A method, device, storage medium and equipment for determining the infection detection rate of infectious diseases

Abstract

The present invention discloses a method, device, storage medium and equipment for determining the infection detection rate of infectious diseases, obtaining the cumulative number of confirmed cases of preset infectious diseases in a preset area before a preset time, and using a risk prediction model for a preset stage of a preset infectious disease in a preset area to determine the number of infected people of the preset stage of the preset infectious disease in the preset area before the preset time, thereby determining the cumulative number of infected people in the preset area up to the preset time based on the number of infected people in the preset stage, and determining the cumulative infection detection rate for the preset infectious disease in the preset area up to the preset time based on the cumulative number of confirmed cases and the cumulative number of infected people. The infectious disease infection detection rate determination method provided by the present invention avoids more assumptions in the process of determining the cumulative infection detection rate, and makes full use of medical data in multiple dimensions to build a risk prediction model, so as to better estimate the infection detection rate of chronic infectious diseases through the risk prediction model.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, computer-readable storage medium and equipment for determining an infectious disease infection detection rate. Background Art

[0002] Chronic infectious diseases usually have a long incubation period and no obvious symptoms during the incubation period. Therefore, there are problems such as not knowing after infection. For example, it is very difficult to quickly self-screen and diagnose infectious diseases such as hepatitis B, tuberculosis, and HIV. For countries with large populations, large regional differences in the distribution of chronic infectious diseases, and low overall infection rates, how to predict the infectious disease detection rate in various places is a technical problem that needs to be solved urgently. The infection detection rate is a very important indicator for AIDS prevention, control, and treatment, but how to evaluate the infection detection rate of infected people at the population level is still an unresolved technical bottleneck. Using the individual demographic, epidemiological, and immunological data of infected people in local areas, and the detection data of detection and discovery institutions, a functional relationship is established between the above basic information and the infection detection rate of the population, and a mathematical model is established.

[0003] In recent decades, the following methods have been developed to estimate the total number of infectious disease infections: the Workbook Method, the estimation and projection package (EPP) model, and the Spectrum model. The infectious disease infection detection rate is further determined based on the total number of infectious disease infections. However, these methods require the application of a large number of parameters and require the setting of many assumptions. For the regional chronic infectious disease infection detection rate, the above methods are used to estimate the infectious disease infection detection rate, and the estimated results of the infectious disease infection detection rate have a low reference value. Therefore, there is an urgent need for a method to determine the infectious disease infection detection rate. Summary of the invention

[0004] In order to solve the above problems, the embodiments of the present invention creatively provide a method, apparatus, computer-readable storage medium and device for determining the infection detection rate of an infectious disease.

[0005] According to a first aspect of the present invention, a method for determining an infection detection rate of an infectious disease is provided, the method comprising: obtaining the number of confirmed people at multiple stages of a preset infectious disease corresponding to multiple preset time periods before a preset time in a preset area; determining the number of infected people at the corresponding stage according to the number of confirmed people at the stage in the preset time period, using a corresponding risk prediction model for the preset time period of the preset infectious disease in the preset area; determining the cumulative infection detection rate for the preset infectious disease in the preset area up to the preset time according to the multiple number of confirmed people at the stage and the number of infected people at the stage.

[0006] According to one embodiment of the present invention, the cumulative infection detection rate of the preset infectious disease in the preset area up to the preset time is determined based on the number of confirmed cases in the multiple stages and the number of infected people in the multiple stages, including: determining the cumulative number of confirmed cases of the preset infectious disease in the preset area before the preset time based on the number of confirmed cases in the multiple stages; determining the cumulative number of infected people of the preset infectious disease in the preset area before the preset time based on the number of infected people in the multiple stages; determining the cumulative infection detection rate of the preset infectious disease in the preset area before the preset time based on the cumulative number of confirmed cases and the cumulative number of infected people.

[0007] According to one embodiment of the present invention, the following operations are used to train a risk prediction model for a preset period of time for a preset infectious disease in the preset area: obtain sample parameters of multiple confirmed cases of the preset infectious disease diagnosed in the preset area before the preset time; determine the infection time of the confirmed cases corresponding to the sample parameters based on the sample parameters; obtain the diagnosis influencing factors of the multiple confirmed cases; construct stage survival data for a preset period of time based on the infection time and diagnosis influencing factors of the multiple confirmed cases; and train a risk prediction model for the preset period of time for the preset infectious disease in the preset area based on the stage survival data for the preset period of time.

