Method and device for estimating mortality rate of instant report of infectious diseases and electronic equipment

Abstract

The invention provides a method and a device for estimating the mortality rate of an instant report of an infectious disease and electronic equipment. The method comprises the following steps: determining a probability distribution function # imgabs1 # of a third time interval # imgabs0 # from a report of a case to death; determining the total death risk # imgabs3 # of all reported cases at the # imgabs2 # moment; determining a death risk # imgabs5 # corresponding to the # imgabs4 # at the first moment; determining that the number of dead people # imgabs7 # due to diseases corresponding to # imgabs6 # at the first moment obeys binomial distribution; according to the number # imgabs9 #, corresponding to the number # imgabs8 # at the first moment, death due to diseases, the time sequence disease of the number of death in each day is fitted to construct a contour likelihood framework, and based on a random optimization algorithm of a contour likelihood function, the instantaneous report case fatality rate # imgabs10 # at any moment is estimated; wherein the first moment # imgabs11 # comprises a future moment; therefore, the case fatality rate of the infectious disease can be estimated in real time in a prospective manner, and the time change dynamic state of the case fatality rate is fully considered.

Description

Technical Field

[0001] The present disclosure relates to the technical field of infectious disease estimation, and in particular to a method, device and electronic equipment for estimating the instantaneous reported case fatality rate of infectious diseases. Background Art

[0002] The mortality rate (abbreviated as case fatality rate, CFR) of infectious diseases refers to the proportion of the number of people who died from a certain infectious disease to the total number of patients in a certain period of time. It indicates the risk of death of a patient with an infectious disease due to the disease, which can reflect the clinical severity of the infectious disease and is often used for acute infectious diseases. The risk of death is one of the key indicators for assessing the clinical severity of the disease. The case fatality rate quantifies the risk of death from an infectious disease at the time of infection and is usually calculated as a constant value. However, changes in some time-varying factors may cause changes in the case fatality rate, such as: genetic mutations of pathogens over time, changes in the overall immune level of the population over time, the application of treatment measures or special drugs, the supply of medical care and intensive care resources, and environmental exposure factors that change over time. In these cases, the case fatality rate may change. Therefore, calculating the instantaneous case fatality rate or time-varying case fatality rate (rCFRt) of reported cases at time t to explore the changing dynamics of the case fatality rate is of great significance for understanding the temporal pattern of the risk of death, which helps to identify the key determinants affecting the risk of death, thereby formulating corresponding risk management and infectious disease control strategies. Summary of the invention

[0003] The present disclosure provides a method, device and electronic device for estimating the instantaneous reported case fatality rate of infectious diseases, so as to at least solve the above technical problems existing in the prior art.

[0004] According to a first aspect of the present disclosure, a method for estimating the instantaneous reported case fatality rate of an infectious disease is provided, comprising: Based on the first time interval from onset to death The probability distribution function of , and the second time interval from onset to reporting as a case The probability distribution function of , determine the third time interval from when the case was reported to when the death The probability distribution function of ; Based on the third time interval The probability distribution function of ,Sure Total mortality risk for all reported cases at any time ; Based on the Total mortality risk for all reported cases at any time , determine the first moment The corresponding risk of death ; Determine the first moment Corresponding number of deaths due to illness Follows a binomial distribution, where the number of experiments is the first moment The corresponding risk of death , the probability of success of each experiment is The instantaneous reported case fatality rate ; Through the first moment Corresponding number of deaths due to illness , fitting the time series of daily deaths to construct a profile likelihood framework, and estimating the instantaneous reported case fatality rate at any time using the stochastic optimization algorithm based on the profile likelihood function ; Among them, the first moment Including future moments.

[0005] In the above scheme, The first time interval from onset to death The probability distribution function of , and the second time interval from onset to reporting as a case The probability distribution function of , determine the third time interval from when the case was reported to when the death The probability distribution function of ,include: Based on the probability distribution function With probability distribution function The probability distribution function is determined by integrating the product of :

[0006] in, , probability distribution function The value represents the relative At the time, the case The likelihood of death at moment .

