WO2021212670A1

WO2021212670A1 - New infectious disease onset risk prediction method, apparatus, terminal device, and medium

Info

Publication number: WO2021212670A1
Application number: PCT/CN2020/102154
Authority: WO
Inventors: 史文中; 童成卓; 史志成
Original assignee: 香港理工大学深圳研究院
Priority date: 2020-04-21
Filing date: 2020-07-15
Publication date: 2021-10-28
Also published as: CN113539509B; CN113539509A

Abstract

A new infectious disease onset risk prediction method, comprising: acquiring case spatio-temporal data of each confirmed case in a target area and population movement data of a region where the confirmed case is located; establishing a prediction model to predict an onset risk value of a new infectious disease, so as to obtain a prediction result; and when the accuracy of the prediction result satisfies a preset requirement, acquiring, in real time, case spatio-temporal data of a new confirmed case in the target area and population movement data of a region where the new confirmed case is located, and then predicting the onset risk of the new infectious disease in a future second preset time period according to the prediction model. An onset risk value in a target area within a future second preset time period is predicted according to case spatio-temporal data, population movement data of a corresponding region, and a prediction model for which accuracy verification has been performed, improving the accuracy of prediction for a new infectious disease onset risk, and the new infectious disease onset risk with unknown epidemiological parameter characteristics can also be accurately predicted.

Description

新发传染病发病风险预测方法、装置、终端设备及介质Method, device, terminal equipment and medium for predicting the risk of emerging infectious diseases

本申请要求于2020年4月21日在中国专利局提交的、申请号为202010316319.0、发明名称为“新发传染病发病风险预测方法、装置、终端设备及介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。This application claims the priority of the Chinese patent application filed at the Chinese Patent Office on April 21, 2020, with the application number 202010316319.0 and the invention title "Methods, devices, terminal equipment and media for risk prediction of emerging infectious diseases". The entire content is incorporated into this application by reference.

技术领域Technical field

本申请属于病毒风险预测技术领域，尤其涉及一种新发传染病发病风险预测方法、装置、终端设备及存储介质。This application belongs to the technical field of virus risk prediction, and in particular relates to a method, device, terminal device, and storage medium for predicting the risk of emerging infectious diseases.

背景技术Background technique

随着经济一体化、工业化、城市化、大规模人口迁徙、社会分化、环境变换等原因，具有传染性的病毒不仅对民众的生命安全产生影响，还对全球经济及社会的稳定都有深远的影响。因此对新发传染病发病风险进行预测，从而对传染性病毒防疫的帮助时是非常必要的。With economic integration, industrialization, urbanization, large-scale population migration, social differentiation, environmental changes and other reasons, infectious viruses not only have an impact on people’s lives and safety, but also have profound effects on global economic and social stability. Influence. Therefore, it is very necessary to predict the risk of emerging infectious diseases, so as to help prevent infectious viruses.

目前对传染性病毒的风险预测大都依赖于各种传染性病毒的传染病学参数特性进行预测，但对新发传染性病毒，由于缺少各种病学参数特性先验知识，因此在新发传染病早期爆发阶段对这类型的传染性病毒的风险无法预测或预测的准确性不高。At present, the risk prediction of infectious viruses mostly relies on the prediction of the characteristics of infectious diseases of various infectious viruses. However, for new infectious viruses, due to the lack of prior knowledge of the characteristics of various diseases, the new infectious The risk of this type of infectious virus in the early outbreak stage of the disease cannot be predicted or the accuracy of prediction is not high.

申请内容Application content

本申请实施例的目的在于：提供一种新发传染病发病风险预测方法、装置、终端设备及存储介质，包括但不限于解决现有在早期爆发阶段对新发传染病发病风险无法预测或预测的准确性不高的问题。The purpose of the embodiments of the present application is to provide a method, device, terminal equipment and storage medium for predicting the risk of emerging infectious diseases, including but not limited to solving the existing inability to predict or predict the risk of emerging infectious diseases in the early outbreak stage The accuracy of the problem is not high.

本申请实施例采用的技术方案是：The technical solutions adopted in the embodiments of this application are:

第一方面，本申请实施例提供了一种新发传染病发病风险预测方法，包括：获取目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据；其中，所述病例时空数据包括发病时间和空间位置；将所述病例时空数据和所述人口流动数据存储至空间数据库，并建立每个确诊病例的病例时空数据和所在地区的人口流动数据之间的关联关系；根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果；其中，所述新发传染病发病风险值表示在所述目标区域内的第一子区域感染新发传染病病毒且在第一日期发病的概率，所述第一日期为所述第一预设时间段内的任一日期；根据在所述第一日期发病的确诊病例的病例时空数据，验证所述预测结果的准确性；在所述预测结果的准确性满足预设要求时，实时获取所述目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据，通过所述预测模型预测未来的第二预设时间段内的新发传染病发病风险值。In the first aspect, the embodiments of the present application provide a method for predicting the risk of emerging infectious diseases, including: obtaining case spatiotemporal data of each confirmed case in a target area and population flow data in the area; wherein, the case spatiotemporal data Including the onset time and spatial location; storing the case spatiotemporal data and the population movement data in a spatial database, and establishing the correlation between the case spatiotemporal data of each confirmed case and the population movement data in the area; according to the said The spatio-temporal data of the cases with the onset time before the Kth time in the spatial database and the population flow data establish a prediction model, and the new infectious diseases in the first preset time period after the Kth time are predicted according to the prediction model The risk value of the onset of the disease is obtained, and the predicted result is obtained; wherein the risk value of the onset of the new infectious disease represents the probability that the first sub-region within the target area is infected with a new infectious disease virus and the onset of the disease occurs on the first day, and the first The date is any date within the first preset time period; the accuracy of the prediction result is verified according to the case spatio-temporal data of the confirmed case on the first date; the accuracy of the prediction result satisfies When pre-determined, obtain real-time case-time and spatial data of each confirmed case in the target area and population flow data in the area, and use the prediction model to predict the risk of emerging infectious diseases in the second preset time period in the future value.

在一个实施例中，所述根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果，包括：根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据，基于时空邻近度和空间迁徙规律的核密度估计方法建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果。In one embodiment, the prediction model is established based on the spatio-temporal data of the cases whose onset time is before the Kth time in the spatial database and the population flow data, and the prediction model is used to predict the first after the Kth time according to the prediction model. Obtaining the risk value of the onset of a new infectious disease within a preset time period to obtain the prediction result includes: according to the spatio-temporal data of the cases and the population flow data in the spatial database whose onset time is before the Kth moment, based on the temporal and spatial proximity The kernel density estimation method of the degree and spatial migration law establishes a prediction model, and predicts the incidence risk value of a new infectious disease in the first preset time period after the Kth time according to the prediction model, and obtains the prediction result.

在一个实施例中，所述根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据，基于时空邻近度和空间迁徙规律的核密度估计方法建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，包括：In one embodiment, the prediction model is established based on the spatiotemporal data of the cases with the onset time before the Kth moment in the spatial database and the population flow data, and the nuclear density estimation method based on spatiotemporal proximity and spatial migration law , And predict the risk value of a new infectious disease in the first preset time period after the Kth time according to the prediction model, including:

根据第一病例时空参数建立第一函数，根据所述第一函数确定在第一子区域L的确诊病例于t _i时间感染新发传染病病毒的概率；其中，所述第一病例时空参数集合包括t _L、n(t _L)和P _incubation(t _L-t _i)；其中，所述t _L表示在第一子区域L的确诊病例的症状发病日期、所述n(t _L)表示于t _L时间在第一子区域L的发病病例数，所述P _incubation(t _L-t _i)表示潜伏期等于t _L-t _i天的概率； Establishing a first function based on a first temporal parameter case, according to the first function to determine the probability emerging infectious virus L of confirmed cases of infection in the first sub-region time t _i; wherein said first set of parameters cases spatiotemporal Including t _L , n(t _L ), and P _incubation (t _L -t _i ); wherein, t _L represents the onset date of symptoms of the confirmed case in the first subregion L, and the n(t _L ) represents The number of onset cases in the first subregion L at time _{t L} _{, and the P incubation} (t _L- t _i ) represents the probability that the incubation period is equal to t _L- t _{i days;}

根据第二病例时空参数以及第一参数建立第二函数，根据所述第二函数确定于t _i时间在第二子区域S感染新发传染病病毒的概率；其中，所述第一参数表示根据所述第一函数确定的于t _i时间在第三子区域L _j的确诊病例感染新发传染病病毒的概率，所述第二病例时空参数包括n(t _i)、M _intercity(S,t _i)、M _intracity(S,t _i)以及K _h(S-L _j)；所述n(t _i)表示于t _i时间在第三子区域L _j的发病病例数，所述M _intercity(S,t _i)表示于t _i时间第二子区域S所在地区外部迁入的人口流动数据，所述M _intracity(S,t _i)表示于t _i时间第二子区域S所在地区内部的人口流动数据，所述K _h(S-L _j)表示根据第三子区域L _j与第二子区域S之间距离确定的核函数，S-L _j表示第二子区域S到第三子区域L _j的距离； Establishing a second function based on the temporal parameter and a second case a first parameter, the second function is determined according to the probability at time t _i the second sub-region S virus infection Emerging Infectious Diseases; wherein said first parameter representation of The first function determines the probability that a confirmed case in the third subregion L _j _{at time t i} will be infected with a new infectious disease virus, and the spatiotemporal parameters of the second case include n(t _i ), M _intercity (S, t _i ), M _intracity (S, t _i ), and K _h (SL _j ); the n(t _i ) represents the number of onset cases in the third subregion L _j _{at time t i} _{, and the M intercity} (S, t _i ) represents the population flow data that migrated from outside the area where the second sub-region S is located at time _{t i} _{, and the M intracity} (S, t _i ) represents the population flow data inside the area where the second sub-region S is located at time _{t i} , The K _h (SL _j ) represents a kernel function determined according to the distance between the third sub-region L _j _{and the second sub-region S, and SL j} represents the distance from the second sub-region S to the third sub-region L _j;

根据第三病例时空参数以及第二参数建立第三函数，根据所述第三函数确定第K时刻之后的第一预设时间段内的新发传染病发病风险值；其中，所述第二参数表示根据所述第二函数确定于t _i时间在第二子区域S的确诊病例感染新发传染病病毒的概率，所述第三病例时空参数包括潜伏期等于t _z-t _i天的概率，其中，所述t _z表示所述第一日期。 A third function is established according to the space-time parameters of the third case and the second parameter, and the risk value of the incidence of a new infectious disease in the first preset time period after the Kth time is determined according to the third function; wherein, the second parameter _{Represents the probability that a confirmed case in the second subregion S at time t i} is determined to be infected with a new infectious disease virus according to the second function, and the spatio-temporal parameters of the third case include the probability that the incubation period is equal to t _z- t _i days, where , The t _z represents the first date.