[0008] According to one embodiment of the present invention, based on the infection time and diagnosis influencing factors of the multiple confirmed cases, stage survival data for a preset time period is constructed, including: determining the following parameters of confirmed cases whose infection time is between the preset time period or before the preset time period and whose diagnosis time is between the preset time period or after the preset time period, as the stage survival data for the preset time period: diagnosis influencing factors, monitoring time, and whether confirmed in the preset time period; wherein the monitoring time is used to show the time from the infection time of the confirmed case to the preset time period.

[0009] According to one embodiment of the present invention, the training of the risk prediction model for the preset time period of the preset infectious disease in the preset area also includes: verifying and adjusting the diagnosis influencing factors according to the risk prediction model for the preset time period of the preset infectious disease in the preset area obtained by training to obtain adjusted diagnosis influencing factors; and optimizing the risk prediction model for the preset time period of the preset infectious disease in the preset area based on the adjusted diagnosis influencing factors.

[0010] According to one embodiment of the present invention, based on the number of confirmed cases in a stage in a preset time period, the corresponding number of infected people in the stage is determined by using the corresponding risk prediction model for the preset time period of the preset infectious disease in the preset area, including: based on the number of confirmed cases in the stage in the preset time period, using the risk prediction model for the preset time period of the preset infectious disease in the preset area, determining the cumulative probability of an infected object in a preset infection year being diagnosed in the preset time period and before the preset time period, wherein the predicted infection year is earlier than the preset time period; obtaining the number of confirmed patients from the predicted infection time period to the preset time period; and determining the number of infected people in the stage when the predicted infection time is the preset time period before the preset time period.

[0011] According to the second aspect of the present invention, a method and device for determining the infection detection rate of an infectious disease is also provided, the device comprising: an acquisition module, used to obtain the number of confirmed people at multiple stages of a preset infectious disease corresponding to multiple preset time periods before a preset time in a preset area; an infection determination module, used to determine the number of infected people at the corresponding stage according to the number of confirmed people at the stage in the preset time period, using a corresponding risk prediction model for the preset time period of the preset infectious disease in the preset area; a detection rate determination module, used to determine the cumulative infection detection rate for the preset infectious disease in the preset area up to the preset time according to the multiple number of confirmed people at the stage and the number of infected people at the stage.

[0012] According to one embodiment of the present invention, the detection rate determination module includes: a cumulative confirmed determination submodule, used to determine the cumulative number of confirmed people who have been diagnosed with the preset infectious disease in the preset area before the preset time based on the number of confirmed people in the multiple stages; a cumulative infection determination submodule, used to determine the cumulative number of infected people who have been diagnosed with the preset infectious disease in the preset area before the preset time based on the number of infected people in the multiple stages; a cumulative infection detection rate determination submodule, used to determine the cumulative infection detection rate of the preset infectious disease in the preset area before the preset time based on the cumulative number of confirmed people and the cumulative number of infected people.

[0013] According to the third aspect of the present invention, there is also provided a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned infectious disease infection detection rate determination method when executing the program.

[0014] According to a fourth aspect of the present invention, there is also provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for determining the infection detection rate of an infectious disease.

[0015] The infectious disease infection detection rate determination method, device, computer-readable storage medium and equipment of the embodiments of the present invention use a risk prediction model for a preset stage of an infectious disease preset in a preset area to determine the number of people infected in the preset stage of the preset infectious disease preset in the preset area before the preset time, and obtain the cumulative number of confirmed cases of the preset infectious disease in the preset area before the preset time, thereby determining the cumulative number of infected people in the preset area up to the preset time based on the number of infected people in the preset stage, and further determining the cumulative infection detection rate for the preset infectious disease in the preset area up to the preset time based on the cumulative number of confirmed cases and the cumulative number of infected people. The infectious disease infection detection rate determination method provided by the present invention avoids more assumptions in the process of determining the cumulative infection detection rate, and makes full use of medical data in multiple dimensions to build a risk prediction model, so as to better estimate the infection detection rate of chronic infectious diseases through the risk prediction model.

