Basic regeneration number estimation method and device, storage medium and electronic equipment

Abstract

The present disclosure provides a basic reproduction number estimation method, a basic reproduction number estimation device, a computer readable storage medium and an electronic device; it relates to the technical field of data processing. The basic reproduction number estimation method comprises: obtaining epidemic data and a corresponding first basic reproduction number in a preset time period of epidemic development; obtaining epidemic data of a current stage, obtaining an estimation parameter according to the epidemic data in the preset time period and the epidemic data of the current stage; obtaining a second basic reproduction number corresponding to the current stage according to the estimation parameter and the first basic reproduction number, for epidemic evaluation based on the second basic reproduction number. The present disclosure can estimate the basic reproduction number of the current stage according to the data in the preset time period of the epidemic, so as to estimate and analyze the propagation status of the epidemic in time, thereby providing help for the prevention and control of the epidemic.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and more specifically, to a basic reproduction number estimation method, a basic reproduction number estimation device, a computer-readable storage medium, and an electronic device. Background Technology

[0002] Large-scale outbreaks of epidemics have an undeniable impact on all aspects of human society. Beyond direct effects such as casualties and medical losses, they also have negative indirect effects on the economy, public psychology, and social stability. For example, the 2003 SARS outbreak, the 2005 H1N1 avian influenza outbreak, the 2012 Middle East Respiratory Syndrome (MERS), and the 2019 novel coronavirus (2019-nCoV) pneumonia outbreak—each large-scale outbreak has caused significant losses to society as a whole and to individuals.

[0003] Appropriate epidemic prevention and control measures are crucial for mitigating losses, and accurate and timely predictions of the epidemic's development and transmission capacity are key to formulating appropriate measures. The epidemiological community has been attempting to gain more information from a dynamic perspective to explore the mechanisms of epidemic development, with the most critical parameter being the basic reproduction number (R0). The basic reproduction number refers to the expected number of secondary cases generated by a single (typical) infected individual in a susceptible population (i.e., those without immunity to the infectious disease virus) without human intervention; it represents the average number of people the disease will spread.

[0004] Using the aforementioned basic reproduction number to predict the development of a population epidemic has a certain degree of effectiveness. However, current technology cannot predict this basic reproduction number in a timely manner; it can only be estimated some time after the outbreak, making it impossible to assess the epidemic's development in a timely manner.

[0005] The information disclosed in the background section above is only for enhancing the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This disclosure provides a basic reproduction number estimation method, a basic reproduction number estimation device, a computer-readable storage medium, and an electronic device, which can estimate the basic reproduction number at the current stage based on data within a preset time period of the epidemic, thereby enabling timely prediction and analysis of the spread of the epidemic and providing assistance for epidemic prevention and control.

[0007] According to a first aspect of this disclosure, a basic reproduction number prediction method is provided, comprising:

[0008] Obtain epidemic data and the corresponding first basic reproduction number within a preset time period of epidemic development;

[0009] Obtain the current stage of the epidemic data, and obtain the estimated parameters based on the epidemic data within the preset time period and the current stage of the epidemic data;

[0010] Based on the estimated parameters and the first basic reproduction number, a second basic reproduction number corresponding to the current stage is obtained, which is used for epidemic assessment based on the second basic reproduction number.

[0011] In one exemplary embodiment of this disclosure, obtaining epidemic data and the corresponding first basic reproduction number within a preset time period of epidemic development includes:

[0012] Obtain the epidemic data within the preset time period, and obtain the first basic reproduction number based on the epidemic data.

[0013] In one exemplary embodiment of this disclosure, obtaining the estimated parameters based on the epidemic data within the preset time period and the epidemic data at the current stage includes:

[0014] Determine a first value for the product of infectivity, average contact rate, and duration of infectivity within a preset time period, and determine a second value for the product of infectivity, average contact rate, and duration of infectivity in the current stage.

[0015] The ratio between the first value and the second value is determined, and the ratio is used as the estimation parameter, which is used to represent the proportional relationship between the first basic reproduction number and the second basic reproduction number.

[0016] In one exemplary embodiment of this disclosure, determining the ratio between the first numerical value and the second numerical value includes:

[0017] Determine the ratio between the average contact rate within the preset time period and the average contact rate in the current stage.