在一个实施例中，根据所述第一函数确定在第一子区域L的确诊病例于t _i时间感染新发传染病病毒的概率的计算公式为： In one embodiment, the first function of determining calculation formula L confirmed cases of infection in the first sub-region time t _i emerging infectious virus in accordance with the probability of:

其中，P _infection(L,t _i)表示于t _i时间在第一子区域L的确诊病例感染新发传染病病毒的概率，t _L表示在第一子区域L的确诊病例的症状发病日期，n(t _L)表示于t _L时间在第一子区域L的发病病例数，p _incubation(t _L-t _i)表示潜伏期等于t _L-t _i天的概率；其中，所述p _incubation(t _L-t _i)为根据所述新发传染病病毒潜伏期的统计分布特征，计算出潜伏期为t _L-t _i天的概率。 Among them, P _infection (L, t _i ) represents the probability that a confirmed case in the first subregion L at time _{t i} _{will be infected with a new infectious disease virus, t L} represents the date of symptom onset of the confirmed case in the first subregion L, n(t _L ) represents the number of onset cases in the first subregion L at time _{t L} _{, p incubation} (t _L -t _i ) represents the probability that the incubation period is equal to t _L -t _i days; where, the p _incubation (t _L- t _i ) is the probability _{that the incubation period is t L-} t _i days calculated according to the statistical distribution characteristics of the incubation period of the newly emerging infectious disease virus.

在一个实施例中，根据所述第二函数确定于t _i时间在第二子区域S感染新发传染病病毒的概率的计算公式为： In one embodiment, the second function is determined according to the time t _i is calculated in the second sub-region S probability of infection of emerging infectious virus is:

其中，P _infection(S,t _i)表示于t _i时间在第二子区域S感染新发传染病病毒的概率，n(t _i)表示在t _i时间在第三子区域L _j的发病病例数，所述M _intercity(S,t _i)表示于t _i时间第二子区域S所在地区外部迁入的人口流动数据，所述M _intracity(S,t _i)表示于t _i时间第二子区域S所在地区内部的人口流动数据；P _infection(L _j,t _i)表示于t _i时间在第三子区域L _j的确诊病例感染新发传染病病毒的概率，K _h(S-L _j)表示根据第三子区域L _j与第二子区域S之间距离确定的核函数，S-L _j表示第二子区域S到第三子区域L _j的距离。 Among them, P _infection (S, t _i ) represents the probability of infection of a new infectious disease virus in the second sub-region S at time _{t i} _{, and n(t i} ) represents the incidence of cases in the third sub-region L _j _{at time t i} The M _intercity (S, t _i ) represents the population flow data that migrated from outside the area where the second sub-region S is located at time _{t i} _{, and the M intracity} (S, t _i ) represents the second sub-region at time _{t i} Population flow data within the area where area S is located; P _infection (L _j ,t _i ) represents the probability of a confirmed case infected with a new infectious disease virus in the third subarea L _j _{at time t i} _{, K h} (SL _j ) represents A kernel function determined according to the distance between the third sub-region L _j _{and the second sub-region S, SL j} represents the distance from the second sub-region S to the third sub-region L _j.

在一个实施例中，根据所述第三函数确定第K时刻之后的第一预设时间段内的新发传染病发病风险值的计算公式为：

其中，P _onset(S,t _z)表示在第二子区域S感染新发传染病病毒且在第一日期t _z发病的概率，P _infection(S,t _i)表示于t _i时间在第二子区域S的确诊病例感染新发传染病病毒的概率，P _incubation(t _z-t _i)表示潜伏期等于t _z-t _i天的概率。 In an embodiment, the calculation formula for determining the risk value of a new infectious disease in the first preset time period after the Kth time according to the third function is:

Among them, P _onset (S, t _z ) represents the probability of infection with a new infectious disease virus in the second sub-region S and the onset of the disease on the first day t _z _{, and P infection} (S, t _i ) represents the second subregion S at time _{t i} The probability of a confirmed case in subregion S being infected with a new infectious disease virus, P _incubation (t _z -t _i ) represents the probability that the incubation period is equal to t _z -t _{i days.}

在一个实施例中，在所述预测结果的准确性满足预设要求时，实时获取所述目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据，通过所述预测模型预测未来的第二预设时间段内的新发传染病发病风险值之后，还包括：根据已预测出未来第二预设时间段内M个子区域的风险值，生成风险分布预测图。In one embodiment, when the accuracy of the prediction result meets the preset requirements, the case spatiotemporal data of each confirmed case in the target area and the population flow data in the area are obtained in real time, and the future is predicted by the prediction model After the risk value of the new infectious disease in the second preset time period, the method further includes: generating a risk distribution prediction map according to the predicted risk values of the M sub-regions in the second preset time period in the future.

第二方面，本申请实施例提供了一种新发传染病发病风险预测装置，包括：第一获取模块，用于获取目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据；其中，所述病例时空数据包括发病时间和空间位置；存储模块，用于将所述病例时空数据和所述人口流动数据存储至空间数据库，并建立每个确诊病例的病例时空数据和所在地区的人口流动数据之间的关联关系；模型建立模块，用于根据所述空间数据库中发病时间在第 K时刻之前的所述病例时空数据和所述人口流动数据建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果；其中，所述新发传染病风险值表示在所述目标区域内的第一子区域感染新发传染病病毒且在第一日期发病的概率，所述第一日期为所述第一预设时间段内的任一日期；验证模块，用于在根据所述第一日期发病的确诊病例的病例时空数据，验证所述预测结果的准确性；预测模块，用于在所述预测结果的准确性满足预设要求时，实时获取所述目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据，通过所述预测模型预测未来的第二预设时间段内的新发传染病发病风险值。In the second aspect, an embodiment of the present application provides a device for predicting the incidence of a newly-emerging infectious disease, including: a first obtaining module, configured to obtain case time-space data of each confirmed case in a target area and population flow data in the area; Wherein, the case spatiotemporal data includes onset time and spatial location; the storage module is used to store the case spatiotemporal data and the population flow data in a spatial database, and establish the case spatiotemporal data and the location of each confirmed case The association relationship between population flow data; a model building module for establishing a prediction model based on the spatio-temporal data of the case whose onset time is before the Kth moment in the spatial database and the population flow data, and based on the prediction model Predict the onset risk value of a new infectious disease in the first preset time period after the Kth moment, and obtain the prediction result; wherein the new infectious disease risk value indicates that the first subregion in the target area is infected with a new The probability of developing an infectious disease virus and becoming ill on the first date, where the first date is any date within the first preset time period; the verification module is used to check the confirmed cases of the onset according to the first date Case spatio-temporal data to verify the accuracy of the prediction result; a prediction module for obtaining real-time case spatio-temporal data and location of each confirmed case in the target area when the accuracy of the prediction result meets the preset requirements Using the prediction model to predict the risk value of a new infectious disease in the second preset time period in the future.

第三方面，本申请实施例提供了一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述新发传染病发病风险预测方法的步骤。In the third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, Steps to realize the above-mentioned method for predicting the risk of emerging infectious diseases.

第四方面，本申请实施例提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，上述计算机程序被处理器执行时实现上述新发传染病发病风险预测方法的步骤。In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for predicting the risk of emerging infectious diseases are implemented .

第五方面，本申请实施例提供了一种计算机程序产品，当计算机程序产品在电子设备上运行时，使得电子设备执行上述现上述新发传染病发病风险预测方法的步骤。In a fifth aspect, the embodiments of the present application provide a computer program product that, when the computer program product runs on an electronic device, causes the electronic device to execute the steps of the aforementioned method for predicting the risk of emerging infectious diseases.

本申请实施例的有益效果在于：可根据获取目标区域中每个确诊病例的病例时空数据及所在地区的人口流动数据，建立预测模型预测新发传染病发病风险值，所述新发传染病发病风险值表示在所述目标区域内的第一子区域感染病毒且在第一日期发病的概率，根据新发传染病发病风险值得到预测结果，并在预测结果的准确性满足预设要求时，通过实时获取目标区域中新增确诊病例的病例时空数据和所在地区的人口流动数据，再根据预测模型预测未来的第二预设时间段内的新发传染病发病风险值。因此通过确诊病例的病例时空数据、对应地区的人口流动数据和已进行准确性验证的预测模型，预测未来的第二预设时间段内的新发传染病发病风险值，提高了对新发传染病发病风险的准确性，且对未知病学参数特性的新兴传染性病毒也可准确预测新发传染病发病风险。The beneficial effect of the embodiments of the present application is that: a prediction model can be established to predict the risk value of the onset of a new infectious disease based on the case time-space data of each confirmed case in the target area and the population flow data in the area. The risk value represents the probability that the first sub-region within the target area will be infected with the virus and will become ill on the first date. The prediction result is obtained according to the risk value of the onset of a new infectious disease, and when the accuracy of the prediction result meets the preset requirements, Through real-time acquisition of spatio-temporal data of newly confirmed cases in the target area and population flow data in the area, the risk value of new infectious diseases in the second preset time period in the future is predicted based on the prediction model. Therefore, through the spatio-temporal data of confirmed cases, the population flow data in the corresponding area, and the prediction model that has been verified for accuracy, the risk of new infectious diseases in the second preset time period in the future is predicted, which improves the risk of new infections. The accuracy of the risk of disease, and the emerging infectious viruses with unknown characteristics of the disease parameters can also accurately predict the risk of emerging infectious diseases.

附图说明Description of the drawings

为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或示范性技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。In order to more clearly describe the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings that need to be used in the embodiments or exemplary technical descriptions. Obviously, the accompanying drawings in the following description are only of the present application. For some embodiments, those of ordinary skill in the art can obtain other drawings based on these drawings without creative work.