[0016] It should be understood that the teachings of the present invention are not required to achieve all of the beneficial effects described above, but specific technical solutions can achieve specific technical effects, and other embodiments of the present invention can also achieve beneficial effects not mentioned above. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0018] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0019] Figure 1 A schematic diagram of the implementation process of a method for determining an infection detection rate of an infectious disease provided by an embodiment of the present invention is shown;

[0020] Figure 2 A schematic diagram of an implementation flow of a method for determining an infection detection rate of an infectious disease provided by another embodiment of the present invention is shown;

[0021] Figure 3 A schematic diagram showing the composition and structure of a device for determining an infection detection rate of an infectious disease provided by an embodiment of the present invention is shown;

[0022] Figure 4 A schematic diagram of the structure of a device provided in accordance with an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0023] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0024] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0025] Figure 1 A schematic diagram of the implementation flow of a method for determining an infectious disease infection detection rate provided by an embodiment of the present invention is shown.

[0026] refer to Figure 1 The infectious disease infection detection rate determination method of the embodiment of the present invention at least includes the following operation procedures: cumulative infection detection rate operation 101, obtaining the number of confirmed people of multiple stages of a preset infectious disease corresponding to multiple preset time periods before a preset time in a preset area; operation 102, according to the number of confirmed people of the stage in the preset time period, using the corresponding risk prediction model for the preset infectious disease in the preset area during the preset time period, to determine the corresponding number of infected people in the stage; operation 103, according to the number of confirmed people in the multiple stages and the number of infected people in the stage, determine the cumulative infection detection rate for the preset infectious disease in the preset area up to the preset time.

[0027] In operation 101, the number of confirmed cases of a preset infectious disease at multiple stages in a preset area corresponding to multiple preset time periods before a preset time is obtained.

[0028] In this embodiment of the present invention, the preset region can be a country, a provincial region or a municipal region, etc. It can be set specifically according to actual conditions, and the present invention does not make specific limitations on this. The preset infectious disease can be a chronic infectious disease, such as hepatitis B, tuberculosis, HIV and other infectious diseases, which usually have the characteristics of long incubation period, no symptoms during the incubation period, and not easy to be discovered through some obvious symptoms after infection.

[0029] In this embodiment of the present invention, the cumulative number of confirmed cases of the preset infectious disease before the preset time can be directly obtained. The relevant departments will have statistical data on these cumulative confirmed cases. Here, it only needs to be used as input data for the infectious disease infection detection rate determination method.

[0030] In this embodiment of the present invention, the preset period can be one year. For the statistics of the infection discovery rate of the preset infectious disease, the reference and application value in terms of years is relatively high. Therefore, in the following, taking the preset period as one year as an example, the solution of the present invention will be described. However, in the actual application process, the duration of the preset period can be set according to the actual situation. For example, the duration of the preset period can be set to half a year, 9 months, 1 year, 2 years, etc.

[0031] In operation 102, according to the number of stage-confirmed cases in the preset period, using the corresponding risk prediction model for the preset infectious disease in the preset area and the preset period, determine the corresponding number of stage infections.

[0032] In this embodiment of the present invention, the following operations are adopted to realize determining the corresponding number of stage infections according to the number of stage-confirmed cases in the preset period and using the corresponding risk prediction model for the preset infectious disease in the preset area and the preset period: According to the number of stage-confirmed cases in the preset period, using the risk prediction model for the preset infectious disease in the preset area and the preset period, determine the cumulative probability that the infected objects in the predicted infection year are diagnosed during the preset period and before the preset period, where the predicted infection year is earlier than the preset period; obtain the number of confirmed patients from the predicted infection period to the preset period; in the case where the predicted infection time is the previous preset period of the preset period, determine the number of stage infections.

[0033] In this embodiment of the present invention, a risk prediction model for the preset stage of the preset infectious disease in the preset area is constructed. The risk prediction model can be constructed using the COX regression model (also known as the proportional hazards model, Proportional Hazards Model).

[0034] By training the risk prediction model, the baseline survival function (Baseline Survival Function) can be obtained: Pn(T>t), where Pn represents the cumulative probability that the cases infected with the preset infectious disease in the nth year (for example: 2000, 2008, or 2020, etc.) are diagnosed after t years (for example: 1 year, 2 years, or 3 years, etc.) starting from the nth year. Thus, the cumulative probability that the cases infected with the preset infectious disease in the nth year are diagnosed before t years starting from the nth year can be further obtained: 1 - Pn(T>t). Where T represents the survival time, which can refer to the time from being infected to being diagnosed for the confirmed cases here.

