Epidemic new case prediction method and device, electronic equipment and readable storage medium

Abstract

The present disclosure provides an epidemic new case prediction method and device, electronic equipment and computer readable storage medium, the method comprises: obtaining the daily new case number and the daily effective number of regenerative number of the target region in the target time for the target epidemic, the target time is one target propagation period before the first time; according to the daily new case number, the daily effective number of regenerative number and the target propagation period of the target epidemic in the target time, the daily new case number of the first time is determined. The technical scheme provided by the embodiments of the present disclosure can obtain the new case number of the epidemic at the next time under the target time.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of computer and Internet technology, and particularly relates to a method and device for predicting the number of new cases of an epidemic, an electronic device, and a readable storage medium. BACKGROUND

[0002] The new coronavirus is the most serious respiratory disease threat to public health since the 1918 H1N1 influenza global pandemic. Different countries or regions may take different prevention and control measures against the new coronavirus.

[0003] However, the prevention and control measures taken depend largely on the daily increase in the number of people in the country or region (if the daily increase in the number of people is too much, more stringent prevention and control measures need to be taken; if the daily increase in the number of people is very small, the current prevention and control measures need to be weakened).

[0004] Therefore, a method that can accurately estimate the future daily increase in the number of people is crucial to the control of the new coronavirus.

[0005] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure. SUMMARY

[0006] The present disclosure provides a method and device for predicting the number of new cases of an epidemic, an electronic device, and a computer readable storage medium, which can determine the daily increase in the number of people in a target region at a first time for a target epidemic.

[0007] Other characteristics and advantages of the present disclosure will become apparent from the following detailed description, or will be learned by practice of the present disclosure.

[0008] The present disclosure provides a method for predicting the number of new cases of an epidemic, which comprises: obtaining the daily increase in the number of people and the daily effective number of regenerations in a target region for a target epidemic within a target time, the target time being a target propagation period before a first time; determining the daily increase in the number of people at the first time according to the daily increase in the number of people, the daily effective number of regenerations within the target time, and the target propagation period of the target epidemic.

[0009] In some embodiments, the target time includes a first target moment; and the obtaining the daily effective number of people reproduction number of the target area at the first target moment for the target epidemic includes: obtaining a first prevention and control measure combination implemented by the target area at the first target moment and a target moment of implementing the first prevention and control measure combination; obtaining an attribute feature of the target area at the target moment; and determining the daily effective number of people reproduction number of the target area at the first target moment for the target epidemic according to the attribute feature of the target area at the target moment and the first prevention and control measure combination.

[0010] In some embodiments, the determining the daily effective number of people reproduction number of the target area at the first target moment for the target epidemic according to the attribute feature of the target area at the target moment and the first prevention and control measure combination includes: obtaining a basic number of people reproduction number of the target area for the target epidemic; determining a target influence value of each prevention and control measure in the first prevention and control measure combination on the prevention and control of the target epidemic at the first target moment according to the attribute feature of the target area and the first prevention and control measure combination; and determining the daily effective number of people reproduction number of the target area at the first target moment for the target epidemic according to the basic number of people reproduction number and the target influence value.

[0011] In some embodiments, the target time includes a second target moment; and the determining the daily new number of people at the first moment according to the daily new number of people, the daily effective number of people reproduction number in the target time, and a target propagation period of the target epidemic includes: determining a daily new propagation number of people in the target time according to the daily new number of people, the daily effective number of people reproduction number in the target time, and the target propagation period of the target epidemic, the daily new propagation number of people describing a number of people propagating at the first moment among the daily new number of people in the target time; and determining the daily new number of people at the first moment according to the daily new propagation number of people in the target time.

[0012] In some embodiments, the determining the daily new number of people at the first moment according to the daily new propagation number of people in the target time includes: summing the daily new propagation number of people in the target time to determine the daily new number of people at the first moment.

[0013] In some embodiments, the epidemic new case number prediction method can further include: determining daily new case numbers of the target region at the first time point and in the future N days according to the daily new case number at the first time point, N being a positive integer greater than or equal to 1; obtaining a discharge cycle of in-hospital patients of the target region and a number of in-hospital patients of the target region at the first time point; determining daily in-hospital patient numbers of the target region at the first time point and in the future N days according to the discharge cycle, the number of in-hospital patients at the first time point, and the daily new case numbers at the first time point and in the future N days; and determining a target time point at which the target medical resource range is first exceeded according to the daily in-hospital patient numbers at the first time point and in the future N days, so as to adjust the prevention and control measures of the target region at the target time point.

[0014] In some embodiments, the target medical resource range includes a lower limit of medical resources; and determining the target time point at which the target medical resource range is first exceeded according to the daily in-hospital patient numbers at the first time point and in the future N days, so as to adjust the prevention and control measures of the target region at the target time point, includes: if the number of in-hospital patients of the target region at the target time point is lower than the lower limit of medical resources, obtaining a second combination of prevention and control measures executed by the target region at the first time point; canceling prevention and control measures from the second combination of prevention and control measures according to the sizes of economic impacts of the prevention and control measures on the target region to obtain a first effective combination of prevention and control measures, and the number of in-hospital patients of the target region within a specified time under the intervention of the first effective combination of prevention and control measures is within the target medical resource range.

[0015] In some embodiments, the target medical resource range includes an upper limit of medical resources; and determining the target time point at which the target medical resource range is first exceeded according to the daily in-hospital patient numbers at the first time point and in the future N days, so as to adjust the prevention and control measures of the target region at the target time point, includes: if the number of in-hospital patients of the target region at the target time point exceeds the upper limit of medical resources, obtaining target prevention and control measures available for the target region; adjusting the prevention and control measures of the target region according to the sizes of economic impacts of the target prevention and control measures on the target region to obtain a second effective combination of prevention and control measures, and the number of in-hospital patients of the target region within a specified time under the intervention of the second effective combination of prevention and control measures is within the target medical resource range.

[0016] The embodiments of the present disclosure provide an epidemic new case number prediction device, which can include a data acquisition module and a new case number determination module.