图1是本申请一实施例提供的新发传染病发病风险预测方法的流程示意图；Fig. 1 is a schematic flow chart of a method for predicting the risk of emerging infectious diseases according to an embodiment of the present application;

图2是本申请另一实施例提供的新发传染病发病风险预测方法的流程示意图；FIG. 2 is a schematic flowchart of a method for predicting the incidence of a new infectious disease according to another embodiment of the present application;

图3是本申请另一实施例提供的新发传染病发病风险预测装置的结构示意图；FIG. 3 is a schematic structural diagram of a device for predicting the risk of emerging infectious diseases according to another embodiment of the present application;

图4是本申请另一实施例提供的终端设备的结构示意图。FIG. 4 is a schematic structural diagram of a terminal device provided by another embodiment of the present application.

具体实施方式Detailed ways

以下描述中，为了说明而不是为了限定，提出了诸如特定***结构、技术之类的具体细节，以便透彻理解本申请实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本申请。在其它情况中，省略对众所周知的***、装置、电路以及方法的详细说明，以免不必要的细节妨碍本申请的描述。In the following description, for the purpose of illustration rather than limitation, specific details such as a specific system structure and technology are proposed for a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from obstructing the description of this application.

本申请实施例提供的新发传染病发病风险预测方法，可应用于风险预测平台***，所述风险预测平台***可以是在终端设备上运行的软件***，由终端设备的处理器在运行计算机程序时执行。所述终端设备包括但不限于：服务器、台式电脑、平板电脑、云端服务器和移动终端等终端设备。本申请实施例对终端设备的具体类型不做任何限制。The method for predicting the risk of emerging infectious diseases provided by the embodiments of the present application can be applied to a risk prediction platform system. The risk prediction platform system may be a software system running on a terminal device, and a computer program is run by the processor of the terminal device. Executed at the time. The terminal equipment includes but is not limited to terminal equipment such as servers, desktop computers, tablet computers, cloud servers, and mobile terminals. The embodiments of this application do not impose any restrictions on the specific types of terminal devices.

为了说明本申请所述的技术方案，下面通过以下实施例来进行说明。In order to illustrate the technical solution described in the present application, the following embodiments are used for description.

请参阅图1，本申请实施例提供的新发传染病发病风险预测方法，包括：Please refer to Figure 1. The method for predicting the risk of emerging infectious diseases provided by the embodiments of the present application includes:

步骤S101，获取目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据；其中，所述病例时空数据包括发病时间和空间位置；Step S101: Obtain case spatio-temporal data of each confirmed case in the target area and population flow data in the area; wherein, the case spatio-temporal data includes onset time and spatial location;

在应用中，目标区域可以是需要预测新发传染病发病风险的区域，获取目标区域所有确诊病例中每个确诊病例的病例时空数据，具体可以是通过从第三方信用平台获取目标区域每个确诊病例的病例时空数据，所述第三方信用平台可以是***门相关平台或其它统计病例数据的企业平台，具体可预先与第三方信用平台签订授权服务后，从第三方信用平台的数据库中自动获取目标区域每个确诊病例的病例时空数据。当然也可以是通过相关工作人员输入每个确诊病例的病例时空数据或其它方式获得，对此不做限定。上述空间位置可以是一个子区域的地址信息。In the application, the target area can be the area where the risk of emerging infectious diseases needs to be predicted, and the case spatiotemporal data of each confirmed case in all confirmed cases in the target area can be obtained. Specifically, it can be obtained by obtaining each confirmed case in the target area from a third-party credit platform. Case spatio-temporal data of the case, the third-party credit platform may be a relevant platform of the health department or other enterprise platforms for statistical case data. Specifically, it can be automatically obtained from the database of the third-party credit platform after signing an authorization service with the third-party credit platform in advance Case spatio-temporal data of each confirmed case in the target area. Of course, it can also be obtained by inputting the spatio-temporal data of each confirmed case by the relevant staff or other methods, which is not limited. The above-mentioned spatial location may be the address information of a sub-region.

在一个实施例中，所述病例时空数据包括但不限于确诊病例的确诊时间、感染时间、症状发病时间以及确诊病例的子区域，所述确诊病例的子区域可以是确诊病例的常住地址的某个具体范围的经纬度信息。In one embodiment, the case spatio-temporal data includes, but is not limited to, the confirmed time of the confirmed case, infection time, symptom onset time, and the sub-region of the confirmed case. The sub-region of the confirmed case may be a certain address of the confirmed case’s usual residence. The latitude and longitude information of a specific range.

在具体应用中，上述新发传染病确诊病例可以理解为被某一种新发传染病病毒感染并确诊的对象，该病毒可理解为具有传染性的新发传染病病毒，如各种流感病毒，特别地，如新型冠状病毒，当然也可以是其它具有传染性的病毒。In specific applications, the above confirmed cases of newly emerging infectious diseases can be understood as objects infected and confirmed by a certain newly emerging infectious disease virus, which can be understood as infectious newly emerging infectious disease viruses, such as various influenza viruses , In particular, such as the new coronavirus, of course, it can also be other infectious viruses.

在一个实施例中，对于病例时空数据中没有症状发病时间信息的确诊病例，采用伽玛分布根据从发病到确诊之间可能的等待天数Δt来推测确诊病例的发病日期，即在未获取到症状发病时间信息的确诊病例时，根据伽玛分布，确定所述未获取到症状发病时间信息的确诊病例的症状发病日期。In one embodiment, for confirmed cases without symptom onset time information in the case spatiotemporal data, the gamma distribution is used to infer the onset date of the confirmed case based on the possible waiting days Δt from the onset to the diagnosis, that is, the onset date of the confirmed case is not obtained. In the case of a confirmed case with onset time information, the symptom onset date of the confirmed case for which symptom onset time information has not been obtained is determined according to the gamma distribution.

例如，在某一天某个地方被确诊的100个病例没有症状发病日期，由于发病到确诊之间可能等待的天数服从伽马分布，从而通过伽马分布可以确定这100个确诊病例中有多少个人的发病到确诊之间等待天数Δt1，有多少个人的发病到确诊之间等待天数Δt2，…，有多少个人的发病到确诊之间等待天数Δtn，从而可以推出这100个确诊病例可能的发病日期的分布情况。For example, 100 cases diagnosed in a certain place on a certain day have no symptom onset date. Since the number of days that may be waited between the onset of the disease and the diagnosis follows the gamma distribution, the gamma distribution can determine how many individuals are among the 100 confirmed cases The number of waiting days from the onset of the disease to the diagnosis Δt1, the number of waiting days Δt2 between the onset of the disease and the diagnosis of how many individuals, the number of waiting days Δtn between the onset of the disease and the diagnosis of the number of individuals, and the possible onset date of these 100 confirmed cases Distribution.

在一个实施例中，若想推算上述100个确诊病例具体每个病例的症状发病日期，可结合获取这100个确诊病例对应的身份信息，就可以推算出这一百个病例每个病例的症状发病时间，所述身份信息包括但不限于对应的年龄，性别和病史等信息。预设不同的身份信息发病到确诊之间等待天数，假设年龄身份信息是60岁，性别女，身体状态良好无病史对应的发病到确诊之间等待天数标签是5天。In one embodiment, if you want to calculate the specific symptom onset date of each of the above 100 confirmed cases, you can combine to obtain the identity information corresponding to these 100 confirmed cases, and you can calculate the symptoms of each of these 100 cases. At the time of onset, the identity information includes, but is not limited to, corresponding information such as age, gender, and medical history. The number of days to wait between the onset of different identification information and the diagnosis is preset. Assuming that the age identification information is 60 years old, gender is female, in good physical condition and no medical history, the number of days to wait between onset and diagnosis label is 5 days.

在应用中，获取所述确诊病例所在地区的人口流动数据可以是通过从第三方交通数据统计平台自动获取对应地区的人口流动数据，具体可以是某个时间段对应城市内部的人流量和城市外部迁入的人流量。In the application, the population flow data of the area where the confirmed case is located can be obtained by automatically obtaining the population flow data of the corresponding area from a third-party traffic data statistics platform. Specifically, it can be a certain period of time corresponding to the flow of people inside the city and the outside of the city. The flow of people moving in.

在一个实施例中，所述所在地区的人口流动数据包括所在地区内部的人口流动数据和由所在地区外部迁入的人口流动数据。In one embodiment, the population movement data of the area includes the population movement data within the area and the population movement data from outside the area.

步骤S102，将所述病例时空数据和所述人口流动数据存储至空间数据库，并建立每个确诊病例的病例时空数据和所在地区的人口流动数据之间的关联关系；Step S102, storing the case spatiotemporal data and the population movement data in a spatial database, and establishing an association relationship between the case spatiotemporal data of each confirmed case and the population movement data in the area;

在应用中，将确诊病例的病例时空数据关联对应地区的人口流动数据，存储至空间数据库中。In the application, the spatio-temporal data of confirmed cases are associated with population flow data in the corresponding area and stored in the spatial database.

步骤S103，根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果；Step S103: Establish a prediction model based on the spatio-temporal data of the cases with the onset time before the Kth time in the spatial database and the population flow data, and predict the first preset time period after the Kth time according to the prediction model The risk value of new infectious diseases in the internal, and get the prediction result;

其中，所述新发传染病发病风险值表示在所述目标区域内的第一子区域感染新发传染病病毒且在第一日期发病的概率，所述第一日期为所述第一预设时间段内的任一日期。Wherein, the risk value of the onset of the newly emerging infectious disease represents the probability that the first subregion within the target area will be infected with a newly emerging infectious disease virus and will become ill on a first date, and the first date is the first preset Any date in the time period.

在应用中，所述第一子区域为所述目标区域中包括的多个子区域中的任一个子区域。可根据空间数据库中某一时刻之前的病例时空数据和人口流动数据去预测某一时刻之后的一段时间(即第一预设时间)的任一个地方感染病毒并在哪天发病的概率，以此来判断各个地方的风险值。In an application, the first sub-region is any one of the multiple sub-regions included in the target region. According to the spatio-temporal data and population flow data of cases in the spatial database before a certain time, the probability of infection and the day of the disease in any place after a certain time (that is, the first preset time) can be predicted, based on this To judge the value of risk in various places.