[0007] In the above scheme, The method based on the third time interval The probability distribution function of ,Sure Total mortality risk for all reported cases at any time ,include: In response to Time report In the case time, will contribute The likelihood value of The total risk of death The likelihood value of all moments is accumulated:

[0008] In the above scheme, Based on the Total mortality risk for all reported cases at any time , determine the first moment The corresponding risk of death ,include: Regarding the Total mortality risk for all reported cases at any time Discretize and determine the first moment is the lower limit, the first moment The total mortality risk for all reported cases is the upper limit of the day after that. Integrate to get the first moment The corresponding risk of death :

[0009] In the above scheme, Determining the first moment Corresponding number of deaths due to illness Follows a binomial distribution, where the number of experiments is the first moment The corresponding risk of death , the probability of success of each experiment is The instantaneous reported case fatality rate ,include: Determine the first moment Corresponding number of deaths due to illness , the probability mass function is:

[0010] In the above scheme, the first time interval The probability distribution function of and the second time interval The probability distribution function of All are gamma distributions with fixed parameter values.

[0011] According to a second aspect of the present disclosure, a device for estimating the instantaneous reported case fatality rate of an infectious disease is provided, the device comprising: A first determining unit is used to determine a first time interval from onset to death based on The probability distribution function of , and the second time interval from onset to reporting as a case The probability distribution function of , determine the third time interval from when the case was reported to when the death The probability distribution function of ; Continuous total mortality risk determination unit for determining the risk of death based on the third time interval The probability distribution function of ,Sure Total mortality risk for all reported cases at any time ; The instantaneous total mortality risk determination unit is used to determine the total mortality risk based on the Total mortality risk for all reported cases at any time , determine the first moment The corresponding risk of death ; The second determining unit is used to determine the first moment Corresponding number of deaths due to illness Follows a binomial distribution, where the number of experiments is the first moment The corresponding risk of death , the probability of success of each experiment is The instantaneous reported case fatality rate ; Estimation unit, used to pass the first moment Corresponding number of deaths due to illness , fit the time series of daily deaths to construct a profile likelihood framework, and estimate the instantaneous reported case fatality rate at any time using the stochastic optimization algorithm based on the profile likelihood function; Among them, the first moment Including future moments.

[0012] According to a third aspect of the present disclosure, there is provided an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.

[0013] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the present disclosure.

[0014] The instantaneous reported case fatality rate estimation method of infectious diseases disclosed in the present invention is based on the first time interval from onset to death. The probability distribution function of , and the second time interval from onset to reporting as a case The probability distribution function of , determine the third time interval from when the case was reported to when the death The probability distribution function of ; Based on the third time interval The probability distribution function of ,Sure Total mortality risk for all reported cases at any time Based on the Total mortality risk for all reported cases at any time , determine the first moment The corresponding risk of death ; Determine the first moment Corresponding number of deaths due to illness Follows a binomial distribution, where the number of experiments is the first moment The corresponding risk of death , the probability of success of each experiment is The instantaneous reported case fatality rate ; through the first moment Corresponding number of deaths due to illness , fitting the time series of daily deaths to construct a profile likelihood framework, and estimating the instantaneous reported case fatality rate at any time using the stochastic optimization algorithm based on the profile likelihood function ; wherein the first moment Including future moments, the case fatality rate of infectious diseases can be estimated in real time in a forward-looking manner, and the temporal dynamics of the case fatality rate can be fully considered, meeting the real-time needs of risk assessment of emerging infectious diseases and the needs for characterizing dynamic changes in risks.