[0017] The data acquisition module can be configured to acquire daily newly added number of people and daily effective number of regenerative number of people in the target region for the target epidemic at a target time, and the target time includes a first time and a target propagation period before the first time. The newly added number of people determination module can be configured to determine the daily newly added number of people at the first time according to the daily newly added number of people, the daily effective number of regenerative number of people in the target time, and a target propagation period of the target epidemic.

[0018] The electronic device includes one or more processors, and a storage device configured to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting the newly added number of people of an epidemic as described in any of the above.

[0019] The electronic device includes one or more processors, and a storage device configured to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting the newly added number of people of an epidemic as described in any of the above.

[0020] The electronic device includes one or more processors, and a storage device configured to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting the newly added number of people of an epidemic as described in any of the above.

[0021] The electronic device includes one or more processors, and a storage device configured to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting the newly added number of people of an epidemic as described in any of the above.

[0022] It should be understood that the above general description and the following detailed description are only exemplary and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0023] 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. The drawings described below are merely some embodiments of this disclosure, and those skilled in the art will be able to derive other drawings from these drawings without any inventive effort.

[0024] Figure 1 A schematic diagram of an exemplary system architecture for a method or apparatus for predicting new COVID-19 cases that can be applied to embodiments of this disclosure is shown.

[0025] Figure 2 This is a schematic diagram of the structure of a computer system applied to a device for predicting the number of new COVID-19 cases, according to an exemplary embodiment.

[0026] Figure 3 This is a flowchart illustrating a method for predicting the number of new COVID-19 cases according to an exemplary embodiment.

[0027] Figure 4 yes Figure 3 The flowchart of step S1 in an exemplary embodiment.

[0028] Figure 5 yes Figure 4 The flowchart of step S13 in an exemplary embodiment.

[0029] Figure 6 This is a method for determining the timing of adjustments to prevention and control measures based on the previously reported number of new cases, as illustrated in an example embodiment.

[0030] Figure 7 yes Figure 6 The flowchart of step S7 in an exemplary embodiment.

[0031] Figure 8 This is an exemplary embodiment illustrating the number of countries implementing various prevention and control measures and the corresponding time periods.

[0032] Figure 9 yes Figure 6 The flowchart of step S7 in an exemplary embodiment.

[0033] Figure 10 This is a block diagram illustrating a device for predicting the number of new COVID-19 cases according to an exemplary embodiment. Detailed Implementation

[0034] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0035] The features, structures, or characteristics described in this disclosure can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough 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 specific details omitted, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0036] The accompanying drawings are merely illustrative of this disclosure, and the same reference numerals in the drawings denote the same or similar parts, thus omitting repeated descriptions of them. Some block diagrams shown in the drawings 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.

[0037] The flowchart shown in the accompanying drawings is merely illustrative and does not necessarily include all content and steps, nor does it require execution in the described order. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0038] In this specification, the terms “a,” “an,” “the,” “the,” and “at least one” are used to indicate the presence of one or more elements / components / etc.; the terms “comprising,” “including,” and “having” are used to indicate an open-ended inclusion and to mean that there may be other elements / components / etc. in addition to the listed elements / components / etc.; the terms “first,” “second,” and “third,” etc., are used only as markings and are not a limitation on the number of objects.

[0039] The exemplary embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0040] Figure 1 A schematic diagram of an exemplary system architecture for a method or apparatus for predicting new COVID-19 cases that can be applied to embodiments of this disclosure is shown.

[0041] likeFigure 1 As shown, system architecture 100 may include 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 or wireless communication links, or fiber optic cables, etc.

[0042] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, desktop computers, wearable devices, virtual reality devices, smart home devices, etc.

[0043] Server 105 can be a server that provides various services, such as a backend management server that supports the devices operated by users using terminal devices 101, 102, and 103. The backend management server can analyze and process received requests and other data, and feed the processing results back to the terminal devices.

[0044] Server 105 may, for example, obtain the daily new cases and daily effective reproduction number of the target epidemic in the target region within a target time, wherein the target time is a target transmission cycle before the first moment; Server 105 may, for example, determine the daily new cases at the first moment based on the daily new cases, daily effective reproduction number and the target transmission cycle of the target epidemic within the target time; Server 105 may, for example, determine the daily new cases at the first moment based on the daily new transmission number within the target time.

[0045] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Server 105 can be a single physical server or a combination of multiple servers. Depending on actual needs, it can have any number of terminal devices, networks, and servers.

[0046] The following is for reference. Figure 2 It shows a schematic diagram of the structure of a computer system 200 suitable for implementing a terminal device according to the embodiments of this application. Figure 2 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0047] like Figure 2As 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 the operation of the system 200. 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.

[0048] 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.

[0049] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 209, and / or installed from removable medium 211. When the computer program is executed by central processing unit (CPU) 201, it performs the functions defined above in the system of this application.

[0050] It should be noted that the computer-readable storage medium shown in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can 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 application, a computer-readable storage medium can 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 application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can 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 storage medium other than a computer-readable storage medium, 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 storage medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0051] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0052] The modules and / or units described in the embodiments of this application can be implemented in software or hardware. The described modules and / or units can also be housed in a processor; for example, a processor can be described as including a sending unit, an acquiring unit, a determining unit, and a first processing unit. The names of these modules and / or units do not, in certain circumstances, constitute a limitation on the module and / or unit itself.

[0053] In another aspect, this application also provides a computer-readable storage medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable storage medium carries one or more programs, which, when executed by the device, enable the device to perform the following functions: acquiring the daily new cases and daily effective reproduction number of a target epidemic in a target region within a target time period, wherein the target time period is a target transmission cycle prior to a first moment; and determining the daily new cases at the first moment based on the daily new cases, daily effective reproduction number, and the target transmission cycle of the target epidemic within the target time period.

[0054] Figure 3 This is a flowchart illustrating a method for predicting the number of new COVID-19 cases according to an exemplary embodiment. The method provided in this disclosure can be processed by any electronic device with computing power, such as the one described above. Figure 1 In the embodiments, server 105 and / or terminal devices 102 and 103 are used as examples in the following embodiments, but this disclosure is not limited thereto.

[0055] Reference Figure 3 The method for predicting the number of new COVID-19 cases provided in this embodiment may include the following steps.

[0056] In step S1, the daily number of new cases and the daily number of effective cases in the target region within the target time period are obtained, where the target time is a target transmission cycle before the first moment.