[0035] According to the cumulative probability, the number of infected people in each year can be estimated. Specifically, count the number of people Wi(n < i < t) who were diagnosed before t years among the patients infected with the infectious disease in the nth year and the total number of confirmed cases Wn-t from the nth year to t years starting from the nth year. Then the total number of infected people in the nth year is Wn-t / (1 - Pn(T>t)).

[0036] For example, using the risk prediction model for HIV (AIDS) in region A in 2000, determine the cumulative probability P of an infected person in the preset infection year 2004 being diagnosed in the preset year 2005 or before the preset year 2005 2004-1 Here we get the total number of confirmed HIV (AIDS) patients in region A from 2004 to 2005, which is W 2004-1 , then the number of infections in the preset year 2005 can be W 2004-1 / (1-P 2004-1 ).

[0037] In operation 103, based on the number of confirmed cases and the number of infected people in multiple stages, a cumulative infection detection rate for a preset infectious disease in a preset area up to a preset time is determined.

[0038] In this embodiment of the present invention, the following operations are used to determine the cumulative infection detection rate of a preset infectious disease in a preset area as of a preset time based on the number of confirmed cases in multiple stages and the number of infected people in each stage: determine the cumulative number of confirmed cases of the preset infectious disease in the preset area before the preset time based on the number of confirmed cases in multiple stages; determine the cumulative number of infected people of the preset infectious disease in the preset area before the preset time based on the number of infected people in multiple stages; determine the cumulative infection detection rate of the preset infectious disease in the preset area before the preset time based on the cumulative number of confirmed cases and the cumulative number of infected people.

[0039] For example, it is necessary to determine the cumulative infection detection rate of infectious disease B as of year K. The cumulative number of confirmed cases as of year K is obtained as Qk, and the cumulative number of infected people as of year K is obtained as Gk, then the cumulative infection detection rate as of year K is Qk / Gk. Here, it should be noted that as of year K refers to the last day of year K as the preset time, for example: the preset time is December 31, 2000.

[0040] The cumulative number of confirmed cases up to year K can be determined by the method in operation 102 by first determining the number of infected people in each preset year since the statistical year for the preset infectious disease in the preset area, and then accumulating and summing the number of infected people in all years up to year K to obtain the cumulative number of confirmed cases up to year K.

[0041] Figure 2 A schematic diagram of the implementation flow of a method for determining an infectious disease infection detection rate provided by another embodiment of the present invention is shown.

[0042] refer to Figure 2 The infectious disease infection detection rate determination method of the embodiment of the present invention at least includes the following operation process:

[0043] Operation 201 is to obtain the number of confirmed cases of a preset infectious disease at multiple stages in a preset area corresponding to multiple preset time periods before a preset time.

[0044] Operation 202, obtaining sample parameters of a plurality of confirmed cases of a preset infectious disease in a preset area before a preset time;

[0045] Operation 203: Determine the infection time of the confirmed case corresponding to the sample parameters according to the sample parameters.

[0046] In this embodiment of the present invention, if the preset infectious disease is HIV, the CD4+T lymphocyte level of confirmed HIV cases before treatment shows a regular downward trend with the time from infection to diagnosis, and the square root of the CD4 test value can have an approximately linear relationship with the infection time. Therefore, the infection time of the confirmed case can be inferred based on the number of CD4+T lymphocytes at the time of diagnosis. The square root of the CD4 test value of a case after a period of time T has an approximately linear relationship with the period of time T. Therefore, the infection time of the confirmed case can be calculated according to the following formula:

[0047]

[0048] Among them, CD4(T) represents the CD4 test value of a confirmed case after a period of time T.

[0049] I NTERCEPT It represents the intercept of the linear function formed by the square root of the CD4 test value and the time length T;

[0050] S LOPE It represents the slope of the linear function formed by the square root of the CD4 test value and the time T;

[0051] E RROR It represents the error of the linear function formed by the square root of the CD4 detection value and the time length T.

[0052] Operation 204: Obtain the diagnosis influencing factors of multiple confirmed cases.

