A general method for predicting infectious diseases with asymptomatic carriers

Abstract

The application discloses a general prediction method for infectious diseases with asymptomatic infected persons, belongs to the technical field of infectious disease prediction, and comprises the following steps: acquiring known epidemic data of a target infectious disease in a fixed time period; optimizing a basic SEIR model in terms of warehouse structure and dynamic propagation process to obtain an SEIRAQHCD model for predicting the infectious ability of the target infectious disease; performing parameter estimation and model fitting on the SEIRAQHCD model according to a least square algorithm, verifying the model through the known epidemic data to obtain fitting parameters and a fitting image, and realizing prediction of epidemic data of the target infectious disease; and the application model not only meets the accuracy of the prediction result, but also considers whether the application model is suitable for use of other viruses of the same type, so that the application model can be used to predict viruses with the same characteristics in the early stage of the outbreak, and reference data can be quickly obtained to provide effective help.

Description

Technical Field

[0001] This invention belongs to the field of infectious disease prediction technology and relates to a general prediction method for infectious diseases in asymptomatic infected individuals. Background Technology

[0002] The spread of infectious diseases has always been a topic of discussion, as it not only severely impacts people's normal lives but also affects social stability. Some infectious diseases involve a special group of asymptomatic carriers—people who carry the virus but have not yet shown symptoms. Although asymptomatic carriers have mild or no symptoms, they still possess a certain degree of infectivity, posing a significant obstacle to disease prevention and control. Transmission dynamics models include SI, SIR, and SEIR, among others. The SEIR model divides the population into four categories: susceptible individuals, infected individuals, recovered individuals, and deceased individuals. While the traditional SEIR model is simple, different infectious diseases have different transmission characteristics, requiring summarization and improvement of the basic model based on actual situations.

[0003] Several patents currently relate to methods and systems for predicting infectious diseases. Some involve constructing infectious disease models based on logistic models and optimizing their parameters. These optimized parameters are then used to improve the SEIR model's formula, replacing the original immutable recovery rate with a recovery rate regression formula to predict the number of infections. Others modify the SEIR model into a SEIRD cell model, categorizing individuals into susceptible individuals, asymptomatic latent carriers, symptomatic carriers, recovered individuals, and deceased individuals. The conversion rate of susceptible individuals is divided into infection rates during the incubation period and infection rates in the confirmed cases. The removal rate is further subdivided into mortality and recovery rates. The least squares method is used to estimate parameters and fit the model, predicting the inflection point and end date of the virus. Still others add an incubation period conversion rate parameter based on the characteristics of asymptomatic carriers, optimizing pre-set parameters using historical infectious disease data. The optimized parameters and preset initial values ​​are then input into the SEIR model to obtain the prediction results for the target infectious disease.

[0004] Existing predictive models have the following drawbacks: First, most are overly simplistic in their design of transmission chambers, merely altering the transmission formula and failing to comprehensively address the characteristics of asymptomatic infections. Second, most applications only simulate and predict the spread of a single virus, emphasizing specificity while neglecting universality. Effective infectious disease prediction models must not only meet the accuracy requirements but also be suitable for use with other viruses of the same type, enabling them to predict the early stages of infection with viruses exhibiting similar characteristics and quickly obtain relevant reference data for effective assistance. Summary of the Invention

[0005] To address the above problems, the present invention provides a technical solution: a general prediction method for infectious diseases in asymptomatic individuals, characterized by comprising the following steps:

[0006] Obtain known epidemic data of the target infectious disease within a fixed time period;

[0007] The basic SEIR model was optimized in terms of compartment structure and dynamic transmission process to obtain the SEIRAQHCD model for predicting the infectivity of target infectious diseases;

[0008] The SEIRAQHCD model is subjected to parameter estimation and model fitting using the least squares algorithm. The fitted parameters and fitted images are obtained by validating the model using known epidemic data, thereby enabling the prediction of epidemic data for the target infectious disease.

[0009] Furthermore, the epidemic data includes the cumulative number of confirmed cases and the cumulative number of deaths.

[0010] Furthermore, the expression for the SEIRAQHCD model is as follows:

[0011]

[0012]

[0013]

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020] N = S + E + I + R + A + Q + H + C + D (15)

[0021] Wherein: S represents susceptible individuals, E represents latent individuals (who will develop into symptomatic or asymptomatic carriers after the incubation period), I represents confirmed symptomatic carriers, R represents recovered individuals, A represents asymptomatic carriers (those who have been infected but have not shown obvious symptoms and have a certain ability to spread the disease), Q represents those in quarantine (suspected cases identified through various means and currently under surveillance, not yet capable of spreading the disease), H represents hospitalized individuals (patients who have been infected and reached a severe stage and require strict hospital treatment), C represents suspected close contacts (people whose travel history is related to those of infected individuals but whose infection status is not yet confirmed), and D represents deceased individuals (patients who died from the disease).