[0018] In one exemplary embodiment of this disclosure, the epidemic data further includes close contact data;

[0019] Determining the ratio between the average contact rate within the preset time period and the current stage includes:

[0020] Acquire the close contact data within the preset time period and at the current stage, the close contact data including the number of confirmed patients and the number of close contacts;

[0021] Based on the number of confirmed cases and close contacts within the preset time period and the current stage, the average contact rate within the preset time period and the current stage is obtained, and the ratio between the average contact rates is calculated.

[0022] In one exemplary embodiment of this disclosure, the close contact data in the current stage and within the preset time period conform to a normal distribution;

[0023] The process of obtaining the estimated parameters based on the epidemic data within the preset time period and the epidemic data at the current stage includes:

[0024] Obtain close contact data within the preset time period and at the current stage, wherein the close contact data includes the number of confirmed cases and the number of close contacts;

[0025] Determine the normal distribution of the close contact data in the current stage, and the normal distribution of the close contact data within the preset time period;

[0026] The distribution corresponding to the ratio of the normal distribution within the preset time period and the current stage is calculated and used as the prediction parameter.

[0027] In one exemplary embodiment of this disclosure, obtaining the second basic regeneration number corresponding to the current stage based on the estimated parameters and the first basic regeneration number includes:

[0028] Calculate the confidence interval of the estimated parameter, and obtain the range of values ​​for the second basic reproduction number based on the confidence interval and the first basic reproduction number.

[0029] According to a second aspect of this disclosure, a medical basic regeneration number prediction device is provided, comprising:

[0030] The data acquisition module is used to acquire epidemic data and the corresponding first basic reproduction number within a preset time period of the epidemic's development.

[0031] The parameter estimation module is used to obtain the epidemic data at the current stage and to obtain the estimated parameters based on the epidemic data within the preset time period and the epidemic data at the current stage.

[0032] The data processing module is used to obtain the second basic reproduction number corresponding to the current stage based on the estimated parameters and the first basic reproduction number, so as to conduct an epidemic assessment based on the second basic reproduction number.

[0033] According to a third aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the preceding claims.

[0034] According to a fourth aspect of this disclosure, an electronic device is provided, comprising:

[0035] processor;

[0036] A memory for storing executable instructions of the processor; wherein the processor is configured to perform any of the methods described above by executing the executable instructions.

[0037] The exemplary embodiments disclosed herein may have some or all of the following beneficial effects:

[0038] In the basic reproduction number estimation method provided in the exemplary embodiments of this disclosure, epidemic data and corresponding first basic reproduction numbers within a preset time period of epidemic development are obtained; epidemic data at the current stage are obtained, and estimation parameters are obtained based on the epidemic data within the preset time period and the epidemic data at the current stage; a second basic reproduction number corresponding to the current stage is obtained based on the estimation parameters and the first basic reproduction number, and an epidemic assessment is performed based on the obtained second basic reproduction number. On the one hand, the basic reproduction number estimation method provided in the exemplary embodiments of this disclosure can estimate the second basic reproduction number at the current stage by using the epidemic data within the preset time period, the epidemic data at the current stage, and the first basic reproduction number within the preset time period. Therefore, the transmission capacity of the epidemic at the current stage can be assessed based on the second basic reproduction number, so as to estimate the stage of epidemic development and take appropriate epidemic prevention and control measures. On the other hand, the method provided in the exemplary embodiments of this disclosure can obtain the above-mentioned estimation parameters based on the epidemic data available at the current stage, and can obtain the second basic reproduction number at the current stage based on the estimation parameters and the first basic reproduction number, without requiring case data or confirmed case data. Therefore, the epidemic development status can be assessed at the early stage of the current stage, making data processing more timely and effective.

[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0041] Figure 1 A schematic diagram of an exemplary system architecture for a basic reproduction number estimation method and apparatus to which embodiments of the present disclosure can be applied is shown;

[0042] Figure 2 A schematic diagram of the structure of a computer system suitable for implementing embodiments of the present disclosure is shown;

[0043] Figure 3A flowchart illustrating the process of a basic reproduction number estimation method according to an embodiment of the present disclosure is shown schematically.

[0044] Figure 4 This schematically illustrates a flowchart of a method for calculating the ratio between the basic reproduction number within a preset time period and the current stage according to an embodiment of the present disclosure;

[0045] Figure 5 The flowchart illustrates a method for calculating the ratio of average contact rates according to an embodiment of the present disclosure.

[0046] Figure 6 The flowchart illustrates a method for calculating the ratio of average contact rates according to an embodiment of the present disclosure.