[0053] In this embodiment of the present invention, factors influencing the diagnosis of confirmed cases include, but are not limited to, demographic characteristics of a preset area, current mandatory testing policies, testing data, etc.

[0054] Operation 205 : constructing stage survival data of a preset time period based on the infection time and diagnosis influencing factors of multiple confirmed cases.

[0055] In this embodiment of the present invention, based on the infection time and diagnosis influencing factors of multiple confirmed cases, the stage survival data of the preset year is constructed, which can be achieved by the following operation: determining the following parameters of the confirmed cases whose infection time is between or before the preset time period and whose diagnosis time is between or after the preset time period as the stage survival data of the preset time period: diagnosis influencing factors, monitoring time, and whether the diagnosis is made in the preset time period. Among them, the monitoring time is used to show the stage survival data of the confirmed cases from the infection time to the preset time period.

[0056] For example, survival data can be generated according to the year of infection, and the infected persons infected with the preset infectious disease in the same year are taken as a survival data. The stage survival data can record the patient's status from infection to diagnosis every year. For example, a confirmed case was infected with the preset infectious disease in 2000 and diagnosed with the preset infectious disease in 2010. The survival data of the confirmed case includes 10 data. The infection of the confirmed case can be regarded as the starting event, the diagnosis can be regarded as the failure event, and the length of time between infection and diagnosis can be regarded as the survival time. Thus, the stage survival data for a year can be constructed according to the year of infection. Each survival data includes various variables of the infected persons who have been infected with the preset infectious disease but not diagnosed in that year, the observation time and whether the outcome event occurs, where the outcome event refers to the diagnosis of the preset infectious disease. The various variables of the infected person refer to the diagnosis influencing factors that affect the diagnosis of the infected person, which can include: the routine inspection mechanism for the preset infectious disease in the preset area and the preset treatment cost subsidy policy for the preset infectious disease.

[0057] Operation 206 : Based on the stage survival data of the preset time period, a risk prediction model stage survival data of the preset time period for a preset infectious disease in a preset area is trained.

[0058] For example, a risk prediction model for a preset infectious disease in a preset area in a preset year can be constructed using a COX regression model (also known as a proportional hazard regression model, Proportional Hazards Model).

[0059] The survival function in the COX regression model is mainly used here. In the survival function, the probability S(t, T) = P(T>t, X) that the survival time T of the observed object with variable X = (X1, X2, ..., Xm) is greater than a certain time t, and S(t, T) can also be the cumulative survival rate. In this scheme, the variable X can be multiple confirmed influencing factors of the confirmed case.

[0060] In this embodiment of the present invention, the risk prediction model for a preset time period of a preset infectious disease in a preset area is trained, and the method also includes: verifying and adjusting the diagnosis influencing factors based on the risk prediction model for the preset time period of a preset infectious disease in a preset area obtained through training to obtain adjusted diagnosis influencing factors; and optimizing the risk prediction model for the preset time period of a preset infectious disease in a preset area based on the adjusted diagnosis influencing factors.

[0061] Specifically, the influencing factors of confirmed cases are predetermined, but in the actual risk prediction model training process, based on the accuracy of the obtained risk prediction model, it is possible to determine the influencing factors of confirmed cases that have a particularly small impact on the diagnosis of infectious diseases among the predetermined influencing factors. At this time, some influencing factors can be deleted or added, or other methods can be used to adjust multiple influencing factors to further improve the accuracy of the risk prediction model. The process of adjusting multiple influencing factors is actually a feature screening process, where the step-by-step forward selection algorithm (Step backward selection) in the feature selection algorithm can be used to screen features.

[0062] Operation 207, according to the number of confirmed cases in the preset period, using the corresponding risk prediction model for the preset infectious disease in the preset area and the preset period, to determine the number of infected people in the corresponding stage.

[0063] Operation 208, determining a cumulative infection detection rate for a preset infectious disease in a preset area up to a preset time according to the number of confirmed cases and the number of infected people in multiple stages.

[0064] The specific implementation process of operations 201, 207 and 208 is the same as Figure 1 The specific implementation processes of operations 101, 102 and 103 in the illustrated embodiment are similar and will not be described in detail here.