在一个实施例中，所述根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病病毒感染风险值，得到预测结果，包括：根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据，基于时空邻近度和空间迁徙规律的核密度估计方法建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果。如可基于时空邻近度和空间迁徙规律的核密度估计方法建立预测模型，并用该预测模型用预测时刻之前的数据来预测该时刻的发病风险。即预测第K时刻之后的第一预设时间段内的每个日期或某时间段新发传染病发病风险值，并根据每个日期或某时间段的多个地方新发传染病发病风险值，统计后即可获得预测结果。In one embodiment, the prediction model is established based on the spatio-temporal data of the cases whose onset time is before the Kth time in the spatial database and the population flow data, and the prediction model is used to predict the first after the Kth time according to the prediction model. The risk value of a new infectious disease virus infection within a preset time period to obtain the prediction result includes: according to the spatio-temporal data of the cases and the population movement data with the onset time before the Kth time in the spatial database, based on the spatio-temporal data The kernel density estimation method of proximity and spatial migration rules establishes a prediction model, and predicts the incidence risk value of a new infectious disease in the first preset time period after the Kth time according to the prediction model, and obtains the prediction result. For example, a prediction model can be established based on the nuclear density estimation method of temporal and spatial proximity and spatial migration law, and the prediction model can be used to predict the risk of disease at that time using the data before the prediction time. That is, predict the incidence of new infectious diseases on each date or a certain time period in the first preset time period after the Kth time, and calculate the incidence of new infectious diseases in multiple places on each date or a certain time period. Then you can get the forecast result.

步骤S104，根据在所述第一日期发病的确诊病例的病例时空数据，验证所述预测结果的准确性；Step S104, verifying the accuracy of the prediction result according to the case spatio-temporal data of the confirmed case on the first date;

在应用中，第一日期可理解为是需要预测时间段中任一日期，在建立模型预测了该日期的风险性后，在该日期到达时，获取该日期实际数据去验证预测结果的准确性。预测出第一时间段内全部日期发病的风险性后，可验证该模型预测的准确性，如计算在发病风险高的区域(如风险高的区域为在这个区域感染病毒且在第一日期t _z发病的概率P _onset(S,t _z)>0.8)中报告的发病病例占全部发病病例的百分比，来评估发病风险预测的准确性。 In application, the first date can be understood as any date in the time period that needs to be predicted. After the model is established to predict the risk of the date, when the date arrives, the actual data of the date is obtained to verify the accuracy of the forecast result. . After predicting the risk of onset on all dates in the first time period, the accuracy of the model's prediction can be verified. _z Probability of _onset (P onset (S,t _z )>0.8) The percentage of cases reported in the total onset cases is used to evaluate the accuracy of the risk prediction.

步骤S105，在所述预测结果的准确性满足预设要求时，实时获取所述目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据，通过所述预测模型预测未来的第二预设时间段内的新发传染病发病风险值。Step S105: When the accuracy of the prediction result meets the preset requirements, obtain the case spatiotemporal data of each confirmed case in the target area and the population flow data in the area in real time, and predict the future second time through the prediction model. The risk value of the onset of a new infectious disease within a preset time period.

在应用中，如第一日期中的风险高的区域中报告的发病病例占该日期全部发病病例的百分比大于预设百分比，即表示这个日期的预测是准确的，且第一预设时间段内全部的日期都预测准确，则可认为预测模型验证的准确性满足预设要求。在预测模型验证的准确性满足预设要求时，就真正的使用所述预测模型，实时通过接口获取每个新增确诊病例的病例时空数据和所在地区人口流动数据，通过预测模型预测未来的第二预设时间段内的风险性。第二预设时间段表示为未来的一段具体时间。可根据实际情况设定。In application, if the percentage of cases reported in the high-risk area on the first date to the total number of cases on that date is greater than the preset percentage, it means that the prediction on this date is accurate and within the first preset time period If all dates are predicted accurately, it can be considered that the accuracy of the prediction model verification meets the preset requirements. When the accuracy of the prediction model verification meets the preset requirements, the prediction model is actually used to obtain the spatiotemporal data of each newly confirmed case and the population flow data in the area through the interface in real time, and predict the future first through the prediction model. 2. Risk within a preset time period. The second preset time period is expressed as a specific time in the future. Can be set according to the actual situation.

在一个实施例中，当预测模型验证的准确性不满足预设要求，则继续不断获取更多的确诊病例的病例时空数据和对应的人口流动数据，建立预测模型，验证预测模型，直至预测模型验证成功后就可使用该预测模型进行风险数据输出。In one embodiment, when the accuracy of the prediction model verification does not meet the preset requirements, continue to obtain more confirmed case spatiotemporal data and corresponding population flow data, establish the prediction model, verify the prediction model, and then continue to obtain the prediction model. After the verification is successful, the predictive model can be used for risk data output.

在应用中，当预测模型的准确性满足预设要求时，获取实时新增的确诊数据对应所在地区的人口流量数据，并基于预测模型来预测未来短期内的发病风险并输出具有时空信息的发病风险分布预测图。如风险分布预测图可表示未来短时间内每个日期中各个地区发病概率的风险情况。In the application, when the accuracy of the prediction model meets the preset requirements, the newly-added real-time confirmed data corresponding to the population flow data of the area is obtained, and based on the prediction model, the incidence risk in the short term in the future is predicted and the incidence with spatiotemporal information is output. Risk distribution forecast chart. For example, the risk distribution forecast chart can indicate the risk of the incidence of each area on each date in a short period of time in the future.

如图2所示，在一个实施例中，所述步骤S104，包括：As shown in Figure 2, in one embodiment, the step S104 includes:

步骤S201，根据第一病例时空参数建立第一函数，根据所述第一函数确定在第一子区域L的确诊病例于ti时间感染新发传染病病毒的概率；Step S201: Establish a first function according to the spatio-temporal parameters of the first case, and determine the probability that a confirmed case in the first subregion L will be infected with a new infectious disease virus at time ti according to the first function;

其中，所述第一病例时空参数集合包括t _L、n(t _L)和P _incubation(t _L-t _i)；其中，所述t _L表示在第一子区域L的确诊病例的症状发病日期、所述n(t _L)表示于t _L时间在第一子区域L的发病病例数，所述P _incubation(t _L-t _i)表示潜伏期等于t _L-t _i天的概率；在应用中，对确诊病例所在的子区域(确诊病例所在的某个位置)进行病毒原体历史存在的可能性分析，可以根据该病毒潜伏期的统计分布特性，从时间维度进行回顾性推断，确认每个确诊病例于某一天(将该天设为t _i时间)感染病毒并因此具有传染性的概率。 Wherein, the spatio-temporal parameter set of the first case includes t _L , n(t _L ), and P _incubation (t _L- t _i ); wherein, t _L represents the symptom onset date of the confirmed case in the first subregion L The n(t _L ) represents the number of cases in the first subregion L at time _{t L} _{, and the P incubation} (t _L- t _i ) represents the probability that the incubation period is equal to t _L- t _i days; in application Analyze the possibility of the historical existence of the virus in the sub-region where the confirmed case is located (a certain location where the confirmed case is located). According to the statistical distribution characteristics of the virus incubation period, retrospective inferences can be made from the time dimension to confirm each confirmed case case particular day (the day set time t _i) virus is contagious and therefore the probability.

在一个实施例中，根据所述第一函数确定在第一子区域L的确诊病例于t _i时间感染新发传染病病毒的概率的计算公式1为： In one embodiment, the first function of determining calculation formula L confirmed cases of infection in the first sub-region time t _i emerging infectious virus in accordance with probability 1:

其中，P _infection(L,t _i)表示于t _i时间在第一子区域L的确诊病例感染新发传染病病毒的概率，t _L表示在第一子区域L的确诊病例的症状发病日期t _L，n(t _L)表示于t _L时间在第一子区域L的发病病例数，p _incubation(t _L-t _i)表示潜伏期等于t _L-t _i天的概率；其中，所述p _incubation(t _L-t _i)为根据所述新发传染病病毒潜伏期的统计分布特征，计算出潜伏期为t _L-t _i天的概率。 Among them, P _infection (L, t _i ) represents the probability that a confirmed case in the first subregion L at time _{t i} _{will be infected with a new infectious disease virus, and t L} represents the symptom onset date t of the confirmed case in the first subregion L _L , n(t _L ) represents the number of onset cases in the first subregion L at time _{t L} _{, p incubation} (t _L- t _i ) represents the probability that the incubation period is equal to t _L- t _i days; where, the p _incubation (t _L- t _i ) is the probability _{that the incubation period is t L-} t _i days based on the statistical distribution characteristics of the incubation period of the newly emerging infectious disease virus.

在一个实施例中，可根据已知道确诊病例的潜伏期进行统计分析，并获取所述病毒潜伏期的统计分布特征；所述统计分布特征为已有确诊病例的潜伏期的分布，根据所述病毒潜伏期的统计分布特征，获得确诊病例的潜伏期为N天的概率。如知道潜伏期的分布特征中百分之四十的对象是在3天，则潜伏期等于3天的概率为百分之四十。In one embodiment, statistical analysis can be performed based on the incubation period of the known confirmed case, and the statistical distribution feature of the incubation period of the virus can be obtained; the statistical distribution feature is the distribution of the incubation period of the confirmed case, based on the incubation period of the virus Statistical distribution characteristics, obtain the probability that the incubation period of a confirmed case is N days. If it is known that 40% of the objects in the distribution characteristics of the incubation period are in 3 days, the probability that the incubation period is equal to 3 days is 40%.

在应用中，所有确诊病例所在的子区域包括L ₁,L ₂,…L _n，上述第一子区域L为L ₁,L ₂,…L _n中任一个子区域。上述公式1等式右边的函数为上述第一函数。 In application, the sub-regions where all confirmed cases are located include L ₁ , L ₂ ,...L _n , and the above-mentioned first sub-region L is any one _{of L 1} , L ₂ ,...L _n. The function on the right side of the above equation 1 is the above first function.