[0047] Figure 7 A block diagram of a basic reproduction number estimation apparatus according to an embodiment of the present disclosure is shown schematically. Detailed Implementation

[0048] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0049] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0050] Figure 1 A schematic diagram of a system architecture for an exemplary application environment in which a basic reproduction number estimation method and apparatus according to embodiments of the present disclosure can be applied is shown.

[0051] like Figure 1 As shown, system architecture 100 may include one or more of terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables. Terminal devices 101, 102, and 103 may be various electronic devices with displays, including but not limited to desktop computers, laptops, smartphones, and tablets. It should be understood that... Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, there can be any number of terminal devices, networks, and servers. For example, server 105 could be a server cluster composed of multiple servers.

[0052] The basic reproduction number estimation method provided in this embodiment can be executed by terminal devices 101, 102, and 103, and correspondingly, the basic reproduction number estimation device can be disposed in terminal devices 101, 102, and 103. The basic reproduction number estimation method provided in this embodiment can also be executed by server 105, and correspondingly, the basic reproduction number estimation device can be disposed in server 105. The basic reproduction number estimation method provided in this embodiment can also be jointly executed by terminal devices 101, 102, and 103 and server 105, and correspondingly, the basic reproduction number estimation device can be disposed in terminal devices 101, 102, and 103 and server 105. This exemplary embodiment does not impose any special limitations on this approach.

[0053] For example, in this exemplary embodiment, terminal devices 101, 102, and 103 can acquire epidemic data and the corresponding first basic reproduction number within a preset time period of epidemic development, as well as epidemic data at the current stage; and send the acquired epidemic data, the first basic reproduction number, and the epidemic data at the current stage to server 105 via network 104; after receiving the above data, the server obtains an estimated parameter based on the epidemic data within the preset time period and the epidemic data at the current stage, and obtains a second basic reproduction number corresponding to the current stage based on the estimated parameter and the first basic reproduction number, so as to conduct epidemic assessment based on the second basic reproduction number.

[0054] Figure 2 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of the present disclosure is shown.

[0055] It should be noted that, Figure 2 The computer system 200 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0056] like Figure 2 As shown, the computer system 200 includes a central processing unit (CPU) 201, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 202 or programs loaded from storage section 208 into random access memory (RAM) 203. The RAM 203 also stores various programs and data required for system operation. The CPU 201, ROM 202, and RAM 203 are interconnected via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0057] The following components are connected to I / O interface 205: an input section 206 including a keyboard, mouse, etc.; an output section 207 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 208 including a hard disk, etc.; and a communication section 209 including a network interface card such as a LAN card, modem, etc. The communication section 209 performs communication processing via a network such as the Internet. Drive 210 is also connected to I / O interface 205 as needed. Removable media 211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 210 as needed so that computer programs read from them can be installed into storage section 208 as needed.

[0058] In addition to direct harm such as casualties and medical losses, large-scale outbreaks of the epidemic can also have negative indirect impacts on the economy, public psychology, and social stability. Therefore, appropriate epidemic prevention and control measures are crucial to mitigating losses, and accurate and timely forecasting of the epidemic's development and transmission capacity is key to formulating appropriate epidemic prevention and control measures.

[0059] The basic reproduction number refers to the expected number of secondary cases generated by a single (typical) infected person in a susceptible population (i.e., those without immunity to the infectious disease virus) without human intervention; it represents the average number of people the disease spreads. Using the basic reproduction number to predict the development of a population outbreak has a certain degree of effectiveness.

[0060] However, current technology requires a large amount of epidemic data, such as confirmed case data, to calculate the basic reproduction number. This data is unavailable in the early stages of an epidemic. Therefore, current technology cannot predict the basic reproduction number in a timely manner, and cannot promptly assess the development of the epidemic.

[0061] In order to address the problems existing in the prior art, the inventors have proposed a new technical solution in this exemplary embodiment. The technical solution of this disclosure embodiment is described in detail below:

[0062] This example implementation first provides a basic reproduction number estimation method, which, as follows: Figure 3 As shown, the specific steps include:

[0063] Step S310: Obtain epidemic data and the corresponding first basic reproduction number within a preset time period of epidemic development;

[0064] Step S320: Obtain the current stage of epidemic data, and obtain the estimated parameters based on the epidemic data within the preset time period and the current stage of epidemic data;

[0065] Step S330: Obtain the second basic reproduction number corresponding to the current stage based on the estimated parameters and the first basic reproduction number, so as to conduct an epidemic assessment based on the second basic reproduction number.