[0065] The infectious disease infection detection rate determination method, device, computer-readable storage medium and equipment of the embodiment of the present invention use the risk prediction model for the preset infectious disease in the preset area during the preset period to determine the number of people infected in the preset infectious disease in the preset area during the preset period before the preset time, and obtain the cumulative number of confirmed cases of the preset infectious disease in the preset area before the preset time, thereby determining the cumulative number of infected people in the preset area up to the preset time based on the number of infected people in the preset period, and further determining the cumulative infection detection rate for the preset infectious disease in the preset area up to the preset time based on the cumulative number of confirmed cases and the cumulative number of infected people. The infectious disease infection detection rate determination method provided by the present invention avoids more assumptions in the process of determining the cumulative infection detection rate, and makes full use of medical data in multiple dimensions to build a risk prediction model, so as to better estimate the infection detection rate of chronic infectious diseases through the risk prediction model.

[0066] like Figure 3 The figure is a specific embodiment of the infectious disease infection detection rate determination device of the present invention. The device of this embodiment is used to perform Figures 1-2 The technical solution is essentially the same as that of the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. Figure 3 The embodiment of the present invention provides a method and device for determining the infection detection rate of an infectious disease. The device 30 includes: an acquisition module 301, which is used to obtain the number of confirmed cases of a preset infectious disease in multiple stages corresponding to multiple preset time periods before a preset time in a preset area; an infection determination module 302, which is used to determine the corresponding number of infected people in the stage according to the number of confirmed cases in the preset time period, using the corresponding risk prediction model for the preset infectious disease in the preset area during the preset time period; a detection rate determination module 303, which is used to determine the cumulative infection detection rate of the preset infectious disease in the preset area up to the preset time according to the number of confirmed cases in multiple stages and the number of infected people in the stages. Cumulative infection detection rate

[0067] Figure 4 : is a schematic diagram of the structure of a device provided by an embodiment of the present invention. At the hardware level, the device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the device may also include hardware required for other services.

[0068] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0069] The memory is used to store execution instructions. Specifically, the execution instructions are computer programs that can be executed. The memory may include internal memory and non-volatile memory, and provides execution instructions and data to the processor.

[0070] In one possible implementation, the processor reads the corresponding execution instructions from the non-volatile memory into the memory and then runs them, and can also obtain the corresponding execution instructions from other devices to form an infectious disease infection detection rate determination device at the logical level. The processor executes the execution instructions stored in the memory to implement the infectious disease infection detection rate determination method provided in any embodiment of the present invention through the execution of the execution instructions.

[0071] As described above, the present invention Figure 3 The method performed by the infectious disease infection detection rate determination device provided in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0072] The steps of the method disclosed in the embodiment of the present invention can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0073] The embodiment of the present invention further provides a readable medium, which stores an execution instruction. When the stored execution instruction is executed by a processor of an electronic device, the electronic device can execute the infectious disease infection detection rate determination method provided in any embodiment of the present invention, and is specifically used to execute the following Figure 1-2 The method shown.

[0074] The electronic device in each of the aforementioned embodiments may be a computer.

[0075] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods or computer program products. Therefore, the present invention may be implemented in the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware.

[0076] The various embodiments of the present invention are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0077] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0078] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for determining the infection detection rate of an infectious disease, characterized in that: The method comprises: Obtain the number of confirmed cases of a preset infectious disease at multiple stages corresponding to multiple preset time periods before a preset time in a preset area; According to the number of confirmed cases in the preset period, the corresponding number of infected people in the stage is determined by using the corresponding risk prediction model for the preset infectious disease in the preset area during the preset period; wherein the risk prediction model for the preset infectious disease during the preset period includes a COX regression model; Determine the cumulative infection detection rate of the preset infectious disease in the preset area up to the preset time according to the number of confirmed cases in the multiple stages and the number of infected people in the stages; Among them, the corresponding number of infected people in the stage is determined according to the number of confirmed cases in the preset time period, using the corresponding risk prediction model for the preset time period of the preset infectious disease in the preset area, including: according to the number of confirmed cases in the stage in the preset time period, using the risk prediction model for the preset time period of the preset infectious disease in the preset area, determining the cumulative probability of infected objects in the preset infection year being diagnosed in the preset time period and before the preset time period, wherein the predicted infection year is earlier than the preset time period; obtaining the number of confirmed patients from the predicted infection time period to the preset time period; and determining the number of infected people in the stage when the predicted infection time is the preset time period before the preset time period.