[0057] In some embodiments, the target area may refer to a country, a region, or an administrative region of a country, such as a province, city, county, autonomous region, or municipality. Any country or region that can implement prevention and control measures is within the scope of protection of this disclosure, and this disclosure does not impose any restrictions on it.

[0058] In some embodiments, the first moment can refer to a future day, and the target time can refer to a target transmission period prior to the first moment. For example, if the target transmission period for the target epidemic is 10 days, and the first moment is June 15, then the target time can be the period from June 5 to June 14.

[0059] In some embodiments, if the target time is a period that has already occurred, then the number of new cases per day during the target time can be known; if the target time is entirely a period that has not yet occurred or partly a period that has not yet occurred, then the number of new cases per day during the period that has not yet occurred during the target time can be predicted by a certain prediction model. For example, it can be predicted by the technical solution provided in this embodiment, or by infectious disease models such as the SIR model (S represents susceptible individuals, I represents infected individuals, and R represents removers) and the SEIR model (S represents susceptible individuals, E represents latent individuals, I represents infected individuals, and R represents removers). This disclosure does not limit this.

[0060] In some embodiments, if the number of new daily users within a target time period is determined, then the number of effective users regenerated each day within the target time period (i.e., the number of effective users regenerated each day) can also be determined. For example, the number of new daily users within the target time period can be calculated using the maximum likelihood method to obtain the number of effective users regenerated each day within the target time period.

[0061] In some embodiments, the effective number of regenerated individuals Rt at time t may refer to the average number of people that an infectious patient can infect within a target transmission cycle T at time t.

[0062] In step S2, the number of new daily cases at the first moment is determined based on the number of new daily cases, the number of new daily cases within the target time period, and the target transmission cycle of the target epidemic.

[0063] In some embodiments, the number of new daily infections within a target time period can be determined based on the number of new daily infections, the number of effective daily infections, and the target transmission cycle of the target epidemic. The number of new daily infections describes the number of people infected by the new daily infections within the target time period at the first moment. Then, the number of new daily infections at the first moment can be determined based on the number of new daily infections within the target time period.

[0064] Generally speaking, the number of new cases at the first moment can be considered to be related only to the number of new cases within the previous target transmission cycle (target time), and not to the total number of confirmed cases before that target transmission cycle (target time). Understandably, only patients infected within the target time will affect the number of new cases at the first moment, while patients infected before the target transmission cycle (target time) have already been isolated or treated, so those infected before that target transmission cycle (target time) will not affect the number of new cases at the first moment.

[0065] In some embodiments, the target time includes a second target moment. It is understood that the second target moment can refer to any moment within the target time. Here, we only use the second target moment as an example to explain how to determine the number of new daily transmissions for each day.

[0066] In some embodiments, the daily new cases in the target region at the second target time can be determined based on the daily new cases, the daily effective number of reproductions, and the target propagation cycle corresponding to the second target time.

[0067] Specifically, this can be achieved by: determining the target number of new daily cases at the second target time within the first transmission cycle based on the effective population reproduction number, wherein the first transmission cycle includes the second target time; and determining the daily new cases at the second target time based on the value of the target transmission cycle and the target number. Here, the first transmission cycle can refer to a transmission cycle of a target epidemic starting from the second target time.

[0068] For example, based on the formula Base t *R t Calculate the daily new cases at the second target time (which could be day t within the target time) and the total number of people the virus can spread to during the first propagation cycle. Then, use formula N... t =Base t *R t / T determines the daily new transmission numbers N at the second target time. t Among them, N t Base represents the daily new transmission count at the second target time (i.e., the number of people that the new transmission count at the second target time can infect). t R represents the number of new cases on the day of the second target time (which could be day t within the target time period) (this data is known). t This represents the daily effective number of people regenerated at the second target time (which could be day t within the target time period), and T represents the target propagation period.

[0069] In some embodiments, the number of new infections for each day within a target time period can be calculated using the process described above. It is understood that since the number of confirmed cases prior to the target time does not affect the number of new infections at the first moment, the daily number of new infections here refers to the number of infections determined based on the number of new infections on that day.

[0070] In some embodiments, it can be done through formula The daily new number of people spreading the virus within the target time period is summed to determine the daily new number of people N at the first moment, where t represents the t-th day within the target time period (i.e. the last day within the target spread cycle), and T represents the total number of days in the target time period.

[0071] The technical solution provided in this embodiment determines the daily new cases at the first moment by considering the daily number of new cases, the daily number of effective reproduction numbers within a target transmission cycle prior to the first moment, and the target transmission cycle of the target epidemic. This technical solution not only considers the impact of the daily new cases within a target transmission cycle prior to the first moment on the daily new cases at the first moment, but also eliminates the impact of the number of confirmed cases prior to that target transmission cycle on the daily new cases at the first moment, thus accurately predicting the daily new cases at the first moment.

[0072] Figure 4 yes Figure 3 The flowchart of step S1 in an exemplary embodiment.

[0073] In some embodiments, the target time may include a first target moment, wherein the first target moment may refer to any day within the target time, and this disclosure does not limit it.

[0074] In some embodiments, if the first target time is a time that has already occurred, the daily effective number of reproductions at the first target time can be determined based on historical data. If the first target time is a time that has not yet occurred, then it is necessary to predict the effective number of reproductions at the first target time. It is understood that there are many methods to obtain the effective number of reproductions at the first target time, such as prediction using infectious disease models like the SIR model and the SEIR model, and this disclosure does not limit this method.

[0075] This embodiment provides a method for predicting the number of effective people regenerating at a first target time.

[0076] refer to Figure 4 The prediction of the effective number of regenerations at the first target time may include the following steps.

[0077] In step S11, the first combination of prevention and control measures implemented in the target area at the first target time and the target time for implementing the first combination of prevention and control measures are obtained.

[0078] In some embodiments, countries or regions may implement non-pharmaceutical control measures to intervene in a target epidemic (such as COVID-19). These non-pharmaceutical control measures can be broadly categorized into three types: control and management measures, economic measures, and health system measures. Control and management measures and health system measures may include, for example, C1: school closures, C2: workplace closures, C3: cancellation of public events, C4: restrictions on gatherings, C5: suspension of public transportation, C6: home isolation requirements, C7: restrictions on internal movement, C8: border controls, H2: nucleic acid testing, and H3: contact tracing.