[0066] In the basic reproduction number estimation method provided in the disclosed exemplary embodiments, on the one hand, the basic reproduction number estimation method provided in the disclosed exemplary embodiments can estimate the second basic reproduction number of the current stage by acquiring epidemic data within a preset time period, epidemic data at the current stage, and a first basic reproduction number within the preset time period. This allows for the assessment of the epidemic's transmissibility at the current stage based on the second basic reproduction number, in order to estimate the stage of epidemic development and take appropriate epidemic prevention and control measures. On the other hand, the method provided in the disclosed exemplary embodiments can obtain the above-mentioned estimation parameters based on the epidemic data available at the current stage, and can obtain the second basic reproduction number of the current stage based on the estimation parameters and the first basic reproduction number, without requiring morbidity data or confirmed case data. Therefore, it can assess the epidemic's development status at the early stage of the current phase, making data processing more timely and effective.

[0067] The above steps will now be described in more detail in another embodiment:

[0068] In step S310, the epidemic data and the corresponding first basic reproduction number within the preset time period of the epidemic development are obtained.

[0069] The basic reproduction number estimation method provided in this example implementation is applicable to scenarios where an epidemic has already gone through at least one outbreak phase, and various measures have been taken to control the epidemic within the capacity of medical resources. After a period of no new confirmed cases, the epidemic may re-emerge. The capacity of medical resources can be considered as the expectation that there will be zero confirmed cases within the next n days. The problem addressed by this example implementation is to predict the current stage of the epidemic's development, in order to anticipate whether medical resources are sufficient or what level of measures are needed to control a large-scale spread.

[0070] In this example implementation, the aforementioned preset time period refers to a period of epidemic development earlier than the current time, from outbreak to control within the capacity of medical resources. For example, suppose an epidemic breaks out in a region at time point A, and various prevention and control measures are subsequently implemented. At time point B, the epidemic is brought under control, meaning it falls within the capacity of medical resources. Then, the time period from time point A to time point B can be considered within the aforementioned preset time period. Because an epidemic usually does not suddenly break out or be controlled at a single moment, time points A and B refer to relatively short time ranges. Furthermore, it should be noted that the above scenario is merely an illustrative example, and the scope of protection of this example implementation is not limited thereto. For example, the aforementioned preset time period may not be the stage from the initial outbreak of the epidemic to its control, but may be any other time period that meets the above requirements.

[0071] In this example implementation, the aforementioned epidemic data refers to data that can characterize the development and impact of an epidemic. For example, the epidemic data may include data characterizing the impact of the virus causing the epidemic, such as infectivity τ and average contact rate. And the duration of infectivity, d. The infectivity τ represents the probability of infection occurring after contact between the exposed person and the infected person; the average contact rate... This represents the average contact rate between exposed individuals and infected individuals. The infectivity duration d is the infectious period of an infected individual, that is, the length of time the virus is infectious. An exposed individual refers to a person who has come into contact with an infected individual without taking any effective protective measures. Furthermore, the aforementioned epidemic data may also include statistical data used to assess the development of the epidemic, such as the number of confirmed cases and close contacts within a certain period. This example implementation does not impose specific limitations on this; other data that conforms to the above definitions fall within the scope of protection for epidemic data in this example implementation.

[0072] In this example implementation, the basic reproduction number can be understood as the number of secondary cases generated by a single infected person among those exposed (i.e., people without immunity to the infectious disease virus) in the absence of human intervention, i.e., the expected value of the average number of people infected. The basic reproduction number, as a comprehensive parameter, describes the infectivity or transmissibility of an infectious agent, reflecting the combined result of the virus and human activity. Before humans take active measures, it reflects the epidemic's spread capacity under normal social activity. The first basic reproduction number is the basic reproduction number corresponding to a predetermined time period in the development of the epidemic.

[0073] The basic reproduction number is primarily affected by infectivity, average contact rate, and duration of infectivity. For example, the number of exposed individuals can be rapidly reduced by isolating population movement, and the basic reproduction number can be lowered by reducing the average contact rate. It should be noted that the above scenario is merely an illustrative example, and the scope of protection of this exemplary implementation is not limited thereto.