[0015] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which: In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0017] Figure 1 A first optional flow chart of a method for estimating the instantaneous reported case fatality rate of infectious diseases provided by an embodiment of the present disclosure is shown; Figure 2A second optional flow chart of the method for estimating the instantaneous reported case fatality rate of infectious diseases provided by an embodiment of the present disclosure is shown; Figure 3 The demonstration results for a certain region from February to July 2020 are shown; Figure 4 An optional structural schematic diagram of an apparatus for estimating the instantaneous reported case fatality rate of an infectious disease provided by an embodiment of the present disclosure is shown; Figure 5 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0018] In order to make the purpose, features, and advantages of the present disclosure more obvious and easy to understand, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0019] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0020] In the following description, the terms "first\second" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein.

[0021] Unless otherwise defined, all technical and scientific terms used in this disclosure have the same meaning as those commonly understood by those skilled in the art to which this disclosure belongs. The terms used in this disclosure are only for the purpose of describing the embodiments of this disclosure and are not intended to limit this disclosure.

[0022] It should be understood that in the various embodiments of the present disclosure, the size of the serial number of each implementation process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

[0023] Before further describing the embodiments of the present disclosure in detail, the nouns and terms involved in the embodiments of the present disclosure are described. The nouns and terms involved in the embodiments of the present disclosure are subject to the following interpretations.

[0024] (1) Mortality rate due to disease (abbreviated as case fatality rate, CFR): It indicates the ratio of the number of people who died from a certain disease to the total number of patients in a certain period of time. That is, case fatality rate = number of people who died from a certain disease in a certain period of time / number of patients with a certain disease in the same period of time × 100%.

[0025] (2) Instantaneous case fatality rate / time-varying case fatality rate (CFR t ):exist t The ratio of the number of deaths due to a certain disease to the total number of patients at a certain moment. That is, the instantaneous mortality rate = the number of deaths due to a certain disease at a certain moment / the number of patients suffering from a certain disease at the same moment × 100%.

[0026] (3) Reported case fatality rate (rCFR): the case fatality rate of reported cases.

[0027] (4) Instantaneous reported case fatality rate (rCFR) t ): Reported cases in t The instantaneous case fatality rate at a certain time (for example, the tth day after the outbreak of the epidemic) is referred to as the instantaneous reported case fatality rate in the embodiments of the present disclosure.

[0028] (5) Probability distribution function (PDF): A function that defines the probability distribution of a continuous random variable.

[0029] (6) Likelihood function: A function that defines the probability distribution of model parameter values ​​under known observation data, containing one or more parameters.

[0030] (7) Profile likelihood function: It is the profile curve of the likelihood function as the estimated parameter value changes. It refers to the change in the likelihood function value of the remaining parameters under the condition of fixing one or more parameters. It is used to implement the optimal numerical method for estimating the estimated parameters in a parametric model.

[0031] (8) Reporting delay: refers to the time interval between the occurrence of an infectious disease case and the formal reporting of the case information to the public health department, which may include multiple stages: diagnosis delay, report submission delay, data processing and review delay, etc.

[0032] Usually, people will calculate the average mortality rate in a certain period by dividing the cumulative number of deaths in that period by the cumulative number of confirmed cases based on the definition of mortality rate.

[0033] The prior art proposes a method for estimating the average case fatality rate after correcting the delay in reporting time, that is, the ratio of the cumulative number of deaths from a disease as of a certain time to the sum of the cumulative number of cured cases.

[0034] Accurate and real-time measurement of the risk of death from infectious diseases is crucial for formulating effective public health strategies and response measures. Therefore, there is an urgent need to fill the relevant technical gaps in estimating the case fatality rate of infectious diseases in order to provide more scientific and precise technical support for my country's infectious disease prevention and control work.

[0035] The shortcomings of traditional methods for estimating case fatality rates include: (1) The average mortality rate can only be calculated “retrospectively” after the cases have been reported and the statistics are clear. Therefore, this method cannot provide timely and real-time risk assessment during an epidemic outbreak and cannot meet the actual needs of timeliness in risk assessment of emerging infectious diseases.

[0036] (2) When using this definition to calculate the case fatality rate, there is no correction for reporting delays, that is, the time difference between the occurrence of an infectious disease case and its reporting to the public health department. This may lead to reporting delay bias and affect the accuracy of the case fatality rate calculation.