[0079] In some embodiments, the first combination of prevention and control measures may include at least one prevention and control measure. It is understood that this disclosure does not limit the number or types of prevention and control measures in the first combination of prevention and control measures. For example, the first combination of prevention and control measures may include at least one of the following: closing schools, closing workplaces, canceling public events, restricting gatherings, suspending public transportation, home isolation requirements, restricting internal movement, border control, virus testing, or close contact tracing.

[0080] In step S12, the attribute characteristics of the target region at the target time are obtained.

[0081] In some embodiments, the attribute characteristics may refer to the national characteristics or regional characteristics of the target region, and this disclosure does not limit this. For example, the attribute characteristics of the target region at a target time may refer to at least one of the following: population density characteristics, economic characteristics, aging characteristics, religious belief characteristics, population movement characteristics, and geographical location characteristics of the target region at the target time.

[0082] In some embodiments, the economic characteristics of the target region may refer to features that can measure the economic level of the country or region, such as Gross Domestic Product (GDP) and per capita income, and this disclosure does not limit this.

[0083] In some embodiments, aging characteristics may refer to the total number of aging people in a country or region, or the proportion of aging people in the total population.

[0084] In some embodiments, religious belief characteristics may refer to the main religious belief of the people in the target area. For example, if more than 50% of the people in the target area believe in religion A, then religion A can be the religious belief of the target area. This disclosure does not limit the types or number of religious beliefs in the target area, but rather depends on actual needs.

[0085] In some embodiments, population movement characteristics may refer to movement data (entry and exit population data) of a target area at a designated location. The designated location may refer to places where people are likely to gather, such as parks, botanical gardens, zoos, libraries, train stations, and airports.

[0086] In some embodiments, geographical location features may refer to the latitude and longitude information of the target area, or whether the target area is near the sea, mountains, etc. This disclosure does not limit this.

[0087] In some embodiments, the attribute characteristics of the target region may also include the total population of the target region, the per capita land area, major trading countries (or regions), major tourist countries (or regions), etc., and this disclosure does not impose any restrictions on this.

[0088] It is understood that any indicator that can reflect the attribute characteristics of the target area is within the scope of protection of this disclosure.

[0089] In step S13, based on the attribute characteristics of the target region at the target time and the combination of the first prevention and control measures, the daily effective number of new cases of the target epidemic in the target region at the first target time is determined.

[0090] In some embodiments, a pre-trained neural network model can be used to process the attribute characteristics of the target region at the target time and the combination of the first prevention and control measures to determine the daily effective number of people reproducing in the target region at the first target time in response to the target epidemic.

[0091] In some embodiments, pre-training a neural network module may include the following steps.

[0092] Obtain the combination of prevention and control measures implemented by the first country or region at the first moment, as well as the attribute characteristics of the first country or region at the first moment, and generate a target training vector based on the combination of prevention and control measures and attribute characteristics.

[0093] In some embodiments, the ratio of the effective number of people reproduced on day t after the implementation of the first prevention and control measures in the first country or region to the effective number of people reproduced on day 1, R1, can be obtained as a training label, where t is the difference between the first target time and the target time.

[0094] Here, the effective reproduction number Rt on day t refers to the average number of people that a patient can infect within a target transmission cycle T at time t. One target transmission cycle can be the sum of the incubation period and the time to diagnosis of the target epidemic. For example, if the average incubation period of the target epidemic is 7 days and the average time to diagnosis is 3 days, then the target transmission cycle of the target epidemic can be 7 + 3 = 10 days.

[0095] The incubation period refers to the time from when a target individual contracts the target epidemic to the onset of symptoms, while the diagnosis time refers to the actual time it takes for a target individual to confirm their infection through medical means. Understandably, both the incubation period and diagnosis time of the target epidemic may change over time. Generally, the incubation period of a target epidemic follows a specific distribution, such as a Poisson or Weiber distribution.

[0096] The neural network model is pre-trained using target training vectors and training labels. Based on the trained neural network model, the attribute characteristics of the target area and the combination of the first prevention and control measures are processed to determine the effective population reproduction number Rt of the target area on day t (i.e., the first target time) after the implementation of the first combination of prevention and control measures.

[0097] It is understandable that a single neural network module trained using the above method may only yield the effective number of people regenerated on a single day. Therefore, if you want to predict the daily effective number of people regenerated over multiple days, you may need to train multiple neural network models. The training process is similar to the training process described above (the difference is that t in the training labels Rt / R1 is variable), and will not be elaborated here.

[0098] The technical solution provided in this embodiment can accurately and effectively determine the effective number of people reproducing in the target area at the first target time by combining the first prevention and control measures implemented in the target area at the first target time and the attribute characteristics of the target area.

[0099] Figure 5 yes Figure 4 The flowchart of step S13 in an exemplary embodiment.

[0100] refer to Figure 5 Step S13 above may include the following steps.

[0101] In step S131, the basic population reproduction number of the target region in response to the target epidemic is obtained.

[0102] In some embodiments, the basic reproduction number may refer to the average number of people that a patient can infect within a target infectious period in an environment consisting entirely of susceptible individuals, without any intervention.

[0103] In step S132, based on the attribute characteristics of the target area and the first combination of prevention and control measures, the target impact value of each prevention and control measure in the first combination of prevention and control measures on the prevention and control of the target epidemic is determined at the first target time.

[0104] In some embodiments, a neural network model can be pre-trained to determine the target impact value of each prevention and control measure in the first combination of prevention and control measures on the prevention and control of the target epidemic at the first target time.

[0105] For example, a pre-trained Gradient Boost Regression Tree (GBRT) can be used to process the first combination of prevention and control measures for the target region and the attribute characteristics of the target region at the target time (the time when the first combination of prevention and control measures is implemented) to determine the target impact value of each prevention and control measure in the first combination of prevention and control measures on the prevention and control of the target epidemic at the first target time.