[0074] In this example implementation, since the development of the epidemic is usually recorded, the epidemic data within the aforementioned preset time period and the corresponding first basic reproduction number within that preset time period can be obtained by consulting relevant epidemic information. Furthermore, the aforementioned epidemic data can also be obtained through statistics, and after obtaining the relevant statistical data of the epidemic data, the first basic reproduction number within the preset time period can be estimated based on the statistical data. Specifically, the basic reproduction number can be calculated using the following formula:

[0075]

[0076] Among them, τ, d and d represent the infectivity, average contact rate, and duration of infectivity data obtained through statistical and calculation processes, respectively.

[0077] It should be noted that the above scenario is only an illustrative example and does not limit the implementation of this example. Other methods for obtaining the above-mentioned epidemic data and the first basic reproduction number also fall within the protection scope of this example.

[0078] In step S320, the current stage of the epidemic data is obtained, and the estimated parameters are obtained based on the epidemic data within a preset time period and the current stage of the epidemic data.

[0079] In this example implementation, the current stage refers to the current stage of a resurgence of the epidemic after a certain preset time period. For example, suppose an epidemic breaks out in a certain region and, through a series of effective measures, controls the epidemic within the capacity of medical resources for a period of time. This time period is a preset time period. After a period of no new confirmed cases, new local cases reappear at a certain time and continue to develop until the epidemic is controlled. The time period from the resurgence of the epidemic to the present and until the epidemic is controlled in the future belongs to the aforementioned current stage. It should be noted that the above scenario is only an illustrative example, and the scope of protection of this example implementation is not limited thereto. For example, the preset time period is not necessarily the preset time period of the initial outbreak of the epidemic; other stages that occur before the current stage and fall within the preset time period also fall within the scope of protection of this example implementation.

[0080] At the current stage of the epidemic's development, in order to prepare for epidemic prevention and control, such as predicting the adequacy of medical resources and determining the appropriate level of control measures to prevent a large-scale spread of the epidemic, it is necessary to assess the current stage of the epidemic's development. This assessment requires the use of the current basic reproduction number. To obtain the current basic reproduction number, it is first necessary to derive predictive parameters based on epidemic data from a preset time period and the current stage's epidemic data.

[0081] In this example implementation, the aforementioned current-stage epidemic data are statistical data used to characterize the current stage of the epidemic's development. For example, they may include the number of confirmed cases and close contacts, and may also include other data conforming to the above definitions. The aforementioned forecasting parameters are parameters used to forecast the basic reproduction number for the current stage.

[0082] For example, the aforementioned estimated parameters can be obtained through the following process: determining a first value for the product of infectivity, average contact rate, and duration of infectivity within a preset time period, and determining a second value for the product of infectivity, average contact rate, and duration of infectivity in the current stage; determining the ratio between the first value and the second value, and using the obtained ratio as the aforementioned estimated parameters. The estimated parameters represent the proportional relationship between the first basic reproduction number and the second basic reproduction number.

[0083] The ratio between the first and second values ​​can be obtained by determining the ratio between the average contact rates within a preset time period and the current stage. Specifically, this may include, for example... Figure 4 The steps shown below, in conjunction with Figure 4 The process is explained below:

[0084] In step S410, the ratio between the average contact rate and the basic number of regenerations is obtained.

[0085] In this example implementation, the process of obtaining the ratio between the average contact rate and the basic reproduction number can be as follows: based on the correlation between the epidemic data and the basic reproduction number, the ratio between the average contact rate and the basic reproduction number is obtained.

[0086] Specifically, the above proportional relationship is expressed by the following formula mentioned in step S310:

[0087]

[0088] This formula shows a linear relationship between the basic reproduction number and infectivity, average contact rate, and duration of infectivity. Since the same type of virus causes the same outbreak, their characteristics are essentially the same. Therefore, the infectivity and duration of infectivity at different stages of the outbreak are also essentially the same (assuming there are currently no drugs or vaccines to prevent this virus). Given a fixed infectivity and duration of infectivity, the basic reproduction number and average contact rate are linearly proportional. That is, the ratio of the basic reproduction number within a predetermined time period to the current stage is approximately equal to the ratio of the average contact rate within the predetermined time period to the current stage.

[0089] In step S420, the ratio between the average contact rate within the preset time period and the average contact rate in the current stage is calculated.