[0037] The shortcomings of existing technologies for estimating case fatality rates include: (1) It is impossible to estimate the instantaneous reported case fatality rate. During an epidemic, the case fatality rate is not constant, but changes with changes in factors that affect the risk of death. Calculating the case fatality rate as a constant value will conceal a considerable amount of information.

[0038] (2) The parameter estimation process is iteratively complex, has a heavy computational burden, slow convergence, and increases the time cost of the computational process.

[0039] Therefore, in order to estimate the case fatality rate in real time in a "prospective" (rather than "retrospective") manner and meet the actual needs of timeliness in risk assessment of emerging infectious diseases; during the epidemic of emerging infectious diseases, due to the time delay in reporting of cases and deaths, it is necessary to correct the estimation bias caused by the reporting time delay when estimating the case fatality rate; the estimation of the instantaneous reported case fatality rate is a real-time estimate of the case fatality rate for each time period, which contains richer information than the average case fatality rate and can more accurately reflect the change in the severity of the disease over time during the evolution of the infectious disease epidemic. The embodiments of the present disclosure provide an estimation method for the instantaneous reported case fatality rate of infectious diseases to overcome the shortcomings of traditional parameter estimation methods, such as the non-existence of an analytical expression for the derivative of the likelihood function, the difficulty in finding the optimal solution, and the slow convergence speed.

[0040] Figure 1 A first optional flow chart of the method for estimating the instantaneous reported case fatality rate of infectious diseases provided by an embodiment of the present disclosure is shown, and will be explained according to each step.

[0041] Step S101, based on the first time interval from onset to death The probability distribution function of , and the second time interval from onset to reporting as a case The probability distribution function of , determine the third time interval from when the case was reported to when the death The probability distribution function of .

[0042] In some embodiments, and It is usually set to a fixed probability distribution, for example, and All can be set to a gamma distribution with fixed parameter values. And slight changes in initial conditions or similar settings will not affect the main estimation results.

[0043] In specific implementation, the carrier that implements the estimation method of the instantaneous reported case fatality rate of infectious diseases (hereinafter referred to as the carrier) can obtain the time series of the number of cases and deaths reported daily by infectious diseases based on monitoring data, and set the initial conditions of the model framework with reference to the results of previous studies to determine The mean (± standard deviation) of The mean values ​​(± standard deviation) of

[0044] In some embodiments, the vector is based on a probability distribution function With probability distribution function The probability distribution function is determined by integrating the product of :

[0045] in, , probability distribution function The value represents the relative At the time, the case The likelihood of death at moment .

[0046] In some embodiments, the carrier can be a computer program, electronic circuit, database, mobile application, electronic device, cloud computing platform, distributed system, artificial intelligence framework, mathematical model, automation tool and microcontroller, etc., which can implement software or hardware of algorithm and method flow.

[0047] Step S102: based on a third time interval The probability distribution function of ,Sure Total mortality risk for all reported cases at any time .

[0048] In some embodiments, each disease-related death develops from a diagnosed case, so each case can be considered a potential "source of death" (or "death pool"), that is, a subject at risk of death. The present disclosure regards all reported cases as a "pool" of deaths, and models this candidate "pool" as a time-varying function, Time express.

[0049] In response to Time report In the case time, will contribute The likelihood value of The total risk of death for All moments before moment ( ) contributes to the cumulative likelihood:

[0050] in, is a positive integer; a function that changes with time is The total mortality risk of all reported cases at any time. It is a continuous function that changes with time and is used to describe the , the sum of the risk of death from the disease for all reported cases.

[0051] Step S103, based on the Total mortality risk for all reported cases at any time , determine the first moment The corresponding risk of death .

[0052] In some embodiments, the carrier is Total mortality risk for all reported cases at any time Discretize and determine the first moment is the lower limit, the first moment The total mortality risk for all reported cases is the upper limit of the day after that. Integrate to get the first moment The corresponding risk of death :

[0053] Among them, the risk of death Corresponding to the first moment Start to The mortality risk of all reported cases at the end of the day.