[0106] GBRT is an iterative regression decision tree algorithm composed of multiple regression decision trees. In this embodiment, each regression tree learns the conclusions and residuals (negative gradients) of all previous trees, fitting a current residual regression tree. The weak learners built at each iteration step are designed to compensate for the shortcomings of existing models, and each decision branch has data interpretability. GBRT can also automatically handle interactions between multiple sets of features, addressing the nonlinear correlation between prevention and control measures features and country-related features in our data. While fitting the target to be predicted, GBRT can also determine the importance of a feature based on the average importance of that feature within a single tree.

[0107] In some embodiments, the second combination of prevention and control measures adopted by the second region (or country) after adjusting the prevention and control measures for the target epidemic at a second time point, and the second attribute features of the second region (or country) at the second time point can be collected in advance, and the target training vector for training the neural network model can be generated based on the second combination of prevention and control measures and the second attribute features. It is understood that the second region (or country) may refer to multiple countries or regions, and this disclosure does not limit this.

[0108] In some embodiments, the GBRT neural network model can be trained using the target training vector and the second epidemic control indicator of the second country after the implementation of the second prevention and control measures (e.g., the ratio of the effective number of people reproduced on day t after the implementation of the second prevention and control measures to the effective number of people reproduced on day 1, R1) as training labels, where t is the time difference between the first target time and the target time.

[0109] In some embodiments, the target prediction vector (generated by the combination of the attribute features of the target region at the target time and the first prevention and control measures) is processed by a pre-trained CBRT neural network model. This process can not only obtain the first epidemic control indicator (e.g., the ratio of the effective number of people reproducing on day t after the implementation of the first prevention and control measures to the effective number of people reproducing on day 1, R1), but also obtain the influence value of each feature in the target prediction vector (corresponding to each prevention and control measure and each attribute feature in the first prevention and control measures) on the first epidemic control indicator (which can also be considered as the effective number of people reproducing in the first epidemic).

[0110] As shown in Table 2, Table 2 shows the magnitude of the influence of each feature in the target prediction vector on the final prediction result (e.g., the ratio of the effective number of people reproducing on day t after the implementation of the first prevention and control measures to the effective number of people reproducing on day 1, R1), where t is a positive integer greater than or equal to 1.

[0111] Table 2 illustrates the impact of each prevention and control measure and attribute feature in the target prediction vector on the output result according to an exemplary embodiment. The one ranked first indicates the greatest impact on the final target value. For example, in the context of a country / region with a population density of H3, canceling prevention and control measure C4 has the greatest impact on the effective reproduction number of the first outbreak.

[0112] C4 0.1056517 H3 0.0491506 C5 0.0288271 C1 0.0243832 C2 0.0218051 C8 0.0189261 C6 0.0184697 C7 0.0169168 H2 0.0070437 C3 0.0034341

[0113] In step S133, the daily effective number of people reproducing in the target region at the first target time is determined based on the basic number of people reproducing and the target impact value.

[0114] In some embodiments, it can be done through formula The target impact value is processed to determine the daily effective number of people regenerated at the first target time. Here, R0 represents the basic number of people regenerated, and R... t α represents the daily effective number of new cases related to the target epidemic at the first target moment. k I represents the target impact value of the k-th prevention and control measure in the first prevention and control measure combination on the control of the target epidemic at the first moment, M is the number of prevention and control measures in the first prevention and control measure combination, and I k,t This indicates whether the target area at time t has adopted the k-th prevention and control measure. Generally, if I k,t If I equals 1, it means that the target area has adopted the k-th prevention and control measure. k,t A value of 0 indicates that the target area did not adopt the k-th prevention and control measure.

[0115] The technical solution provided in this embodiment accurately determines the daily effective number of people regenerating at the first target time by using the impact value of each prevention and control measure in the first prevention and control measure combination implemented at the first target time on the epidemic prevention and control at the first target time and the basic number of people regenerating.

[0116] Figure 6 This is a method for determining the timing of adjustments to prevention and control measures based on the previously reported number of new cases, as illustrated in an example embodiment.

[0117] refer to Figure 6 The above method may include the following steps.

[0118] In step S4, the number of new daily cases at the first moment and for the next N days is determined based on the number of new daily cases at the first moment, where N is a positive integer greater than or equal to 1.

[0119] In some embodiments, based on the daily new number of users and the daily effective user regeneration number at a first time point and within a target time period, and using the technical solution provided in this embodiment, the daily new number of users for the next N days after the first time point can be determined. Since the prediction method is consistent with the above-described method for determining the number of new users, this disclosure will not elaborate further.

[0120] In step S5, the discharge cycle of inpatients in the target region and the number of inpatients in the target region at the first time point are obtained.

[0121] In step S6, the number of inpatients in the target region at the first moment and the next N days is determined based on the discharge cycle, the number of inpatients at the first moment, and the number of new patients at the first moment and the next N days.

[0122] In some embodiments, since the number of new daily cases after the first moment is known, the number of inpatients at the first moment is also known, and the discharge cycle of inpatients (i.e. how long after admission can they be discharged) can also be known, then the number of inpatients at the first moment can be calculated (number of inpatients at the first moment = number of new daily confirmed cases + number of inpatients at the first moment - number of discharged patients at the first moment).

[0123] In step S7, the target time point for the first time exceeding the target medical resource range is determined based on the first time point and the number of daily inpatients in the next N days, so as to adjust the prevention and control measures of the target area at the target time point.

[0124] In some embodiments, the target area has limited medical resources and a limited number of patients that can be treated. In order to avoid an excessive number of patients in the target area, the prevention and control measures in the target area can be adjusted according to the medical resources of the target area.

[0125] In some embodiments, the target time point for the first time exceeding the target number of hospitalized patients can be determined based on the daily number of inpatients after the first time point, without adjusting prevention and control measures. It is understood that if the daily number of inpatients at the target time point is higher than the target medical resource range, then a target outbreak may occur; if the daily number of inpatients at the target time point is lower than the target medical resource range, then there is a surplus of medical resources in the target region for the target outbreak. The target medical resource range can be determined by the number of available hospital beds in the target region.

[0126] In some embodiments, if the number of inpatients at a target time point exceeds the target medical resource range, the prevention and control measures for the target area can be adjusted at the target time point.

