prediction model by using the current new natural sample to obtain at least one new natural parameter of the trend prediction model; and taking a next new natural sample of the current new natural sample as the current new natural sample, and repeatedly executing the operations until the trend prediction model meets a new convergence condition corresponding to the natural onset stage.
Further, the training module 404 is further configured to extract a new data sample from the new data samples in the city management and control stage as a current new management and control sample; if the trend prediction model does not meet a preset new convergence condition corresponding to the city management and control stage, inputting the current new management and control sample to the trend prediction model, and training the trend prediction model by using the current new management and control sample to obtain at least one new management and control parameter of the trend prediction model; and taking a next new management and control sample of the current new management and control sample as the current new management and control sample, and repeatedly executing the above operations until the trend prediction model meets a new convergence condition corresponding to the city management and control stage.
The infectious disease trend prediction device can execute the method provided by any embodiment of the application, and has corresponding functional modules and beneficial effects of the execution method. The technical details not described in detail in this embodiment can be referred to the infectious disease trend prediction method provided in any embodiment of the present application.
Example four
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 6, the electronic device is a block diagram of an electronic device of a method for infectious disease trend prediction according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the infectious disease trend prediction methods provided herein. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to perform the infectious disease trend prediction method provided herein.
The memory 602, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the infectious disease trend prediction method in the embodiment of the present application (for example, the obtaining module 401, the input-output module 402, and the prediction module 403 shown in fig. 4). The processor 601 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 602, that is, the infectious disease trend prediction method in the above method embodiment is implemented.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the electronic device predicted from the tendency of the infectious disease, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 602 may optionally include memory located remotely from the processor 601, which may be connected to the electronic device of the infectious disease trend prediction method via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the infectious disease trend prediction method may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected by a bus or other means, and fig. 6 illustrates the connection by a bus as an example.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic equipment of the infectious disease tendency prediction method, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or the like. The output devices 604 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
According to the technical scheme of the embodiment of the application, the number of the isolated personnel in each historical period in N historical periods before the current period is obtained; wherein, the quantity of isolation personnel includes: the number of deceased persons and the number of recovery persons; then inputting the number of the isolated personnel in each historical period into a trained trend prediction model; then acquiring the number of isolated persons of a trend prediction model in each future period based on each historical period and the number of isolated persons of each future period in M future periods after the current period output by the pre-acquired prior knowledge; and predicting epidemic situation trends of the infectious diseases in M future periods based on the number of the isolated persons in each future period. That is to say, the present application optimizes the existing SEIR model, and the number of the dead people and the number of the recovery people are uniformly summarized as the number of the isolated people, so that the parameters associated with the dead people can be reduced, the parameter leap is small, and the convergence speed is high. Because the technical means of uniformly summarizing the number of dead people and the number of recovery people into the number of isolation people is adopted, the technical problems that an SQIR model in the prior art is very sensitive to parameters, difficult to adjust parameters, poor in interpretability and difficult to guarantee generalization ability are solved, the technical scheme provided by the application not only can improve the accuracy of disease prediction, but also can provide basic indexes of disease propagation, is strong in interpretability and solves the problem that the generalization ability is difficult to guarantee; moreover, the technical scheme of the embodiment of the application is simple and convenient to implement, convenient to popularize and wide in application range.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (18)
1. An infectious disease trend prediction method, the method comprising:
acquiring the number of isolated personnel in each historical period in N historical periods before the current period; wherein the number of isolated persons comprises: the number of deceased persons and the number of recovery persons;
inputting the number of the isolated personnel in each historical period into a trained trend prediction model; acquiring the number of isolated persons of each future period in M future periods after the current period, which are output by the trend prediction model based on the number of isolated persons of each historical period and pre-acquired prior knowledge;
predicting epidemic situation trends of the infectious diseases in the M future periods based on the number of the isolated persons in each future period; wherein N and M are both natural numbers greater than or equal to 1.
2. The method of claim 1, wherein the a priori knowledge comprises: probability of infection, average exposure, latent exposure, mortality or recovery; wherein the value range of the infection probability is [0.01-0.07 ]; the value range of the average number of the contact persons is [2-7 ]; the value range of the latent period exposure coefficient is [1/14-1/10 ]; the death or recovery coefficient has a value range of [1/10-1 ].
3. The method of claim 1, prior to said obtaining the number of isolated people for each of N historical periods prior to the current period, the method further comprising:
extracting a data sample from the data samples in the stage of natural onset as a current natural sample;
if the trend prediction model does not meet the preset convergence condition corresponding to the natural morbidity stage, inputting the current natural sample into the trend prediction model, and training the trend prediction model by using the current natural sample to obtain at least one natural parameter of the trend prediction model; and taking the next natural sample of the current natural sample as the current natural sample, and repeatedly executing the operations until the trend prediction model meets the convergence condition corresponding to the natural morbidity stage.
4. The method of claim 3, further comprising:
extracting a data sample from the data samples in the city management and control stage as a current management and control sample;
if the trend prediction model does not meet the preset convergence condition corresponding to the city management and control stage, inputting the current management and control sample to the trend prediction model, and training the trend prediction model by using the current management and control sample to obtain at least one management and control parameter of the trend prediction model; and taking the next control sample of the current control sample as the current control sample, and repeatedly executing the operations until the trend prediction model meets the convergence condition corresponding to the city control stage.