[0054] Step S104, determine the first moment Corresponding number of deaths due to illness It follows the binomial distribution.

[0055] In some embodiments, determining the first time Corresponding number of deaths due to illness ,Right now , its probability mass function is:

[0056] Among them, the instantaneous reported case fatality rate is the parameter to be estimated in the embodiment of the present disclosure.

[0057] Step S105, through the first moment Corresponding number of deaths due to illness , fitting the time series of daily deaths to construct a profile likelihood framework, and estimating the instantaneous reported case fatality rate at any time using the stochastic optimization algorithm based on the profile likelihood function.

[0058] In some embodiments, the vector is constructed by fitting a time series of daily deaths A profile likelihood framework was constructed, and the "stochastic optimization method based on profile likelihood function" was used to estimate the mortality rate of instantaneous reported cases. The 95% confidence interval of the mortality rate of instantaneous reported cases was calculated by using the profile likelihood estimation framework, and the chi-square quantile was used to determine the upper and lower confidence limits of the confidence interval.

[0059] In a specific implementation, the carrier determines each first moment Corresponding number of deaths due to illness , and obtain a time series of daily deaths.

[0060] In a specific implementation, the vector determines that the parameter to be estimated is the instantaneous reported case fatality rate , then determine the given observation data The likelihood function is a time series of the number of deaths per day. The product of the probability mass functions of For the parameter to be profiled, other parameters are considered fixed. For other fixed parameter values, the estimated parameter value that maximizes the likelihood function is found by varying the estimated parameter. For all possible parameter values, the corresponding maximum likelihood value is calculated to construct a profile likelihood curve. By finding the extreme value of the profile likelihood curve and the likelihood ratio method, the confidence interval of the parameter can be estimated.

[0061] Thus, by setting a specific first moment , the corresponding first moment can be obtained by the algorithm provided by the embodiment of the present disclosure The instantaneous reported case fatality rate; if the first moment If it is set to a future moment (i.e. a moment that has not yet occurred), the corresponding instantaneous reported case fatality rate will be the instantaneous reported case fatality rate at the future moment. This can prospectively estimate the case fatality rate of infectious diseases in real time and fully consider the temporal dynamics of the case fatality rate, thus meeting the real-time requirements for risk assessment of emerging infectious diseases and the need to characterize dynamic changes in risks.

[0062] Figure 2 A second optional flow chart of the method for estimating the instantaneous reported case fatality rate of infectious diseases provided in an embodiment of the present disclosure is shown and will be explained according to each step.

[0063] Step S201, constructing a third time interval from being reported as a case to death The probability distribution function of .

[0064] In some embodiments, The third time interval from when the case was reported to when the death occurred, if any, is expressed as f (·) indicates the third time interval The probability distribution function of represents the first time interval from onset to death, and its probability distribution function is to express; to use represents the second time interval from onset to being reported as a case, and its probability distribution function is The third time interval can be simulated as minus ,Right now , then, the third time interval The probability distribution function is , which can be expressed as:

[0065] Therefore, if in If a case is reported at time , and the final outcome of the case is disease-related death, then the function The value represents the value relative to time (the time when it was reported as a case), the case The possibility of death at any moment.

[0066] In some embodiments, when using the disclosed embodiments to estimate instantaneous reported case fatality rate, it is necessary to set the initial conditions of the model framework, where and It is usually set to a fixed probability distribution, for example, and All can be set to a gamma distribution with fixed parameter values. And slight changes in initial conditions or similar settings will not affect the main estimation results.

[0067] Step S202: construct a database of all reported cases. The time-varying function of the total mortality risk at time .