[0090] In this example implementation, after obtaining the ratio between the average contact rate and the basic reproduction number, in order to obtain the ratio between the basic reproduction number within the preset time period and the current stage, it is also necessary to calculate the ratio between the average contact rate within the preset time period and the current stage. For example, this process can be as follows: calculate the ratio between the average contact rate within the preset time period and the current stage based on the epidemic data within the preset time period and the epidemic data of the current stage.

[0091] Specifically, the process for calculating the ratio of average contact rates described above is as follows: Figure 5 As shown, it may include the following steps:

[0092] In step S510, close contact data within a preset time period and at the current stage are obtained, wherein the close contact data includes the number of confirmed patients and the number of close contacts.

[0093] In step S520, based on the number of confirmed cases and close contacts within the preset time period and the current stage, the average contact rate within the preset time period and the current stage is obtained, and the ratio between the average contact rates is calculated.

[0094] In this example implementation, the aforementioned average contact rate is the average contact rate between confirmed patients and close contacts. Therefore, the average contact rate can be estimated by dividing the statistical data of confirmed patients and close contacts. For example, if the number of confirmed patients over 5 days is 52 and the number of close contacts is 202, then the average contact rate for these 5 days is 3.885. It should be noted that the above scenario is only an illustrative example, and the scope of protection of this example implementation is not limited thereto.

[0095] To ensure accuracy when calculating the ratio between the average contact rate within a preset time period and the current stage, it is advisable to use statistical data from the same period for both periods. For example, statistical data from 5 days within the preset time period can be used, as shown in Table 1.

[0096] Table 1:

[0097] date Number of confirmed cases Number of close contacts 2020 / 1 / 1 4 24 2020 / 1 / 2 6 23 2020 / 1 / 3 10 53 2020 / 1 / 4 12 42 2020 / 1 / 5 20 60

[0098] Table 1 includes the dates, number of confirmed cases, and number of close contacts for the statistical data. Based on the data in Table 1, the total number of confirmed cases over these 5 days is 52, and the total number of close contacts is 202. Therefore, the average contact rate for these 5 days is 3.885.

[0099] Next, we also take 5 days of statistical data for the current stage, as shown in Table 2:

[0100] Table 2

[0101] date Number of confirmed cases Number of close contacts 2020 / 6 / 15 12 60 2020 / 6 / 16 14 100 2020 / 6 / 17 16 121 2020 / 6 / 18 20 140 2020 / 6 / 19 25 165

[0102] The data in Table 2 shows a total of 87 confirmed cases and 456 close contacts over these 5 days. The average contact rate over these 5 days is 5.241, and the ratio of the average contact rate during the preset time period to the current stage is approximately 1.35. It should be noted that the above scenario is merely an illustrative example, and the scope of protection of this example implementation is not limited thereto.

[0103] In this example implementation, in order to obtain a more accurate result of the basic regeneration number in the current stage, the ratio between the average contact rate in the aforementioned preset time period and the average contact rate in the current stage can also be calculated by the distribution of close contact data.

[0104] Typically, the event of a confirmed patient coming into contact with close contacts follows a random distribution. For example, assuming that the average number of people in close contact with each confirmed patient each day follows a normal distribution, the process described above, which calculates the ratio between the average contact rate over a preset time period and the current stage based on the distribution of close contact data, is as follows: Figure 6 As shown, it may include the following steps:

[0105] In step S610, close contact data within a preset time period and at the current stage are obtained. This close contact data includes the number of confirmed patients and the number of close contacts.

[0106] In step S620, the normal distribution of close contact data in the current stage and the normal distribution of close contact data within a preset time period are determined.

[0107] In this example implementation, the process of determining the normal distribution within the preset time period and the current stage can be as follows: take the statistical data of confirmed patients and close contacts with the same time period within the preset time period and the current stage; obtain the average value and standard deviation of the daily average number of contacts within the preset time period and the current stage based on the above statistical data; and calculate the normal distribution corresponding to the preset time period and the current stage based on the average value and standard deviation.

[0108] In step S630, the distribution corresponding to the ratio of the normal distribution within the preset time period and the current stage is calculated and used as the above-mentioned prediction parameter.

[0109] In this example implementation, this step is used to calculate the ratio of the current stage and the normal distribution within the preset time period obtained through step S620. This ratio is also a distribution, and the ratio is used as the above-mentioned prediction parameter.

[0110] In step S430, the ratio between the basic number of regenerations within the preset time period and the current stage is obtained based on the proportional relationship between the average contact rate and the basic number of regenerations, and the ratio between the average contact rate within the preset time period and the average contact rate within the current stage.