步骤S202，根据第二病例时空参数以及第一参数建立第二函数，根据所述第二函数确定于t _i时间在第二子区域S感染新发传染病病毒的概率； Step S202, the function establishing a second parameter according to a second case and a first temporal parameter, a probability at time t _i the second sub-region S emerging infectious virus infection according to the second function determination;

其中，所述第一参数表示根据所述第一函数确定的于t _i时间在第三子区域L _j的确诊病例感染新发传染病病毒的概率，所述第二病例时空参数包括n(t _i)、M _intercity(S,t _i)、M _intracity(S,t _i)以及K _h(S-L _j)；所述n(t _i)表示于t _i时间在第三子区域L _j的发病病例数，所述M _intercity(S,t _i)表示于t _i时间第二子区域S所在地区外部迁入的人口流动数据，所述M _intracity(S,t _i)表示于t _i时间第二子区域S所在地区内部的人口流动数据，所述K _h(S-L _j)表示根据第三子区域L _j与第二子区域S之间距离确定的核函数，S-L _j表示第二子区域S到所述第三子区域L _j的距离。第二子区域S到所述第三子区域L _j的距离可以是第二子区域S与第三子区域L _j之间的最小距离，或者两者中心之间的距离。 Wherein, the first parameter represents the probability of a confirmed case infected with a new infectious disease virus in the third subregion L _j _{at time t i determined according to the first function, and the second case spatio-temporal parameter includes n(t} _i ), M _intercity (S, t _i ), M _intracity (S, t _i ), and K _h (SL _j ); the n(t _i ) represents the onset cases in the third subregion L _j _{at time t i} The M _intercity (S, t _i ) represents the population flow data that migrated from outside the area where the second sub-region S is located at time _{t i} _{, and the M intracity} (S, t _i ) represents the second sub-region at time _{t i} The population flow data within the area where the area S is located, the K _h (SL _j ) represents the kernel function determined according to the distance between the third sub-area L _j _{and the second sub-area S, and SL j} represents the second sub-area S to all The distance of the third sub-region L _j. The distance from the second sub-region S to the third sub-region L _j may be the smallest distance between the second sub-region S and the third sub-region L _j , or the distance between the centers of the two.

在应用中，基于所有确诊病例所在的子区域L ₁,L ₂,…L _n的病原体历史存在可能性分析的结果，对病原体在目标区域内随机子区域S(设为第二子区域S)的历史存在可能性进行空间外推进行分析。L _j表示L ₁,L ₂,…L _n中的第j个子区域。 In the application, based on _{the results of the analysis of the possibility of pathogen history in the sub-regions L 1} , L ₂ ,...L _n where all confirmed cases are located, random sub-region S of the pathogen in the target region (set as the second sub-region S) The historical existence of the possibility of space extrapolation for analysis. L _j represents the j-th subregion in _{L 1} , L ₂ ,...L _n.

在一个实施例中，根据所述第二函数确定于t _i时间在第二子区域S感染新发传染病病毒的概率的计算公式2为： In an embodiment, the calculation formula 2 of the probability of being infected with a new infectious disease virus in the second subregion S at time _{t i} according to the second function is:

其中，P _infection(S,t _i)表示于t _i时间在第二子区域S感染新发传染病病毒的概率，n(t _i)表示在t _i时间在第三子区域L _j的发病病例数，所述M _intercity(S,t _i)表示于t _i时间第二子区域S所在地区外部迁入的人口流动数据，所述M _intracity(S,t _i)表示于t _i时间第二子区域S所在地区内部的人口流动数据；P _infection(L _j,t _i)表示于t _i时间在第三子区域L _j的确诊病例感染新发传染病病毒的概率，K _h(S-L _j)表示第三子区域L _j与第二子区域S之间距离确定的核函数，S-L _j表示第二子区域S到第三子区域L _j的距离。 Among them, P _infection (S, t _i ) represents the probability of infection of a new infectious disease virus in the second sub-region S at time _{t i} _{, and n(t i} ) represents the incidence of cases in the third sub-region L _j _{at time t i} The M _intercity (S, t _i ) represents the population flow data that migrated from outside the area where the second sub-region S is located at time _{t i} _{, and the M intracity} (S, t _i ) represents the second sub-region at time _{t i} Population flow data within the area where area S is located; P _infection (L _j ,t _i ) represents the probability of a confirmed case infected with a new infectious disease virus in the third subarea L _j _{at time t i} _{, K h} (SL _j ) represents The kernel function is determined by the distance between the third sub-region L _j _{and the second sub-region S, and SL j} represents the distance from the second sub-region S to the third sub-region L _j.

在应用中，P _infection(S,t _i)可理解为任何感染者在第t _i天访问随机子区域S并对那里的其他人构成感染风险的可能性，核函数K _h(S-L _j)可理解为利用随机点S到确诊病例子区域L _j的距离来决定L _j对估计随机子区域S的感染可能性时所起的作用。 In application, P _infection (S, t _i ) can be understood as _{the possibility that any infected person visits a random sub-region S on the t i} day and poses a risk of infection to other people there. The kernel function K _h (SL _j ) can be It is understood that the distance between the random point S and the confirmed disease example area L _j is used to determine the role of L _j in estimating the infection probability of the random sub-area S.

步骤S203，根据第三病例时空参数以及第二参数建立第三函数，根据所述第三函数确定第K时刻之后的第一预设时间段内的新发传染病发病风险值；Step S203: Establish a third function according to the third case time-space parameter and the second parameter, and determine the risk value of a new infectious disease in the first preset time period after the Kth time according to the third function;

其中，所述第二参数表示根据所述第二函数确定于t _i时间在第二子区域S的确诊病例感染病毒的概率，所述第三病例时空参数包括潜伏期等于t _z-t _i天的概率，其中，所述t _z表示所述第一日期。 Wherein said second parameter representation of the second function to determine the time t _i the probability of a second sub-confirmed cases of infection in the region S, the third case includes latency equal temporal parameters t _z -t _i days Probability, where the t _z represents the first date.

在应用中，预测第K时刻之后的第一预设时间段内的风险值可以理解为预测第K时刻之后短期内的某个特定日期或某个特定时间段时某个子区域的新发传染病发病风险值。In application, predicting the risk value in the first preset time period after the Kth time can be understood as predicting the new infectious disease in a certain sub-region at a specific date or a specific time period after the Kth time. Risk value of morbidity.

在一个实施例中，根据所述第三函数确定第K时刻之后的第一预设时间段内新发传染病发病风险值的计算公式为：In an embodiment, the calculation formula for determining the risk value of a new infectious disease in the first preset time period after the Kth time according to the third function is:

其中，P _onset(S,t _z)表示在第二子区域S感染新发传染病病毒且在第一日期t _z发病的概率，P _infection(S,t _i)表示于t _i时间在第二子区域S的确诊病例感染新发传染病病毒的概率，P _incubation(t _z-t _i)表示潜伏期等于t _z-t _i天的概率。 Among them, P _onset (S, t _z ) represents the probability of infection with a new infectious virus in the second sub-region S and the onset of the disease on the first day t _z _{, and P infection} (S, t _i ) represents the second subregion S at the time _{t i} The probability that a confirmed case in subregion S is infected with a new infectious disease virus, P _incubation (t _z -t _i ) represents the probability that the incubation period is equal to t _z -t _{i days.}

本实施例根据获取目标区域中每个确诊病例的病例时空数据及所在地区的人口流动数据，建立预测模型预测新发传染病发病风险值，所述新发传染病发病风险值表示在所述目标区域内的第一子区域感染病毒且在第一日期发病的概率，根据新发传染病发病风险值得到预测结果，并在预测结果的准确性满足预设要求时，通过实时获取目标区域中新增确诊病例的病例时空数据和所在地区的人口流动数据，再根据预测模型预测未来的第二预设时间段内的新发传染病发病风险值。因此通过确诊病例的病例时空数据、对应地区的人口流动数据和已进行准确性验证的预测模型，预测未来的第二预设时间段内的新发传染病发病风险值，提高了对新发传染病发病风险的准确性，且对未知病学参数特性的新兴传染性病毒也可准确预测新发传染病发病风险。In this embodiment, a prediction model is established to predict the risk value of the onset of a new infectious disease based on the case space-time data of each confirmed case in the target area and the population flow data in the area. The probability that the first sub-region in the region will be infected with the virus and will develop on the first day will get the prediction result based on the risk value of the onset of a new infectious disease. The spatio-temporal data of the confirmed cases and the population flow data in the region are added, and then the risk value of the incidence of new infectious diseases in the second preset time period in the future is predicted based on the prediction model. Therefore, through the spatio-temporal data of confirmed cases, the population flow data in the corresponding area, and the prediction model that has been verified for accuracy, the risk of new infectious diseases in the second preset time period in the future is predicted, which improves the risk of new infections. The accuracy of the risk of disease, and the emerging infectious viruses with unknown characteristics of the disease parameters can also accurately predict the risk of emerging infectious diseases.

应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实时过程构成任何限定。It should be understood that the size of the sequence number of each step in the foregoing embodiment does not mean the order of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the real-time process of the embodiment of the present application.

对应于上文实施例所述的新发传染病发病风险预测方法，图3示出了本申请实施例提供的新发传染病发病风险预测装置300的结构框图，为了便于说明，仅示出了与本申请实施例相关的部分。Corresponding to the method for predicting the risk of emerging infectious diseases described in the above embodiment, FIG. 3 shows a structural block diagram of the apparatus 300 for predicting the risk of emerging infectious diseases provided by an embodiment of the present application. For ease of description, only The part related to the embodiment of this application.

参照图3，该装置包括：Referring to Figure 3, the device includes:

第一获取模块301，用于获取目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据；其中，所述病例时空数据包括发病时间和空间位置；The first acquisition module 301 is used to acquire case spatio-temporal data of each confirmed case in the target area and population flow data in the area; wherein, the case spatiotemporal data includes onset time and spatial location;

存储模块302，用于将所述病例时空数据和所述人口流动数据存储至空间数据库，并建立每个确诊病例的病例时空数据和所在地区的人口流动数据之间的关联关系；The storage module 302 is configured to store the case spatio-temporal data and the population flow data in a spatial database, and establish an association relationship between the case spatiotemporal data of each confirmed case and the population flow data in the area;

模型建立模块303，用于根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果；其中，所述新发传染病发病风险值表示在所述目标区域内的第一子区域感染新发传染病病毒且在第一日期发病的概率，所述第一日期为所述第一预设时间段内的任一日期；The model establishment module 303 is configured to establish a prediction model according to the spatio-temporal data of the cases whose onset time is before the Kth time in the spatial database and the population flow data, and predict the first after the Kth time according to the prediction model. The risk value of the onset of a new infectious disease within the preset time period is obtained, and the prediction result is obtained; wherein the risk value of the onset of the new infectious disease indicates that the first subregion in the target area is infected with a new infectious disease virus and is in the first subregion. The probability of onset on a date, where the first date is any date within the first preset time period;

验证模块304，用于根据在所述第一日期发病的确诊病例的病例时空数据，验证所述预测结果的准确性；The verification module 304 is configured to verify the accuracy of the prediction result according to the spatio-temporal data of the confirmed case on the first date;

预测模块305，用于在所述预测结果的准确性满足预设要求时，实时获取所述目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据，通过所述预测模型预测未来的第二预设时间段内的新发传染病发病风险值。The prediction module 305 is configured to obtain in real time the case spatio-temporal data of each confirmed case in the target area and the population flow data in the area when the accuracy of the prediction result meets the preset requirements, and predict the future through the prediction model The risk value of a new infectious disease in the second preset period of time.