[0068] In some embodiments, each disease-related death develops from a diagnosed case, so each case can be considered a potential "source of death" (or "death pool"), that is, a subject at risk of death. In the disclosed embodiment, all reported cases are considered as a candidate "pool" for generating death, and this candidate "pool" is modeled as a time-varying function. Time Indicates. Then, The report Cases, in time, will contribute The likelihood value of The sum of the contributions of all reported cases is :

[0069] Functions that vary over time is The total mortality risk of all reported cases at any time. It is a continuous function that changes with time and is used to describe the , the sum of the risk of death from the disease for all reported cases.

[0070] Step S203, constructing a profile likelihood framework to estimate the instantaneous reported case fatality rate.

[0071] Reported case fatality rate (rCFR), i.e., the case fatality rate of reported cases, The instantaneous reported case fatality rate at time ) can be = / Calculated, At a specific point in time (For example: The number of deaths due to illness observed at different time points can be directly obtained from infectious disease surveillance data. on The values ​​are arranged in chronological order to form a time series. yes Time (for example: Day Discrete expression of Function at a specific point in time Sampling is performed on the Value:

[0072] Will It is modeled as a classic binomial process with parameters (rounded to the nearest integer) and (to be estimated), namely: , its probability mass function is:

[0073] By fitting a time series of daily deaths A profile likelihood framework was constructed, and the "random optimization method based on profile likelihood function" was used to estimate the instantaneous reported case fatality rate. The 95% confidence interval of the instantaneous reported case fatality rate was calculated by using the profile likelihood estimation framework, and the chi-square quantile was used to determine the upper and lower confidence limits of the confidence interval.

[0074] In this way, the method for estimating the mortality rate of infectious diseases reported instantly provided by the embodiments of the present disclosure breaks the traditional idea of ​​"retrospective" estimation of mortality rate, no longer relies on the completion of case reporting and complete statistical clarity of data, and can track and estimate the mortality rate in real time in a "forward-looking" manner during the outbreak, meeting the actual needs of timeliness of risk assessment of emerging infectious diseases. The time-varying function of the total mortality risk at time To estimate the instantaneous mortality rate, and to construct a time-varying function When calculating the mortality rate, the time of the reporting delay is taken into account, so the bias related to the reporting delay can be corrected. Compared with calculating the mortality rate as a constant value, the instantaneous reported mortality rate calculated by the embodiment of the present disclosure reveals the temporal variation dynamics of the mortality rate, which helps to understand the temporal pattern of the risk of death from infectious diseases. Further analysis of the changes in the temporal trend of the mortality rate can propose hypotheses about its association with potential determinants, providing a scientific basis for identifying the key determinants affecting the risk of death and formulating corresponding epidemic prevention and control measures. The parameter estimation method of the embodiment of the present disclosure adopts a "random optimization method based on the profile likelihood function". This method uses a random search strategy to gradually approach the optimal solution through an iterative process without relying on derivative function information, overcoming the shortcomings of the traditional parameter estimation method (which requires calculating the derivative of the likelihood function, and then solving the solution where the derivative function is zero to find the optimal solution) that the analytical expression of the derivative of the likelihood function does not exist, the optimal solution is difficult to find, and the convergence speed is too slow.

[0075] Next, we will demonstrate using the monitoring data of an emerging infectious disease in a certain area as an example.

[0076] Based on the monitoring data, we can obtain the time series of the number of cases and deaths reported daily for this emerging infectious disease, and refer to the results of previous studies to set the initial conditions of the model framework. The mean (± standard deviation) is set to be a gamma distribution of 20 days (± 10 days); Gamma distribution was set with a mean (± standard deviation) of 7 days (± 4 days).

[0077] Since the first death case was reported on March 11, 2020, this technology was used to fit the time series of the number of deaths and estimate the instantaneous reported case mortality rate, ranging from 0% to 23.8%, and the average reported case mortality rate of this emerging infectious disease was estimated to be 6.9% (95% CI: 4.5-10.6).

[0078] Figure 3 The demonstration results for a certain region from February to July 2020 are shown. Figure 3 (A) is the epidemic curve of the number of daily cases reported from February to July 2020. Figure 3 (B) is the epidemic curve of the number of deaths reported daily from February to July 2020. Figure 3 (C) is the time series of daily reported case fatality rate (i.e., daily instantaneous reported case fatality rate) estimated by the estimation method of instantaneous reported case fatality rate of infectious diseases provided by the embodiments of the present disclosure.