5. The method of claim 3, the data samples of the stage of nature onset comprising: the number of populations within the target geographic range in the natural onset phase at a predetermined onset state; wherein the predetermined morbidity state comprises: susceptible, exposed, diseased, and isolated states.
6. The method of claim 4, the city governance phase data samples comprising: the number of people within the target geographic range in the city management and control stage under the predetermined morbidity state; wherein the predetermined morbidity state comprises: susceptible, exposed, diseased, and isolated states.
7. The method of claim 3, further comprising:
when the population in the target geographic range moves out of the target geographic range or the population outside the target geographic range moves into the target geographic range, extracting a new data sample from the new data samples in the natural onset stage as a current new natural sample;
if the trend prediction model does not meet a preset new convergence condition corresponding to the natural morbidity stage, inputting the current new natural sample into the trend prediction model, and training the trend prediction model by using the current new natural sample to obtain at least one new natural parameter of the trend prediction model; and taking a next new natural sample of the current new natural sample as the current new natural sample, and repeatedly executing the operations until the trend prediction model meets a new convergence condition corresponding to the natural onset stage.
8. The method of claim 7, further comprising:
extracting a new data sample from the new data samples in the city management and control stage as a current new management and control sample;
if the trend prediction model does not meet a preset new convergence condition corresponding to the city management and control stage, inputting the current new management and control sample to the trend prediction model, and training the trend prediction model by using the current new management and control sample to obtain at least one new management and control parameter of the trend prediction model; and taking a next new management and control sample of the current new management and control sample as the current new management and control sample, and repeatedly executing the above operations until the trend prediction model meets a new convergence condition corresponding to the city management and control stage.
9. An infectious disease trend prediction device, the device comprising: the device comprises an acquisition module, an input/output module and a prediction module; wherein,
the acquisition module is used for acquiring the number of isolated personnel in each historical period in N historical periods before the current period; wherein the number of isolated persons comprises: the number of deceased persons and the number of recovery persons;
the input and output module is used for inputting the number of the isolated personnel in each historical period into the trained trend prediction model; acquiring the number of isolated persons of each future period in M future periods after the current period, which are output by the trend prediction model based on the number of isolated persons of each historical period and pre-acquired prior knowledge;
the prediction module is used for predicting epidemic situation trends of the infectious diseases in the M future periods based on the number of the isolated persons in each future period; wherein N and M are both natural numbers greater than or equal to 1.
10. The apparatus of claim 9, the a priori knowledge comprising: probability of infection, average exposure, latent exposure, mortality or recovery; wherein the value range of the infection probability is [0.01-0.07 ]; the value range of the average number of the contact persons is [2-7 ]; the value range of the latent period exposure coefficient is [1/14-1/10 ]; the death or recovery coefficient has a value range of [1/10-1 ].
11. The apparatus of claim 9, the apparatus further comprising: the training module is used for extracting a data sample from the data samples in the stage of natural onset as a current natural sample; if the trend prediction model does not meet the preset convergence condition corresponding to the natural morbidity stage, inputting the current natural sample into the trend prediction model, and training the trend prediction model by using the current natural sample to obtain at least one natural parameter of the trend prediction model; and taking the next natural sample of the current natural sample as the current natural sample, and repeatedly executing the operations until the trend prediction model meets the convergence condition corresponding to the natural morbidity stage.
12. The device of claim 11, wherein the training module is further configured to extract one data sample from data samples in an urban management and control stage as a current management and control sample; if the trend prediction model does not meet the preset convergence condition corresponding to the city management and control stage, inputting the current management and control sample to the trend prediction model, and training the trend prediction model by using the current management and control sample to obtain at least one management and control parameter of the trend prediction model; and taking the next control sample of the current control sample as the current control sample, and repeatedly executing the operations until the trend prediction model meets the convergence condition corresponding to the city control stage.
13. The apparatus of claim 11, the data samples of the stage of nature onset comprising: the number of populations within the target geographic range in the natural onset phase at a predetermined onset state; wherein the predetermined morbidity state comprises: susceptible, exposed, diseased, and isolated states.
14. The apparatus of claim 12, the data samples of the city regulation phase comprising: the number of people within the target geographic range in the city management and control stage under the predetermined morbidity state; wherein the predetermined morbidity state comprises: susceptible, exposed, diseased, and isolated states.
15. The apparatus according to claim 11, wherein the training module is further configured to extract a new data sample from the new data samples in the natural onset stage as a current new natural sample when the population within the target geographic range migrates outside the target geographic range or the population outside the target geographic range migrates inside the target geographic range; if the trend prediction model does not meet a preset new convergence condition corresponding to the natural morbidity stage, inputting the current new natural sample into the trend prediction model, and training the trend prediction model by using the current new natural sample to obtain at least one new natural parameter of the trend prediction model; and taking a next new natural sample of the current new natural sample as the current new natural sample, and repeatedly executing the operations until the trend prediction model meets a new convergence condition corresponding to the natural onset stage.
16. The device of claim 15, wherein the training module is further configured to extract a new data sample from the new data samples in the city management and control stage as a current new management and control sample; if the trend prediction model does not meet a preset new convergence condition corresponding to the city management and control stage, inputting the current new management and control sample to the trend prediction model, and training the trend prediction model by using the current new management and control sample to obtain at least one new management and control parameter of the trend prediction model; and taking a next new management and control sample of the current new management and control sample as the current new management and control sample, and repeatedly executing the above operations until the trend prediction model meets a new convergence condition corresponding to the city management and control stage.
17. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.
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