[0127] The technical solution provided in this embodiment determines the number of daily hospitalized patients in the target area in the future based on the first combination of prevention and control measures in the target area, and determines the target time point of the outbreak (the medical resources of the target area cannot bear all patients) or the surplus of medical resources based on the number of daily hospitalized patients in the future, so as to make adjustments to prevention and control measures according to the target time point, and realize early warning of the outbreak or the surplus of medical resources.

[0128] In some embodiments, due to the limited medical resources in the target area, if the number of patients is too large due to overly relaxed prevention and control measures, it will have a great impact on the medical resources of the target area; if the number of patients is too small due to overly strict prevention and control measures, it will lead to an oversupply of medical resources in the target area, which will have an adverse impact on the economy or politics of the target area.

[0129] Therefore, the daily number of new cases in the target area in the future can be used to determine whether there will be an outbreak or an oversupply of medical resources in the target area.

[0130] In some embodiments, to determine whether a target area will experience an outbreak or a surplus of medical resources, a reasonable target range of medical resources needs to be set. In some embodiments, this target range of medical resources may include an upper limit and a lower limit of medical resources. The upper limit of medical resources may refer to the maximum number of medical resources available in the target area, and the lower limit may refer to the minimum number of medical resources available in the target area. For example, if the target area has 10,000 available hospital beds, then the upper limit of medical resources in the target area may be 10,000 * 0.3 = 9,000, and the lower limit of medical resources in the target area may be 10,000 * 0.3 = 3,000.

[0131] Figure 7 yes Figure 6 The flowchart of step S7 in an exemplary embodiment.

[0132] In some embodiments, the target medical resource range may include a lower limit of medical resources.

[0133] refer to Figure 7 Step S7 above may include the following steps.

[0134] In step S71, if the number of inpatients in the target region at the target time point is lower than the lower limit of medical resources, then the second combination of prevention and control measures implemented in the target region at the first time point is obtained.

[0135] In step S72, based on the magnitude of the economic impact of each prevention and control measure in the second prevention and control measure combination on the target area, prevention and control measures are removed from the second prevention and control measure combination to obtain a first effective prevention and control measure combination. Under the intervention of the first effective prevention and control measure combination, the number of inpatients in the target area within a specified time is within the target medical resources range.

[0136] In some embodiments, if the number of inpatients in the target region at a target time point is lower than the lower limit of medical resources, a second combination of prevention and control measures implemented in the target region at the first moment can be obtained; based on the magnitude of the economic impact of each prevention and control measure in the second combination of prevention and control measures on the target region, prevention and control measures are removed from the second combination of prevention and control measures to obtain a second effective combination of prevention and control measures, under the intervention of the second effective combination of prevention and control measures, the number of inpatients in the target region within a specified time (e.g., one month) is within the target medical resources range.

[0137] In some embodiments, the number of countries implementing various prevention and control measures and the implementation time can be counted to determine the impact of each prevention and control measure on the economy.

[0138] Figure 8 This illustrates, according to an exemplary embodiment, the number of countries implementing various prevention and control measures and the corresponding time periods. For example... Figure 8 As shown, the "restriction on international travel" control measure was adopted by many countries or regions at the beginning of the epidemic, so it can be considered that the "restriction on international travel" is a control measure with relatively small impact on the economy of various regions; the "stay-at-home order" control measure was adopted by fewer countries and was adopted relatively late, so it can be considered that the "stay-at-home order" control measure has a greater impact on the economy of various regions.

[0139] In some embodiments, it is possible to Figure 8 Draw a horizontal line at the number of target countries (e.g., 80), and we can assume that the impact of control measures on the economy is increasing from left to right along this line.

[0140] If the number of in-hospital patients in the target area at the target time point is lower than the lower limit of medical resources, obtain the target prevention and control measures that can be adopted in the target area (that is, the prevention and control measures or the intensity of prevention and control measures that have not been adopted but can be adopted in the target area), and adjust the prevention and control measures in the target area according to the magnitude of the economic impact of each target prevention and control measure on the target area to obtain the second effective prevention and control measure combination. For example, assume that the target prevention and control measures that can be adopted in the target area include multiple prevention and control measures such as a, b, c, d... and the magnitude of the economic impact on the target area is a < b < c < d... Then, if the number of in-hospital patients in the target area at the target time point is lower than the lower limit of medical resources, the prevention and control measures can be added to the target prevention and control measure combination in the order of a, b, c, d... until the number of in-hospital patients in the target area within the specified time (such as one month) under the intervention of the adjusted target prevention and control measure combination is within the target medical resources range. Then, the adjusted target prevention and control measure combination is the second effective prevention and control measure combination.

[0141] Figure 9 Yes Figure 6 The flowchart of step S7 in an exemplary embodiment.

[0142] In some embodiments, the target medical resource range may include the upper limit of medical resources.

[0143] Reference Figure 9 , the above step S7 may include the following steps.

[0144] In step S73, if the number of in-hospital patients in the target area at the target time point exceeds the upper limit of medical resources, obtain the target prevention and control measures that can be adopted in the target area.

[0145] In step S74, adjust the prevention and control measures in the target area according to the magnitude of the economic impact of each target prevention and control measure on the target area to obtain the second effective prevention and control measure combination, and the number of in-hospital patients in the target area within the specified time under the intervention of the second effective prevention and control measure combination is within the target medical resources range.

[0146] In some embodiments, if the number of in-hospital patients in the target area at the target time point is higher than the upper limit of medical resources, obtain the target prevention and control measures that can be adopted in the target area (that is, the prevention and control measures or the intensity of prevention and control measures that have not been adopted but can be adopted in the target area); adjust the prevention and control measures in the target area according to the magnitude of the economic impact of each target prevention and control measure on the target area to obtain the second effective prevention and control measure combination, and the number of in-hospital patients in the target area within the specified time (such as one month) under the intervention of the second effective prevention and control measure combination is within the target medical resources range.

[0147] If the number of hospitalized patients in the target region at the target time exceeds the upper limit of medical resources, the target prevention and control measures combination implemented in the target region at the first moment can be obtained. From the target prevention and control combination, the measures are reduced sequentially according to their economic impact on the target region. For example, if the target prevention and control measures include four measures A, B, C, and D, and their economic impact on the target region is A>B>C>D, then if the number of hospitalized patients in the target region at the target time exceeds the upper limit of medical resources, the measures can be reduced from the target prevention and control combination in the order of A, B, C, D, until the number of hospitalized patients in the target region within a specified time (e.g., one month) under the intervention of the target prevention and control combination is within the target medical resource range. The adjusted target prevention and control combination is then the second effective prevention and control combination.