[0079] The segmented logistic regression model was used to analyze the temporal trend of mortality among instantaneous reported cases: Figure 3 As shown in (C), by using a segmented logistic regression model, a change in the temporal trend of the reported case fatality rate, i.e., a structural break, was detected, which was most likely to occur around May 3, 2020. Before May 3, the reported case fatality rate showed a significant upward trend, with an odds ratio (OR) increasing by 1.08 (95% CI: 1.03-1.14) per week, while after May 3, the reported case fatality rate showed a significant downward trend, with an odds ratio (OR) decreasing by 0.90 (95% CI: 0.87-0.93) per week. The external factors that affect the change in the temporal trend of the reported case fatality rate can be further analyzed, so as to propose corresponding speculative hypotheses.

[0080] Figure 4 An optional structural schematic diagram of an apparatus for estimating the instantaneous reported case fatality rate of an infectious disease provided by an embodiment of the present disclosure is shown and will be described according to each part.

[0081] In some embodiments, the device 400 for estimating the instantaneous reported case fatality rate of an infectious disease includes a first determination unit 401 , a continuous total death risk determination unit 402 , an instantaneous total death risk determination unit 403 , a second determination unit 404 and an estimation unit 405 .

[0082] The first determining unit 401 is configured to determine a first time interval from onset to death based on The probability distribution function of , and the second time interval from onset to reporting as a case The probability distribution function of , determine the third time interval from when the case was reported to when the death The probability distribution function of ; The continuous total death risk determination unit 402 is used to determine the total death risk based on the third time interval. The probability distribution function of ,Sure Total mortality risk for all reported cases at any time ; The instantaneous total death risk determination unit 403 is used to determine the total death risk based on the Total mortality risk for all reported cases at any time , determine the first moment The corresponding risk of death ; The second determining unit 404 is used to determine the first moment Corresponding number of deaths due to illness , which follows a binomial distribution, where the number of experiments is the first moment The corresponding risk of death , the probability of success of each experiment is The instantaneous reported case fatality rate ; The estimation unit is used to Corresponding number of deaths due to illness , fit the time series of daily deaths to construct a profile likelihood framework, and estimate the instantaneous reported case fatality rate at any time using the stochastic optimization algorithm based on the profile likelihood function; Among them, the first moment Including future moments.

[0083] The first determining unit 401 is specifically configured to determine the probability distribution function , is the probability distribution function With probability distribution function The integral of the product of :

[0084] in, , probability distribution function The value represents the relative τ At the time, the case The likelihood of death at moment .

[0085] The continuous total death risk determination unit 402 is specifically configured to respond to Time report In the case time, will contribute The likelihood value of The total risk of death The likelihood value of all moments is accumulated:

[0086] The instantaneous total death risk determination unit 403 is specifically configured to determine the Total mortality risk for all reported cases at any time Discretize and determine the first moment is the lower limit, the first moment The total mortality risk for all reported cases is the upper limit of the day after that. Integrate to get the first moment The corresponding risk of death :

[0087] The second determining unit 404 is specifically configured to determine the first moment Corresponding number of deaths due to illness , the probability mass function is:

[0088] In some embodiments, the first time interval The probability distribution function of and the second time interval The probability distribution function of All are gamma distributions with fixed parameter values.

[0089] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0090] Figure 5A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0091] like Figure 5 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0092] Multiple components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0093] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the estimation method of the instantaneous report fatality rate of infectious diseases. For example, in some embodiments, the estimation method of the instantaneous report fatality rate of infectious diseases may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the estimation method of the instantaneous report fatality rate of infectious diseases described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute a method for estimating the instantaneous reported case fatality rate of an infectious disease in any other appropriate manner (eg, by means of firmware).