在一个实施例中，模型建立模块303具体用于根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据，基于时空邻近度和空间迁徙规律的核密度估计方法建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果。In one embodiment, the model establishment module 303 is specifically configured to determine the nuclear density based on the temporal and spatial proximity and the spatial migration law according to the spatio-temporal data of the cases with the onset time before the Kth moment in the spatial database and the population flow data. The estimation method establishes a prediction model, and predicts the incidence risk value of a new infectious disease in the first preset time period after the Kth time according to the prediction model, and obtains the prediction result.

在一个实施例中，模型建立模块303包括：In one embodiment, the model establishment module 303 includes:

第一建立单元，用于根据第一病例时空参数建立第一函数，根据所述第一函数确定在第一子区域L的确诊病例于t _i时间感染新发传染病病毒的概率；其中，所述第一病例时空参数集合包括t _L、n(t _L)和P _incubation(t _L-t _i)；其中，所述t _L表示在第一子区域L的确诊病例的症状发病日期、所述n(t _L)表示于t _L时间在第一子区域L的发病病例数，所述P _incubation(t _L-t _i)表示潜伏期等于t _L-t _i天的概率。 A first establishing means for establishing a first function based on a first temporal parameter case, the probability of the L confirmed cases of infection in the first sub-region time t _i emerging infectious virus determined according to the first function; wherein the The spatio-temporal parameter set of the first case includes t _L , n(t _L ), and P _incubation (t _L- t _i ); wherein, t _L represents the symptom onset date of the confirmed case in the first subregion L, and the n(t _L ) represents the number of onset cases in the first subregion L at time _{t L} _{, and the P incubation} (t _L- t _i ) represents the probability that the incubation period is equal to t _L- t _{i days.}

第二建立单元，用于根据第二病例时空参数以及第一参数建立第二函数，根据所述第二函数确定于t _i时间在第二子区域S感染新发传染病病毒的概率；其中，所述第一参数表示根据所述第一函数确定的于t _i时间在第三子区域L _j的确诊病例感染新发传染病病毒的概率，所述第二病例时空参数包括n(t _i)、M _intercity(S,t _i)、M _intracity(S,t _i)以及K _h(S-L _j)；所述n(t _i)表示于t _i时间在第三子区域L _j的发病病例数，所述M _intercity(S,t _i)表示于t _i时间第二子区域S所在地区外部迁入的人口流动数据，所述M _intracity(S,t _i)表示于t _i时间第二子区域S所在地区内部的人口流动数据，所述K _h(S-L _j)表示根据第三子区域L _j与第二子区域S之间距离确定的核函数，S-L _j表示第二子区域S到第三子区域L _j的距离。 Second establishing means for establishing a second case according to a first parameter and a second parameter space-time function, the time t _i the probability of a second sub-region S emerging infectious virus infection according to the second function determination; wherein, The first parameter represents the probability that a confirmed case in the third subregion L _j _{at time t i} determined according to the first function will be infected with a new infectious disease virus, and the second case spatio-temporal parameter includes n(t _i ) , M _intercity (S, t _i ), M _intracity (S, t _i ), and K _h (SL _j ); the n(t _i ) represents the number of cases in the third subregion L _j _{at time t i,} The M _intercity (S, t _i ) represents the population flow data that migrated from outside the area where the second sub-region S is located at time _{t i} _{, and the M intracity} (S, t _i ) represents the second sub-region S at time _{t i} The population flow data within the area where the K _h (SL _j ) represents the kernel function determined according to the distance between the third sub-region L _j _{and the second sub-region S, and SL j} represents the second sub-region S to the third sub-region S The distance of the area L _j.

第三建立单元，用于根据第三病例时空参数以及第二参数建立第三函数，根据所述第三函数确定第K时刻之后的第一预设时间段内的新发传染病发病风险值；其中，所述第二参数表示根据所述第二函数确定于t _i时间在第二子区域S的确诊病例感染新发传染病病毒的概率，所述第三病例时空参数包括潜伏期等于t _z-t _i天的概率，其中，所述t _z表示所述第一日期。 The third establishment unit is configured to establish a third function according to the third case space-time parameters and the second parameter, and determine the risk value of the onset of a new infectious disease in the first preset time period after the Kth time according to the third function; wherein said second parameter representation of the second function to determine the time t _i the probability that the second sub-region S confirmed cases of virus infection new infectious diseases, the third case includes latency equal temporal parameters t _z - _{The probability of ti} days, where the t _z represents the first date.

在一个实施例中，根据所述第三函数确定第K时刻之后的第一预设时间段内的新发传染病发病风险值的计算公式为：In an embodiment, the calculation formula for determining the risk value of a new infectious disease in the first preset time period after the Kth time according to the third function is:

在一个实施例中，所述风险预测装置300还包括：In an embodiment, the risk prediction device 300 further includes:

生成模块，用于根据已预测出未来第二预设时间段内M个子区域的风险值，生成风险分布预测图。The generating module is used to generate a risk distribution prediction map according to the predicted risk values of the M sub-regions in the second preset time period in the future.

本实施例根据获取目标区域中每个确诊病例的病例时空数据及所在地区的人口流动数据，建立预测模型预测新发传染病发病风险值，所述新发传染病发病风险值表示在所述目标区域内的第一子区域感染病毒且在第一日期发病的概率，根据新发传染病发病风险值得到预测结果，并在预测结果的准确性满足预设要求时，通过实时获取目标区域中新增确诊病例的病例时空数据和所在地区的人口流动数据，再根据预测模型预测未来的第二预设时间段内的新发传染病发病风险值。因此通过确诊病例的病例时空数据、对应地区的人口流动数据和已进行准确性验证的预测模型，预测未来的第二预设时间段内的新发传染病发病风险值，提高了对新发传染病发病风险的准确性，且对未知病学参数特性的新兴传染性病毒也可准确预测发病风险。In this embodiment, a prediction model is established to predict the risk value of the onset of a new infectious disease based on the case space-time data of each confirmed case in the target area and the population flow data in the area. The probability that the first sub-region in the region will be infected with the virus and will develop on the first day will get the prediction result based on the risk value of the onset of a new infectious disease. The spatio-temporal data of the confirmed cases and the population flow data in the region are added, and then the risk value of the incidence of new infectious diseases in the second preset time period in the future is predicted based on the prediction model. Therefore, through the spatio-temporal data of confirmed cases, the population flow data in the corresponding area, and the prediction model that has been verified for accuracy, the risk of new infectious diseases in the second preset time period in the future is predicted, which improves the risk of new infections. The accuracy of the risk of disease incidence, and the emerging infectious viruses with unknown characteristics of the disease parameters can also accurately predict the risk of incidence.

图4为本申请一实施例提供的终端设备的结构图，如图4所示终端设备400包括：处理器401，存储器402以及存储在所述存储器402中并可在所述处理器401上运行的计算机程序403，例如新发传染病发病风险预测程序。所述处理器401执行所述计算机程序403时实现上述各个新发传染病发病风险预测方法实施例中的步骤。所述处理器401执行所述计算机程序403时实现上述各装置实施例中各模块的功能，例如图3所示模块301至305的功能。FIG. 4 is a structural diagram of a terminal device provided by an embodiment of the application. As shown in FIG. 4, the terminal device 400 includes a processor 401, a memory 402, and is stored in the memory 402 and can run on the processor 401 Computer programs 403, such as the risk prediction program for emerging infectious diseases. When the processor 401 executes the computer program 403, the steps in the above-mentioned embodiments of the method for predicting the risk of emerging infectious diseases are implemented. When the processor 401 executes the computer program 403, the functions of the modules in the foregoing device embodiments, such as the functions of the modules 301 to 305 shown in FIG. 3, are realized.

示例性的，所述计算机程序403可以被分割成一个或多个模块，所述一个或者多个模块被存储在所述存储器402中，并由所述处理器401执行，以完成本发明。所述一个或多个模块可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序403在所述终端设备400中的执行过程。例如，所述计算机程序403可以被分割成第一获取模块，存储模块，模型建立模块，验证模块，预测模块，各模块具体功能在上述实施例中已有描述，此处不再赘述。Exemplarily, the computer program 403 may be divided into one or more modules, and the one or more modules are stored in the memory 402 and executed by the processor 401 to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 403 in the terminal device 400. For example, the computer program 403 may be divided into a first acquisition module, a storage module, a model building module, a verification module, and a prediction module. The specific functions of each module have been described in the above-mentioned embodiments and will not be repeated here.

所述终端设备400可以是服务器、台式电脑、平板电脑、云端服务器和移动终端等计算设备。所述终端设备400可包括，但不仅限于，处理器401，存储器402。本领域技术人员可以理解，图4仅仅是终端设备400的示例，并不构成对终端设备400的限定。所称处理器401可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。The terminal device 400 may be a computing device such as a server, a desktop computer, a tablet computer, a cloud server, and a mobile terminal. The terminal device 400 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art can understand that FIG. 4 is only an example of the terminal device 400, and does not constitute a limitation on the terminal device 400. The so-called processor 401 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.

在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。In the above-mentioned embodiments, the description of each embodiment has its own focus. For parts that are not described in detail or recorded in an embodiment, reference may be made to related descriptions of other embodiments.

所述集成的模块如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。If the integrated module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiments and methods, and can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, it can implement the steps of the foregoing method embodiments. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electrical carrier signal, telecommunications signal, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or deleted according to the requirements of the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to the legislation and patent practice, the computer-readable medium Does not include electrical carrier signals and telecommunication signals.

以上所述实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围，均应包含在本发明的保护范围之内。The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that it can still implement the foregoing various embodiments. The technical solutions recorded in the examples are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in Within the protection scope of the present invention.