[0148] The technical solution provided in this embodiment predicts the daily number of hospitalized patients in the target area based on the daily number of new COVID-19 cases, and predicts the target time point for medical resource surplus or outbreak based on the daily number of hospitalized patients. On the other hand, it adjusts the prevention and control measures in the target area at the target time point based on the economic impact of each prevention and control measure, so as to effectively control the epidemic while minimizing the economic impact on the target area.

[0149] Figure 10 This is a block diagram illustrating a device for predicting the number of new COVID-19 cases according to an exemplary embodiment. (Refer to...) Figure 10 The COVID-19 new case prediction device 1000 provided in this embodiment may include: a data acquisition module 1001 and a new case determination module 1002.

[0150] The data acquisition module 1001 can be configured to acquire the daily new cases and daily effective number of recurring cases in a target region within a target time period for a target epidemic. The target time period includes a first moment and a target transmission cycle prior to the first moment. The new case determination module 1002 can be configured to determine the daily new cases at the first moment based on the daily new cases, daily effective number of recurring cases, and the target transmission cycle of the target epidemic within the target time period.

[0151] In some embodiments, the target time includes a first target moment.

[0152] In some embodiments, the data acquisition module 1001 may include: a first prevention and control measure combination determination unit, an attribute feature acquisition unit, and a daily effective number of recurrence acquisition unit.

[0153] The first prevention and control measure combination determination unit can be configured to acquire the first prevention and control measure combination implemented in the target area at the first target time and the target time for implementing the first prevention and control measure combination. The attribute feature acquisition unit can be configured to acquire the attribute features of the target area at the target time. The daily effective number of recurrences acquisition unit can be configured to determine the daily effective number of recurrences of the target area against the target epidemic at the first target time based on the attribute features of the target area at the target time and the first prevention and control measure combination.

[0154] In some embodiments, the daily effective number of regenerations acquisition unit may include: a basic number of regenerations acquisition subunit, a target impact value acquisition subunit, and a daily effective number of regenerations determination subunit.

[0155] The basic population reproduction number acquisition subunit can be configured to acquire the basic population reproduction number of the target region in response to the target epidemic. The target impact value acquisition subunit can be configured to determine the target impact value of each prevention and control measure in the first prevention and control measure combination on the prevention and control of the target epidemic at the first target time, based on the attribute characteristics of the target region and the first prevention and control measure combination. The daily effective population reproduction number determination subunit can be configured to determine the daily effective population reproduction number of the target region in response to the target epidemic at the first target time, based on the basic population reproduction number and the target impact value.

[0156] In some embodiments, the target time includes a second target time.

[0157] In some embodiments, the new number of people determination module 1002 may include: a new number of people determination unit.

[0158] The newly added transmission number determination unit can be configured to determine the daily newly added transmission number within the target time period based on the daily new number of cases, the daily effective number of reproductions, and the target transmission cycle of the target epidemic. The daily newly added transmission number describes the number of people infected by the daily new number within the target time period at the first moment. The daily new number at the first moment is determined based on the daily newly added transmission number within the target time period.

[0159] In some embodiments, the new number determination module 1002 may be configured to sum the daily new transmission numbers within the target time period to determine the daily new number of people at the first moment.

[0160] In some embodiments, the COVID-19 new case prediction device 1000 may include: a module for determining the number of new cases per day at future times, a module for obtaining the number of inpatients at the first moment, a module for determining the number of inpatients per day, and a module for determining the target time point.

[0161] The module for determining the daily new cases at future times can be configured to determine the daily new cases for the first time and the next N days based on the daily new cases at the first time, where N is a positive integer greater than or equal to 1. The module for obtaining the number of hospitalized patients at the first time can be configured to obtain the discharge cycle of hospitalized patients in the target region and the number of hospitalized patients in the target region at the first time. The module for determining the number of hospitalized patients daily can be configured to determine the number of hospitalized patients in the target region daily for the first time and the next N days based on the discharge cycle, the number of hospitalized patients at the first time, and the daily new cases for the first time and the next N days. The module for determining the target time point can be configured to determine the target time point at which the number of hospitalized patients first exceeds the target medical resource range based on the number of hospitalized patients at the first time and the next N days, so as to adjust the prevention and control measures in the target region at the target time point.

[0162] In some embodiments, the target medical resource range includes a lower limit for medical resources.

[0163] In some embodiments, the target time point determination module may include: a second prevention and control measure combination acquisition unit and a first adjustment unit.

[0164] The second prevention and control measure combination acquisition unit can be configured to acquire the second prevention and control measure combination implemented in the target region at the first time point if the number of inpatients in the target region at the target time point is lower than the lower limit of medical resources. The first adjustment unit can be configured to cancel prevention and control measures from the second prevention and control measure combination to obtain a first effective prevention and control measure combination based on the magnitude of the economic impact of each prevention and control measure in the second prevention and control measure combination on the target region. Under the intervention of the first effective prevention and control measure combination, the number of inpatients in the target region within the target medical resources range within a specified time period.

[0165] In some embodiments, the target medical resource range includes a medical resource ceiling.

[0166] In some embodiments, the target time point determination module may include: a target prevention and control measures acquisition unit and a second adjustment unit.

[0167] The target prevention and control measure acquisition unit can be configured to acquire target prevention and control measures that can be adopted in the target area if the number of inpatients in the target area at the target time point exceeds the upper limit of medical resources. The second adjustment unit can be configured to adjust the prevention and control measures in the target area according to the magnitude of the economic impact of each target prevention and control measure on the target area to obtain a second effective combination of prevention and control measures. Under the intervention of the second effective combination of prevention and control measures, the number of inpatients in the target area within the target medical resources range within a specified time period.

[0168] Since the functional modules of the COVID-19 new number prediction device 1000 in the example embodiment of this disclosure correspond to the steps of the above-described COVID-19 new number prediction method in the example embodiment, they will not be described again here.