[0094] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0096] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0098] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0099] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0100] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0101] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0102] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for estimating the instantaneous case fatality rate of infectious diseases, characterized in that: The method comprises: Based on the first time interval from onset to death The probability distribution function of , and the second time interval from onset to reporting as a case The probability distribution function of , determine the third time interval from when the case was reported to when the death The probability distribution function of ; Based on the third time interval The probability distribution function of ,Sure Total mortality risk for all reported cases at any time ; Based on the Total mortality risk for all reported cases at any time , determine the first moment The corresponding risk of death ; Determine the first moment Corresponding number of deaths due to illness Follows a binomial distribution, where the number of experiments is the first moment The corresponding risk of death , the probability of success of each experiment is The instantaneous reported case fatality rate ; Through the first moment Corresponding number of deaths due to illness , fitting the time series of daily deaths to construct a profile likelihood framework, and estimating the instantaneous reported case fatality rate at any time using the stochastic optimization algorithm based on the profile likelihood function ; Among them, the first moment Including future moments.

2. The method according to claim 1, characterized in that The first time interval from onset to death The probability distribution function of , and the second time interval from onset to reporting as a case The probability distribution function of , determine the third time interval from when the case was reported to when the death The probability distribution function of ,include: Based on the probability distribution function With probability distribution function The probability distribution function is determined by integrating the product of : in, , probability distribution function The value represents the relative At the time, the case The likelihood of death at moment .

3. The method according to claim 1, characterized in that The method based on the third time interval The probability distribution function of ,Sure Total mortality risk for all reported cases at any time ,include: In response to Time report In the case time, will contribute The likelihood value of The total risk of death The likelihood value of all moments is accumulated: 。 4. The method according to claim 1, characterized in that: Based on the Total mortality risk for all reported cases at any time , determine the first moment The corresponding risk of death ,include: Regarding the Total mortality risk for all reported cases at any time Discretize and determine the first moment is the lower limit, the first moment The total mortality risk for all reported cases is the upper limit of the day after that. Integrate to get the first moment The corresponding risk of death : 。 5. The method according to claim 1, characterized in that Determining the first moment Corresponding number of deaths due to illness Follows a binomial distribution, where the number of experiments is the first moment The corresponding risk of death , the probability of success of each experiment is The instantaneous reported case fatality rate ,include: Determine the first moment Corresponding number of deaths due to illness , the probability mass function is: 。 6. The method according to claim 1, characterized in that First time interval The probability distribution function of and the second time interval The probability distribution function of All are gamma distributions with fixed parameter values.

7. A device for estimating the instantaneous case fatality rate of infectious diseases, characterized in that: The device comprises: A first determining unit is used to determine a first time interval from onset to death based on The probability distribution function of , and the second time interval from onset to reporting as a case The probability distribution function of , determine the third time interval from when the case was reported to when the death The probability distribution function of ; Continuous total mortality risk determination unit for determining the risk of death based on the third time interval The probability distribution function of ,Sure Total mortality risk for all reported cases at any time ; The instantaneous total mortality risk determination unit is used to determine the total mortality risk based on the Total mortality risk for all reported cases at any time , determine the first moment The corresponding risk of death ; The second determining unit is used to determine the first moment Corresponding number of deaths due to illness Follows a binomial distribution, where the number of experiments is the first moment The corresponding risk of death , the probability of success of each experiment is The instantaneous reported case fatality rate ; Estimation unit, used to pass the first moment Corresponding number of deaths due to illness , fit the time series of daily deaths to construct a profile likelihood framework, and estimate the instantaneous reported case fatality rate at any time using the stochastic optimization algorithm based on the profile likelihood function; Among them, the first moment Including future moments.

8. The device according to claim 7, characterized in that The first determining unit is specifically configured to: Based on the probability distribution function With probability distribution function The probability distribution function is determined by integrating the product of : in, , probability distribution function The value represents the relative At the time, the case The likelihood of death at moment .

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to make a computer execute the method according to any one of claims 1-6.

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