[0022] β represents the transmission rate; symptomatic and asymptomatic individuals have varying degrees of transmissibility. inc The incubation period refers to the time from when an asymptomatic carrier becomes infected. During this period, the asymptomatic carrier may develop into one of two states: symptomatic or asymptomatic. λ represents the observation period for suspected close contacts, a represents the proportion of asymptomatic carriers among the asymptomatic carriers, h represents the proportion of severely ill infected individuals, f represents the mortality rate, c represents the number of potential contacts, q represents the isolation rate, and r represents the mortality rate. h r represents the cure rate of hospitalized patients i represents the cure rate of non-hospitalized patients, and m represents the modulatory factor for the transmissibility of asymptomatic infected individuals.

[0023] Furthermore, the SEIRAQHCD model makes the following assumptions: the environment of the target infectious disease is regarded as a closed environment, the natural birth and death rates of the population and the immigration and emigration of the population are ignored, and each individual has the same probability of contact and the same probability of being infected.

[0024] Due to limited medical resources, it is impossible to guarantee that all patients can receive inpatient treatment. A certain proportion of infected individuals with severe symptoms are classified as hospitalized. The transmission capacity of symptomatic and asymptomatic infected individuals is different. The emergence of super-spreaders is not considered. Infected individuals who are hospitalized or in isolation or observation are not infectious.

[0025] Furthermore, it also includes using a nonlinear Richards regression model to correct errors in the epidemic data.

[0026] A universal predictive device for infectious diseases in asymptomatic individuals, comprising:

[0027] Acquisition module: Used to acquire known epidemic data of the target infectious disease within a fixed time period;

[0028] Optimization module: used to optimize the basic SEIR model in terms of compartment structure and dynamic transmission process, to obtain the SEIRAQHCD model for predicting the infectivity of target infectious diseases;

[0029] Fitting and Prediction Module: This module is used to perform parameter estimation and model fitting of the SEIRAQHCD model according to the least squares algorithm. It obtains the fitting parameters and fitting image by verifying the model using known epidemic data, thereby enabling the prediction of epidemic data of the target infectious disease.

[0030] A computer-readable storage medium storing a computer program that, when executed by a processor, implements a general prediction method for infectious diseases in asymptomatic individuals as described in any one of the claims.

[0031] An electronic device, comprising:

[0032] One or more processors;

[0033] Storage device, storing one or more programs;

[0034] When one or more programs are executed by the one or more processors, the one or more processors implement a general prediction method for infectious diseases in asymptomatic individuals as described in any one of them.

[0035] This invention provides a general prediction method for infectious diseases in asymptomatic carriers. It can be applied to predict the spread of infectious diseases in asymptomatic carriers. By fitting a model, the method obtains transmission data and uses the fitting results to understand the short-term development, providing a scientific basis for subsequent preventative measures. This method improves the basic SEIR model, designing a SEIRAQHCD transmission model with nine states. This model, encompassing multiple transmission states, can more effectively simulate real-world transmission, obtain relevant parameters, and predict short-term transmission. This model is primarily used to predict the transmission trend of infectious diseases in asymptomatic carriers. Historical viral data is substituted into the model for fitting to derive short-term transmission trends and the fitted transmission parameters. Firstly, this application features the classification of infected individuals into asymptomatic and symptomatic states, and adjusts factors to set different transmissibility levels, which better reflects the characteristics of transmission. Secondly, since many states are involved in the entire transmission chain, this application adds various states such as close contacts, asymptomatic carriers, those in isolation, and hospitalized individuals, making the dynamic switching between these states more practical. Therefore, the model design should incorporate relevant states and design the dynamic transmission process between various states, obtaining specific parameters through fitting, which is more realistic and valuable for infectious disease research. Thirdly, this invention does not model a specific virus, but rather conducts a general model study on infectious diseases with similar characteristics. The model in this application must not only meet the accuracy of the prediction results, but also consider its suitability for use with other similar viruses, enabling the model to predict in the early stages of the disease of viruses with similar characteristics, quickly obtaining reference data to provide effective assistance. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 Flowchart of the SEIRAQHCD model;

[0038] Figure 2 Image showing the number of confirmed COVID-19 cases in a certain province before correction;

[0039] Figure 3 Image corrected to show the number of confirmed COVID-19 cases in a certain province;

[0040] Figure 4 Fit an image to the number of confirmed cases of Virus A in a certain province;

[0041] Figure 5Fit an image to the number of deaths from Virus A in a certain province;

[0042] Figure 6 Fit an image to the number of confirmed cases of Virus B in a certain administrative region;

[0043] Figure 7 Fit an image to the number of deaths from Virus B in a certain administrative region;

[0044] Figure 8 A fitted image representing the number of confirmed H1N1 virus cases in Mexico;

[0045] Figure 9 An image fitted to the number of deaths from the H1N1 virus in Mexico. Detailed Implementation

[0046] It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0049] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0050] In the description of this invention, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is generally based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this invention and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this invention. The directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0051] For ease of description, spatial relative terms such as "above," "over," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation besides the orientation of the device as described in the figures. For example, if the device in the figures is inverted, a device described as "above" or "above" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0052] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0053] A general method for predicting infectious diseases in asymptomatic individuals includes the following steps:

[0054] S1: Obtain known epidemic data of the target infectious disease within a fixed time period; the target infectious disease is characterized by asymptomatic infections;

[0055] S2: The basic SEIR model is optimized in terms of compartment structure and dynamic transmission process to obtain the SEIRAQHCD model for predicting the transmissibility of target infectious diseases. For example, asymptomatic infected persons, although they do not have obvious symptoms after the incubation period, still have the ability to transmit the disease and need to be medically isolated. Different transmission rates are set for symptomatic and asymptomatic infected persons. Considering the limited medical resources, individuals with severe symptoms are considered as hospitalized.