[0111] In this example embodiment, the ratio between the average contact rate and the basic regeneration number obtained through step S410 is a linear relationship, and the ratio between the basic regeneration number within the preset time period and the current stage is approximately equal to the ratio between the average contact rate within the preset time period and the current stage. Therefore, in this step, the ratio between the average contact rates obtained in step S420 can be used as the ratio between the basic regeneration numbers.

[0112] It should be noted that the above scenario is only an illustrative example and does not limit the implementation of this example. For example, other estimation parameters that can play the same role, as well as other methods for calculating the average contact rate and estimation parameters, are also within the protection scope of this example.

[0113] In step S330, a second basic reproduction number corresponding to the current stage is obtained based on the estimated parameters and the first basic reproduction number, so as to be used for epidemic assessment based on the second basic reproduction number.

[0114] In this example implementation, the second basic regeneration number is the basic regeneration number corresponding to the current stage. The basic regeneration number obtained in this step can be a single value or a range of values. When the estimated parameter is the ratio of the average contact rate of the current stage to the average contact rate within a preset time period, the process of obtaining the second basic regeneration number corresponding to the current stage based on the estimated parameter and the first basic regeneration number can be as follows: multiply the first basic regeneration number and the estimated parameter to obtain the second basic regeneration number.

[0115] For example, when the average contact rate between the current stage and the preset time period is calculated using the process from steps S510 to S520, i.e., the estimated parameter, the first basic regeneration number and the estimated parameter are multiplied together to obtain a single value. For instance, when the ratio of the average contact rate between the current stage and the preset time period is 1.35, and the first basic regeneration number within the preset time period is 1.5, the second basic regeneration number obtained is 2.025.

[0116] When the average contact rate with the current stage within a preset time period, i.e., the estimated parameter, is calculated using steps S610 to S630, this estimated parameter is a distribution, and the resulting second basic reproduction number is a range of values. Specifically, this can be achieved as follows: calculate the confidence interval of the above estimated parameter, and obtain the range of values ​​for the second basic reproduction number based on the confidence interval and the first basic reproduction number. Taking a confidence interval of 95% as an example, the ratio of the second basic reproduction number to the first basic reproduction number, i.e., the estimated parameter R0c, conforms to the following range:

[0117] μ c -1.96*θ C <R0c<μ c +1.96*θ C

[0118] Where, μ c and θ C The mean and standard deviation of the distribution corresponding to the estimated parameter R0c are given. By multiplying the estimated parameter R0c and the first basic reproduction number, the range of values ​​for the second basic reproduction number can be obtained.

[0119] It should be noted that the above scenario is only an illustrative example, and the scope of protection of this example implementation is not limited thereto.

[0120] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0121] Furthermore, this example embodiment provides a basic reproduction number estimation apparatus. (See reference...) Figure 7 As shown, the basic reproduction number prediction device 700 may include a data acquisition module 710, a parameter estimation module 720, and a data processing module 730. Wherein:

[0122] The data acquisition module 710 can be used to acquire epidemic data and the corresponding first basic reproduction number within a preset time period of the epidemic's development.

[0123] The parameter estimation module 720 can be used to obtain the current stage of epidemic data and obtain the estimated parameters based on the epidemic data within a preset time period and the current stage of epidemic data;

[0124] The data processing module 730 can be used to obtain the second basic reproduction number corresponding to the current stage based on the estimated parameters and the first basic reproduction number, so as to conduct an epidemic assessment based on the second basic reproduction number.

[0125] In this exemplary embodiment, the data acquisition module may include an acquisition unit and a calculation unit. In one scenario, the acquisition unit is used to acquire epidemic data and a first basic reproduction number within a preset time period. In another scenario, the data acquisition module acquires epidemic data within the preset time period through the acquisition unit, and estimates the first basic reproduction number based on the acquired epidemic data through the calculation unit.

[0126] In this example embodiment, the parameter estimation module includes an acquisition unit and an estimation unit. The estimation unit is used to acquire the current stage of epidemic data, and the estimation unit is used to obtain estimated parameters based on the epidemic data within a preset time period and the current stage of epidemic data.

[0127] The specific details of each module or unit in the aforementioned basic reproduction number prediction device have been described in detail in the corresponding basic reproduction number prediction method, so they will not be repeated here.