2. The method according to claim 1, characterized in that Determining the cumulative infection detection rate of the preset infectious disease in the preset area up to the preset time according to the number of confirmed cases in the multiple stages and the number of infected people in the stages, including: Determine the cumulative number of confirmed cases of the preset infectious disease in the preset area before the preset time according to the number of confirmed cases in the multiple stages; Determine the cumulative number of people infected with the preset infectious disease in the preset area before the preset time according to the number of people infected in multiple stages; The cumulative infection detection rate of the preset infectious disease in the preset area before the preset time is determined based on the cumulative number of confirmed cases and the cumulative number of infected people.

3. The method according to claim 1, characterized in that The risk prediction model for the preset infectious disease in the preset area and the preset time period is trained by the following operations: Obtaining sample parameters of a plurality of confirmed cases of a preset infectious disease in the preset area before the preset time; According to the sample parameters, determine the infection time of the confirmed cases corresponding to the sample parameters; Obtaining the influencing factors of the multiple confirmed cases; Constructing stage survival data for a preset period of time based on the infection time and diagnosis influencing factors of the multiple confirmed cases; Based on the stage survival data of the preset time period, a risk prediction model for the preset infectious disease in the preset area during the preset time period is trained.

4. The method according to claim 3, characterized in that Based on the infection time and influencing factors of the multiple confirmed cases, stage survival data for a preset period of time is constructed, including: Determine the following parameters of confirmed cases whose infection time is between or before the preset time period and whose diagnosis time is between or after the preset time period as stage survival data for the preset time period: Factors influencing diagnosis, monitoring duration, and whether the diagnosis was confirmed during the preset period; Among them, the monitoring duration is used to show the duration from the infection time to the preset time period of the confirmed case.

5. The method according to claim 3, characterized in that: The training of the risk prediction model for the preset infectious disease in the preset area during the preset period also includes: According to the risk prediction model obtained through training for the preset infectious disease in the preset area and the preset time period, verifying and adjusting the diagnosis influencing factors to obtain adjusted diagnosis influencing factors; Based on the adjusted confirmed influencing factors, the risk prediction model for the preset infectious disease in the preset area during the preset period is optimized.

6. A method and device for determining the infection detection rate of infectious diseases, characterized in that: The device comprises: An acquisition module, used to acquire the number of confirmed cases of a preset infectious disease at multiple stages in a preset area corresponding to multiple preset time periods before a preset time; An infection determination module, for determining the number of infected people in a corresponding stage according to the number of confirmed cases in a preset period of time, using a corresponding risk prediction model for a preset period of time for a preset infectious disease in the preset area; wherein the risk prediction model for a preset period of time for a preset infectious disease is a COX regression model; A detection rate determination module, used to determine the cumulative infection detection rate of the preset infectious disease in the preset area up to the preset time according to the number of confirmed cases in the multiple stages and the number of infected people in the stages; Among them, the infection determination module is specifically used to determine the cumulative probability of an infected object in a preset infection year being diagnosed in the preset time period and before the preset time period according to the number of confirmed cases in the stage in the preset time period, using the risk prediction model for the preset time period of the preset infectious disease in the preset area, wherein the predicted infection year is earlier than the preset time period; obtain the number of confirmed patients from the predicted infection time period to the preset time period; and determine the number of infected people in the stage when the predicted infection time is the preset time period before the preset time period.

7. The device according to claim 6, characterized in that The discovery rate determination module includes: A cumulative confirmed number determination submodule, for determining the cumulative number of confirmed persons who were diagnosed with the preset infectious disease before the preset time in the preset area according to the number of confirmed persons in the multiple stages; A cumulative infection determination submodule, used to determine the cumulative number of infected persons diagnosed with the preset infectious disease before the preset time in the preset area according to the number of infected persons in multiple stages; The cumulative infection detection rate determination submodule is used to determine the cumulative infection detection rate of the preset infectious disease in the preset area before the preset time based on the cumulative number of confirmed cases and the cumulative number of infected people.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for determining the infection detection rate of an infectious disease as described in any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for determining the infectious disease infection detection rate as described in any one of claims 1 to 5 is implemented.

Images