Claims

一种新发传染病发病风险预测方法，其特征在于，包括：A method for predicting the risk of emerging infectious diseases, which is characterized in that it includes:

获取目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据；其中，所述病例时空数据包括发病时间和空间位置；Obtain case spatio-temporal data of each confirmed case in the target area and population flow data in the area; wherein, the case spatio-temporal data includes onset time and spatial location;

将所述病例时空数据和所述人口流动数据存储至空间数据库，并建立每个确诊病例的病例时空数据和所在地区的人口流动数据之间的关联关系；Store the case spatiotemporal data and the population movement data in a spatial database, and establish an association relationship between the case spatiotemporal data of each confirmed case and the population movement data in the area;

根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果；其中，所述新发传染病发病风险值表示在所述目标区域内的第一子区域感染新发传染病病毒且在第一日期发病的概率，所述第一日期为所述第一预设时间段内的任一日期；Establish a prediction model based on the spatio-temporal data of the cases with the onset time before the Kth time and the population flow data in the spatial database, and predict the new data in the first preset time period after the Kth time according to the prediction model. The risk value of the onset of an infectious disease is obtained, and the prediction result is obtained; wherein the risk value of the onset of the new infectious disease represents the probability that the first subregion in the target area will be infected with a new infectious disease virus and will be on the first date, so The first date is any date within the first preset time period;

根据在所述第一日期发病的确诊病例的病例时空数据，验证所述预测结果的准确性；Verify the accuracy of the prediction result according to the case spatio-temporal data of the confirmed case on the first day;

在所述预测结果的准确性满足预设要求时，实时获取所述目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据，通过所述预测模型预测未来的第二预设时间段内的新发传染病发病风险值。When the accuracy of the prediction result meets the preset requirements, the case spatiotemporal data of each confirmed case in the target area and the population flow data in the area are obtained in real time, and the second preset time in the future is predicted through the prediction model The risk value of emerging infectious diseases in the segment.
根据权利要求1所述的新发传染病发病风险预测方法，其特征在于，所述根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病风险值，得到预测结果，包括：The method for predicting the risk of emerging infectious diseases according to claim 1, wherein the prediction model is established based on the spatio-temporal data of the cases whose onset time is before the Kth moment in the spatial database and the population flow data , And predict the risk value of a new infectious disease in the first preset time period after the Kth time according to the prediction model, and obtain the prediction result, including:

根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据，基于时空邻近度和空间迁徙规律的核密度估计方法建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果。According to the spatio-temporal data of the cases in the spatial database with the onset time before the Kth moment and the population flow data, a prediction model is established based on the nuclear density estimation method based on the temporal and spatial proximity and spatial migration rules, and the prediction is made according to the prediction model The risk value of the onset of a new infectious disease in the first preset time period after the K-th moment, and the prediction result is obtained.
根据权利要求2所述的新发传染病发病风险预测方法，其特征在于，所述根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据，基于时空邻近度和空间迁徙规律的核密度估计方法建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，包括：The method for predicting the risk of emerging infectious diseases according to claim 2, characterized in that, according to the spatio-temporal data of the cases in the spatial database whose onset time is before the Kth moment and the population flow data are based on spatio-temporal The kernel density estimation method of proximity and spatial migration rules establishes a prediction model, and predicts the risk value of a new infectious disease in the first preset time period after the Kth time according to the prediction model, including:

根据第一病例时空参数建立第一函数，根据所述第一函数确定在第一子区域L的确诊病例于t _i时间感染新发传染病病毒的概率；其中，所述第一病例时空参数集合包括t _L、n(t _L) 和P _incubation(t _L-t _i)；其中，所述t _L表示在第一子区域L的确诊病例的症状发病日期、所述n(t _L)表示于t _L时间在第一子区域L的发病病例数，所述P _incubation(t _L-t _i)表示潜伏期等于t _L-t _i天的概率； Establishing a first function based on a first temporal parameter case, according to the first function to determine the probability emerging infectious virus L of confirmed cases of infection in the first sub-region time t _i; wherein said first set of parameters cases spatiotemporal Including t _L , n(t _L ) and P _incubation (t _L -t _i ); wherein, the t _L represents the date of symptom onset of the confirmed case in the first subregion L, and the n(t _L ) represents The number of onset cases in the first subregion L at time _{t L} _{, and the P incubation} (t _L- t _i ) represents the probability that the incubation period is equal to t _L- t _{i days;}

根据第二病例时空参数以及第一参数建立第二函数，根据所述第二函数确定于t _i时间在第二子区域S感染新发传染病病毒的概率；其中，所述第一参数表示根据所述第一函数确定的于t _i时间在第三子区域L _j的确诊病例感染新发传染病病毒的概率，所述第二病例时空参数包括n(t _i)、M _intercity(S,t _i)、M _intracity(S,t _i)以及K _h(S-L _j)；所述n(t _i)表示于t _i时间在第三子区域L _j的发病病例数，所述M _intercity(S,t _i)表示于t _i时间第二子区域S所在地区外部迁入的人口流动数据，所述M _intracity(S,t _i)表示于t _i时间第二子区域S所在地区内部的人口流动数据，所述K _h(S-L _j)表示根据第三子区域L _j与第二子区域S之间距离确定的核函数，S-L _j表示第二子区域S到第三子区域L _j的距离； Establishing a second function based on the temporal parameter and a second case a first parameter, the second function is determined according to the probability at time t _i the second sub-region S virus infection Emerging Infectious Diseases; wherein said first parameter representation of The first function determines the probability that a confirmed case in the third subregion L _j _{at time t i} will be infected with a new infectious disease virus, and the spatiotemporal parameters of the second case include n(t _i ), M _intercity (S, t _i ), M _intracity (S, t _i ), and K _h (SL _j ); the n(t _i ) represents the number of onset cases in the third subregion L _j _{at time t i} _{, and the M intercity} (S, t _i ) represents the population flow data that migrated from outside the area where the second sub-region S is located at time _{t i} _{, and the M intracity} (S, t _i ) represents the population flow data inside the area where the second sub-region S is located at time _{t i} , The K _h (SL _j ) represents a kernel function determined according to the distance between the third sub-region L _j _{and the second sub-region S, and SL j} represents the distance from the second sub-region S to the third sub-region L _j;

根据第三病例时空参数以及第二参数建立第三函数，根据所述第三函数确定第K时刻之后的第一预设时间段内的新发传染病发病风险值；其中，所述第二参数表示根据所述第二函数确定于t _i时间在第二子区域S的确诊病例感染新发传染病病毒的概率，所述第三病例时空参数包括潜伏期等于t _z-t _i天的概率，其中，所述t _z表示所述第一日期。 A third function is established according to the space-time parameters of the third case and the second parameter, and the risk value of the incidence of a new infectious disease in the first preset time period after the Kth time is determined according to the third function; wherein, the second parameter _{Represents the probability that a confirmed case in the second subregion S at time t i} is determined to be infected with a new infectious disease virus according to the second function, and the spatio-temporal parameters of the third case include the probability that the incubation period is equal to t _z- t _i days, where , The t _z represents the first date.
根据权利要求3所述的新发传染病发病风险预测方法，其特征在于，根据所述第一函数确定在第一子区域L的确诊病例于ti时间感染新发传染病病毒的概率的计算公式为：The method for predicting the risk of emerging infectious diseases according to claim 3, characterized in that, according to the first function, a formula for calculating the probability that a confirmed case in the first subregion L is infected with a new infectious disease virus at time ti is determined according to the first function for:

其中，P _infection(L,t _i)表示于t _i时间在第一子区域L的确诊病例感染新发传染病病毒的概率，t _L表示在第一子区域L的确诊病例的症状发病日期，n(t _L)表示于t _L时间在第一子区域L的发病病例数，p _incubation(t _L-t _i)表示潜伏期等于t _L-t _i天的概率；其中，所述p _incubation(t _L-t _i)为根据所述新发传染病病毒潜伏期的统计分布特征，计算出潜伏期为t _L-t _i天的概率。 Among them, P _infection (L, t _i ) represents the probability that a confirmed case in the first subregion L at time _{t i} _{will be infected with a new infectious disease virus, t L} represents the date of symptom onset of the confirmed case in the first subregion L, n(t _L ) represents the number of onset cases in the first subregion L at time _{t L} _{, p incubation} (t _L -t _i ) represents the probability that the incubation period is equal to t _L -t _i days; where, the p _incubation (t _L- t _i ) is the probability _{that the incubation period is t L-} t _i days calculated according to the statistical distribution characteristics of the incubation period of the newly emerging infectious disease virus.
根据权利要求3所述的新发传染病发病风险预测方法，其特征在于，根据所述第二函数确定于t _i时间在第二子区域S感染新发传染病病毒的概率的计算公式为： Prediction risk emerging infectious diseases of claim 3, wherein the second function is determined according to a time t _i is calculated in the second sub-region S probability of infection of emerging infectious virus is:

其中，P _infection(S,t _i)表示于t _i时间在第二子区域S感染新发传染病病毒的概率，n(t _i)表示在t _i时间在第三子区域L _j的发病病例数，所述M _intercity(S,t _i)表示于t _i时间第二子区域S所在地区外部迁入的人口流动数据，所述M _intracity(S,t _i)表示于t _i时间第二子区域S所在地区内部的人口流动数据；P _infection(L _j,t _i)表示于t _i时间在第三子区域L _j的确诊病例感染新发传染病病毒的概率，K _h(S-L _j)表示根据第三子区域L _j与第二子区域S之间距离确定的核函数，S-L _j表示第二子区域S到第三子区域L _j的距离。 Among them, P _infection (S, t _i ) represents the probability of infection of a new infectious disease virus in the second sub-region S at time _{t i} _{, and n(t i} ) represents the incidence of cases in the third sub-region L _j _{at time t i} The M _intercity (S, t _i ) represents the population flow data that migrated from outside the area where the second sub-region S is located at time _{t i} _{, and the M intracity} (S, t _i ) represents the second sub-region at time _{t i} Population flow data within the area where area S is located; P _infection (L _j ,t _i ) represents the probability of a confirmed case infected with a new infectious disease virus in the third subarea L _j _{at time t i} _{, K h} (SL _j ) represents A kernel function determined according to the distance between the third sub-region L _j _{and the second sub-region S, SL j} represents the distance from the second sub-region S to the third sub-region L _j.
根据权利要求3所述的新发传染病发病风险预测方法，其特征在于，根据所述第三函数确定第K时刻之后的第一预设时间段内的新发传染病发病风险值的计算公式为：The method for predicting the risk of emerging infectious diseases according to claim 3, wherein the calculation formula for the risk of emerging infectious diseases in the first preset time period after the Kth time is determined according to the third function for:

其中，P _onset(S,t _z)表示在第二子区域S感染新发传染病病毒且在第一日期t _z发病的概率，P _infection(S,t _i)表示于t _i时间在第二子区域S的确诊病例感染新发传染病病毒的概率，P _incubation(t _z-t _i)表示潜伏期等于t _z-t _i天的概率。 Among them, P _onset (S, t _z ) represents the probability of infection with a new infectious virus in the second sub-region S and the onset of the disease on the first day t _z _{, and P infection} (S, t _i ) represents the second subregion S at the time _{t i} The probability that a confirmed case in subregion S is infected with a new infectious disease virus, P _incubation (t _z -t _i ) represents the probability that the incubation period is equal to t _z -t _{i days.}
根据权利要求1至6任一项所述的新发传染病发病风险预测方法，其特征在于，在所述预测结果的准确性满足预设要求时，实时获取所述目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据，通过所述预测模型预测未来的第二预设时间段内的新发传染病发病风险值之后，还包括：The method for predicting the risk of emerging infectious diseases according to any one of claims 1 to 6, characterized in that, when the accuracy of the prediction result meets a preset requirement, each confirmed case in the target area is acquired in real time After using the prediction model to predict the risk value of a new infectious disease in the second preset time period in the future, it also includes:

根据已预测出未来第二预设时间段内M个子区域的风险值，生成风险分布预测图。According to the predicted risk values of the M sub-regions in the second preset time period in the future, a risk distribution prediction map is generated.
根据权利要求1至6任一项所述的新发传染病发病风险预测方法，其特征在于，所述获取目标区域中每个确诊病例的病例时空数据包括：The method for predicting the risk of an emerging infectious disease according to any one of claims 1 to 6, wherein said obtaining case spatiotemporal data of each confirmed case in the target area comprises:

获取目标区域中每个确诊病例的确诊时间、症状发病日期以及所在子区域信息。Obtain the diagnosis time, symptom onset date, and sub-region information of each confirmed case in the target area.
根据权利要求8所述的新发传染病发病风险预测方法，其特征在于，所述方法还包括：The method for predicting the risk of emerging infectious diseases according to claim 8, wherein the method further comprises:

对未获取到症状发病时间信息的确诊病例，根据伽玛分布，确定所述未获取到症状发病时间信息的确诊病例的症状发病日期。For a confirmed case for which symptom onset time information is not obtained, the symptom onset date of the confirmed case for which symptom onset time information is not obtained is determined according to the gamma distribution.
一种新发传染病发病风险预测装置，其特征在于，包括：A device for predicting the risk of emerging infectious diseases, which is characterized in that it comprises:

第一获取模块，用于获取目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据；其中，所述病例时空数据包括发病时间和空间位置；The first acquisition module is used to acquire case spatiotemporal data of each confirmed case in the target area and population flow data in the area; wherein, the case spatiotemporal data includes onset time and spatial location;

存储模块，用于将所述病例时空数据和所述人口流动数据存储至空间数据库，并建立每个确诊病例的病例时空数据和所在地区的人口流动数据之间的关联关系；The storage module is used to store the case spatio-temporal data and the population flow data in a spatial database, and establish the association relationship between the case spatiotemporal data of each confirmed case and the population flow data in the area;

模型建立模块，用于根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果；其中，所述新发传染病发病风险值表示在所述目标区域内的第一子区域感染新发传染病病毒且在第一日期发病的概率，所述第一日期为所述第一预设时间段内的任一日期；The model establishment module is used to establish a prediction model based on the spatio-temporal data of the cases whose onset time is before the Kth time and the population flow data in the spatial database, and predict the first prediction after the Kth time according to the prediction model. Set the risk value of the onset of a new infectious disease in the time period to obtain the prediction result; wherein the risk value of the onset of the new infectious disease indicates that the first subregion in the target area is infected with a new infectious disease virus and is in the first subregion. The probability of onset on a date, where the first date is any date within the first preset time period;

验证模块，用于根据在所述第一日期发病的确诊病例的病例时空数据，验证所述预测结果的准确性；The verification module is used to verify the accuracy of the prediction result according to the case spatiotemporal data of the confirmed case on the first date;

预测模块，用于在所述预测结果的准确性满足预设要求时，实时获取所述目标区域中每个确诊病例的病例时空数据和所在地区的人口流动数据，通过所述预测模型预测未来的第二预设时间段内的新发传染病发病风险值。The prediction module is used to obtain real-time case spatio-temporal data of each confirmed case in the target area and population flow data in the area when the accuracy of the prediction result meets the preset requirements, and predict the future through the prediction model The risk value of the onset of a new infectious disease within the second preset time period.
根据权利要求10所述的新发传染病发病风险预测装置，其特征在于，所述模型建立模块具体用于：The device for predicting the risk of emerging infectious diseases according to claim 10, wherein the model establishment module is specifically configured to:

根据所述空间数据库中发病时间在第K时刻之前的所述病例时空数据和所述人口流动数据，基于时空邻近度和空间迁徙规律的核密度估计方法建立预测模型，并根据所述预测模型预测第K时刻之后的第一预设时间段内的新发传染病发病风险值，得到预测结果。According to the spatio-temporal data of the cases in the spatial database with the onset time before the Kth moment and the population flow data, a prediction model is established based on the nuclear density estimation method based on the temporal and spatial proximity and spatial migration rules, and the prediction is made according to the prediction model The risk value of the onset of a new infectious disease in the first preset time period after the K-th moment, and the prediction result is obtained.
根据权利要求11所述的新发传染病发病风险预测装置，其特征在于，所述模型建立模块具体包括：The apparatus for predicting the risk of emerging infectious diseases according to claim 11, wherein the model building module specifically comprises:

第一建立单元，用于根据第一病例时空参数建立第一函数，根据所述第一函数确定在第一子区域L的确诊病例于t _i时间感染新发传染病病毒的概率；其中，所述第一病例时空参数集合包括t _L、n(t _L)和P _incubation(t _L-t _i)；其中，所述t _L表示在第一子区域L的确诊病例的症状发病日期、所述n(t _L)表示于t _L时间在第一子区域L的发病病例数，所述P _incubation(t _L-t _i)表示潜伏期等于t _L-t _i天的概率。 The first establishment unit is configured to establish a first function according to the spatio-temporal parameters of the first case, and determine the probability of a confirmed case in the first subregion L being infected with a new infectious disease virus at time _{t i according to the first function;} The spatio-temporal parameter set of the first case includes t _L , n(t _L ), and P _incubation (t _L- t _i ); wherein, t _L represents the symptom onset date of the confirmed case in the first subregion L, and the n(t _L ) represents the number of onset cases in the first subregion L at time _{t L} _{, and the P incubation} (t _L- t _i ) represents the probability that the incubation period is equal to t _L- t _{i days.}

第二建立单元，用于根据第二病例时空参数以及第一参数建立第二函数，根据所述第二函数确定于t _i时间在第二子区域S感染新发传染病病毒的概率；其中，所述第一参数表示根据所述第一函数确定的于t _i时间在第三子区域L _j的确诊病例感染新发传染病病毒的概率，所述第二病例时空参数包括n(t _i)、M _intercity(S,t _i)、M _intracity(S,t _i)以及K _h(S-L _j)；所述n(t _i)表示于t _i时间在第三子区域L _j的发病病例数，所述M _intercity(S,t _i)表示于t _i时间第二子区域S所在地区外部迁入的人口流动数据，所述M _intracity(S,t _i)表示于t _i时间第二子区域S所在地区内部的人口流动数据，所述K _h(S-L _j)表示根据第三子区域L _j与第二子区域S之间距离确定的核函数，S-L _j表示第二子区域S到第三子区域L _j的距离。 Second establishing means for establishing a second case according to a first parameter and a second parameter space-time function, the time t _i the probability of a second sub-region S emerging infectious virus infection according to the second function determination; wherein, The first parameter represents the probability that a confirmed case in the third subregion L _j _{at time t i} determined according to the first function will be infected with a new infectious disease virus, and the second case spatio-temporal parameter includes n(t _i ) , M _intercity (S, t _i ), M _intracity (S, t _i ), and K _h (SL _j ); the n(t _i ) represents the number of cases in the third subregion L _j _{at time t i,} The M _intercity (S, t _i ) represents the population flow data that migrated from outside the area where the second sub-region S is located at time _{t i} _{, and the M intracity} (S, t _i ) represents the second sub-region S at time _{t i} The population flow data within the area where the K _h (SL _j ) represents the kernel function determined according to the distance between the third sub-region L _j _{and the second sub-region S, and SL j} represents the second sub-region S to the third sub-region S The distance of the area L _j.

第三建立单元，用于根据第三病例时空参数以及第二参数建立第三函数，根据所述第三函数确定第K时刻之后的第一预设时间段内的新发传染病发病风险值；其中，所述第二参数表示根据所述第二函数确定于t _i时间在第二子区域S的确诊病例感染新发传染病病毒的概率，所述第三病例时空参数包括潜伏期等于t _z-t _i天的概率，其中，所述t _z表示所述第一日期。 The third establishment unit is configured to establish a third function according to the third case space-time parameters and the second parameter, and determine the risk value of the onset of a new infectious disease in the first preset time period after the Kth time according to the third function; wherein said second parameter representation of the second function to determine the time t _i the probability that the second sub-region S confirmed cases of virus infection new infectious diseases, the third case includes latency equal temporal parameters t _z - _{The probability of ti} days, where the t _z represents the first date.
根据权利要求10至12任一项所述的新发传染病发病风险预测装置，其特征在于，所述风险预测装置还包括：The device for predicting the risk of emerging infectious diseases according to any one of claims 10 to 12, wherein the device for predicting the risk further comprises:

生成模块，用于根据已预测出未来第二预设时间段内M个子区域的风险值，生成风险分布预测图。The generating module is used to generate a risk distribution prediction map according to the predicted risk values of the M sub-regions in the second preset time period in the future.
一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至9任一项所述的方法。A terminal device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program as claimed in claims 1 to 9. The method of any one of.
一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至9任一项所述的方法。A computer-readable storage medium storing a computer program, wherein the computer program implements the method according to any one of claims 1 to 9 when the computer program is executed by a processor.