[0128] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0129] On the other hand, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments. For example, the electronic device may perform... Figures 3-6 The various steps shown are as follows.

[0130] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0131] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A basic reproduction number prediction method for an epidemic, used in scenarios of a resurgence of the epidemic, characterized in that, include: The server receives epidemic data from terminal devices within a preset time period and at the current stage. The epidemic data includes infectivity, average contact rate, and duration of infectivity. The epidemic data within the preset time period and at the current stage are acquired by the terminal device; The server determines the first basic reproduction number based on the infectivity, average contact rate, and duration of infectivity within a preset time period. The server obtains estimated parameters based on the infectivity, average contact rate, and duration of infectivity within the preset time period and the infectivity, average contact rate, and duration of infectivity in the current stage. These estimated parameters represent the proportional relationship between the first basic reproduction number and the second basic reproduction number. Obtaining the estimated parameters includes: determining a first value representing the product of the infectivity, average contact rate, and duration of infectivity within the preset time period; determining a second value representing the product of the infectivity, average contact rate, and duration of infectivity in the current stage; determining the ratio between the first value and the second value; and using this ratio as the estimated parameter. The server obtains the second basic reproduction number corresponding to the current stage based on the estimated parameters and the first basic reproduction number, so as to conduct an epidemic assessment based on the second basic reproduction number.

2. The method for predicting the basic reproduction number of an epidemic according to claim 1, characterized in that, Determining the ratio between the first value and the second value includes: Determine the ratio between the average contact rate within the preset time period and the average contact rate in the current stage.

3. The method for predicting the basic reproduction number of an epidemic according to claim 2, characterized in that, The epidemic data also includes close contact data; Determining the ratio between the average contact rate within the preset time period and the current stage includes: Acquire the close contact data within the preset time period and at the current stage, the close contact data including the number of confirmed patients and the number of close contacts; Based on the number of confirmed cases and close contacts within the preset time period and the current stage, the average contact rate within the preset time period and the current stage is obtained, and the ratio between the average contact rates is calculated.

4. The method for predicting the basic reproduction number of an epidemic according to claim 1, characterized in that, The epidemic data includes close contact data during the current stage and within the preset time period, and the close contact data conforms to a normal distribution; The estimated parameters can also be obtained in the following ways: Obtain close contact data within the preset time period and at the current stage, wherein the close contact data includes the number of confirmed cases and the number of close contacts; Determine the normal distribution of the close contact data in the current stage, and the normal distribution of the close contact data within the preset time period; The distribution corresponding to the ratio of the normal distribution within the preset time period and the current stage is calculated and used as the prediction parameter.

5. The method for predicting the basic reproduction number of an epidemic according to claim 4, characterized in that, The step of obtaining the second basic regeneration number corresponding to the current stage based on the estimated parameters and the first basic regeneration number includes: Calculate the confidence interval of the estimated parameter, and obtain the range of values ​​for the second basic reproduction number based on the confidence interval and the first basic reproduction number.

6. A device for predicting the basic reproduction number of an epidemic, characterized in that, include: The data acquisition module is used to acquire epidemic data within a preset time period and at the current stage through a terminal device. The epidemic data includes infectivity, average contact rate, and duration of infectivity. The first basic reproduction number is determined based on the infectivity, average contact rate, and duration of infectivity within a preset time period. The parameter estimation module is used to obtain estimated parameters based on the infectivity, average contact rate, and duration of infectivity within the preset time period and the infectivity, average contact rate, and duration of infectivity in the current stage. The estimated parameters are used to represent the proportional relationship between the first basic reproduction number and the second basic reproduction number. Obtaining the estimated parameters includes: determining a first value for the product of infectivity, average contact rate, and duration of infectivity within the preset time period; determining a second value for the product of infectivity, average contact rate, and duration of infectivity in the current stage; determining the ratio between the first value and the second value; and using the ratio as the estimated parameters. The data processing module is used to obtain the second basic reproduction number corresponding to the current stage based on the estimated parameters and the first basic reproduction number, so as to conduct an epidemic assessment based on the second basic reproduction number.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the basic reproduction number prediction method for an epidemic as described in any one of claims 1-5.

8. An electronic device, characterized in that, include: processor; Memory for storing the executable instructions of the processor; The processor is configured to execute the basic reproduction number estimation method for an epidemic as described in any one of claims 1-5 by executing the executable instructions.

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