[0169] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions of the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or smart device, etc.) to execute the method according to the embodiments of this disclosure, for example... Figure 3 One or more of the steps shown.

[0170] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0171] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not claimed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0172] It should be understood that this disclosure is not limited to the detailed structures, drawing arrangements or implementations shown herein; rather, this disclosure is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. A method for predicting the number of new COVID-19 cases, characterized in that, include: The system obtains the daily new cases and daily effective reproduction number for a target region within a target time period. The target time is a target transmission cycle prior to a first moment. The daily effective reproduction number describes the number of effective reproductions each day within the target time period. The target time includes a first target moment and a second target moment, where either the first target moment or the second target moment refers to any moment within the target time period. Based on the daily new cases, daily effective reproduction number, and the target transmission cycle of the target epidemic within the target time period, the system determines the daily new transmission number within the target time period. The daily new transmission number describes the number of people infected by the daily new cases within the target time period at the first moment. The daily new transmission numbers within the target time period are summed to determine the daily new transmission numbers at the first moment; Specifically, the daily new cases at the second target time are determined based on the daily number of new cases, the daily number of effective cases recurring, and the target transmission cycle of the target epidemic. This includes: According to Base t *R t Calculate the total number of people who can be infected during the first transmission cycle at the second target time, where the first transmission cycle is the transmission cycle of a target epidemic starting from the second target time. According to formula N t =Base t *R t / T determines the daily new transmission numbers N corresponding to the second target time. t , where N t Baset represents the daily new number of people at the second target time, which is the daily new number of people propagated at the first time. Rt represents the daily new number of people at the second target time, and T represents the daily effective number of people regenerated at the second target time. Specifically, if the first target time is a time that has already occurred, then the daily new user count and daily effective user regeneration count for the first target time are determined based on historical data; if the first target time is a time that has not yet occurred, then the daily new user count and daily effective user regeneration count for the first target time are predicted; the predicted effective user regeneration count for the first target time includes: Obtain the first combination of prevention and control measures implemented in the target area at the first target time, and the target time at which the first combination of prevention and control measures was implemented; Obtain the attribute characteristics of the target region at the target time; Obtain the basic population reproduction number for the target region in response to the target epidemic; By processing the attribute characteristics of the target area at the target time and the combination of the first prevention and control measures through a pre-trained neural network model, the target impact value of each prevention and control measure in the first prevention and control measure combination on the prevention and control of the target epidemic is determined at the first target time. Based on the basic population reproduction number and the target impact value, determine the daily effective population reproduction number of the target region for the target epidemic at the first target time.

2. The method according to claim 1, characterized in that, Also includes: The number of new daily cases at the first moment and for the next N days is determined based on the number of new daily cases at the first moment, where N is a positive integer greater than or equal to 1. Obtain the discharge cycle of inpatients in the target region and the number of inpatients in the target region at the first time point; Based on the discharge cycle, the number of inpatients at the first moment, and the number of new patients added daily at the first moment and in the next N days, determine the number of inpatients in the target area daily at the first moment and in the next N days. The target time point for the first time exceeding the target medical resource range is determined based on the first moment and the number of daily inpatients in the next N days, so that the prevention and control measures in the target area can be adjusted at the target time point.

3. The method according to claim 2, characterized in that, The target medical resource range includes a lower limit for medical resources; wherein, the target time point for the first time exceeding the target medical resource range is determined based on the first moment and the number of daily inpatients over the next N days, so as to adjust the prevention and control measures in the target area at the target time point, including: If the number of inpatients in the target region at the target time point is lower than the lower limit of medical resources, then obtain the second combination of prevention and control measures implemented in the target region at the first time point; Based on the magnitude of the economic impact of each prevention and control measure in the second prevention and control measure combination on the target region, the prevention and control measures are reduced from the second prevention and control measure combination to obtain a first effective prevention and control measure combination. Under the intervention of the first effective prevention and control measure combination, the number of inpatients in the target region within a specified time is within the target medical resources range.

4. A device for predicting the number of new COVID-19 cases, characterized in that, include: The data acquisition module is configured to acquire the daily number of new cases and the daily number of effective reproductions of the target epidemic in the target region within a target time. The target time is a target transmission cycle before the first moment. The daily number of effective reproductions is used to describe the number of effective reproductions of each day within the target time. The target time includes a first target moment and a second target moment. The first target moment or the second target moment refers to any moment within the target time. The module for determining the number of new cases is configured to determine the number of new cases per day within the target time period based on the number of new cases per day, the number of new effective cases per day, and the target transmission cycle of the target epidemic. The number of new cases per day describes the number of people infected by the new cases per day within the target time period at the first moment. The module sums up the number of new cases per day within the target time period to determine the number of new cases per day at the first moment. Specifically, the daily new cases at the second target time are determined based on the daily number of new cases, the daily number of effective cases recurring, and the target transmission cycle of the target epidemic. This includes: According to Base t *R t Calculate the total number of people who can be infected within the first transmission cycle from the daily new cases at the second target time. The first transmission cycle is the transmission cycle of a target epidemic starting from the second target time. According to formula N... t =Base t *R t / T determines the daily new transmission numbers N corresponding to the second target time. t , where N t The daily new cases at the second target time point represent the daily new transmission numbers at the first time point. t R represents the daily new number of people at the second target time. t The number of effective users regenerated daily at the second target time point represents the target propagation period; Specifically, if the first target time is a time that has already occurred, then the daily new user count and daily effective user regeneration count for the first target time are determined based on historical data; if the first target time is a time that has not yet occurred, then the daily new user count and daily effective user regeneration count for the first target time are predicted; the predicted effective user regeneration count for the first target time includes: Obtain the first combination of prevention and control measures implemented in the target area at the first target time, and the target time at which the first combination of prevention and control measures was implemented; Obtain the attribute characteristics of the target region at the target time; Obtain the basic population reproduction number for the target region in response to the target epidemic; By processing the attribute characteristics of the target area at the target time and the combination of the first prevention and control measures through a pre-trained neural network model, the target impact value of each prevention and control measure in the first prevention and control measure combination on the prevention and control of the target epidemic is determined at the first target time. Based on the basic population reproduction number and the target impact value, determine the daily effective population reproduction number of the target region for the target epidemic at the first target time.

5. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-3.

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