[0056] S3: The SEIRAQHCD model is subjected to parameter estimation and model fitting using the least squares algorithm. The fitted parameters and fitted image are obtained by validating the model using known epidemic data, thereby enabling the prediction of epidemic data of the target infectious disease.

[0057] The steps S1 / S2 / S3 are executed sequentially;

[0058] The epidemic data includes the cumulative number of confirmed cases and the cumulative number of deaths.

[0059] In the traditional SEIR model, the subjects are divided into four categories: susceptible individuals, latent individuals, infected individuals, and recovered individuals, resulting in a simple transmission process.

[0060] The basic infectious disease model SEIR is constructed from the following set of equations:

[0061]

[0062]

[0063]

[0064]

[0065] N = S + E + I + R (5)

[0066] Where N represents the total population, β represents the transmission rate from susceptible individuals to infected individuals, infectious diseases have an incubation period, and susceptible individuals who have been in contact with infected individuals do not immediately become ill but become carriers of the pathogen, i.e., latent individuals, α represents the probability that a latent individual will become an infected individual, and γ represents the cure rate of infected individuals recovering to health. This model does not consider the transmissibility of latent individuals, nor does it consider the introduction of virus control measures.

[0067] While the basic model is simple, different infectious diseases have different transmission characteristics, requiring generalization and improvement based on actual situations. Assuming a constant total population N, we consider that most viruses have an incubation period, and people in the incubation period also have the ability to transmit the virus; asymptomatic carriers, although not showing symptoms, are also virus carriers and have the ability to transmit the virus. These two groups are important reasons for the rapid outbreak of the virus in society.

[0068] The expression for the SEIRAQHCD model is as follows:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] N = S + E + I + R + A + Q + H + C + D (15)

[0079] Wherein: S represents susceptible individuals, E represents latent individuals (who will develop into symptomatic or asymptomatic carriers after the incubation period), I represents confirmed symptomatic carriers, R represents recovered individuals, A represents asymptomatic carriers (those who have been infected but have not shown obvious symptoms and have a certain ability to spread the disease), Q represents those in quarantine (suspected cases identified through various means and currently under surveillance, not yet capable of spreading the disease), H represents hospitalized individuals (patients who have been infected and reached a severe stage and require strict hospital treatment), C represents suspected close contacts (people whose travel history is related to those of infected individuals but whose infection status is not yet confirmed), and D represents deceased individuals (patients who died from the disease).

[0080] In this SEIRAQHCD model, it is assumed that latent individuals, infected individuals, and asymptomatic infected individuals all have the potential to transmit the virus, β represents the transmission rate probability, and it is further assumed that the transmission rate remains constant throughout the transmission process. Symptomatic and asymptomatic infected individuals possess different degrees of transmission capacity, tinc The incubation period refers to the time from when an asymptomatic carrier becomes infected. During this period, the asymptomatic carrier may develop into one of two states: symptomatic or asymptomatic. λ represents the observation period for suspected close contacts, a represents the proportion of asymptomatic carriers among the asymptomatic carriers, h represents the proportion of severely ill infected individuals, f represents the mortality rate, c represents the number of potential contacts, q represents the isolation rate, and r represents the mortality rate. h r represents the cure rate of hospitalized patients i represents the cure rate of non-hospitalized patients, and m represents the modulatory factor for the transmissibility of asymptomatic infected individuals.

[0081] Considering the possibility of susceptible individuals experiencing "false normality" after contact with infected individuals, the concept of a suspected close contact is introduced, referring to individuals who have had contact with an infected person but are unsure whether they have been infected. Therefore, suspected close contacts should be isolated and observed. If they do not test positive for the virus after the observation period, it indicates they are not infectious and can return to the susceptible population, but they still face the risk of reinfection. The average observation period is taken as... The interventions considered in the SEIRAQHCD model, such as contact tracing and isolation of infected individuals, are constrained by set control parameters. It is assumed that the average incubation period of the virus is... After the incubation period, patients infected with the virus will transition from the latent state to the infectious state. In epidemiology, asymptomatic infections are not uncommon; therefore, latent individuals may transition to one of two states after the incubation period: symptomatic infections (i.e., infected individuals), accounting for (1-a), with severe cases requiring hospital treatment; and asymptomatic infections, accounting for a. Since asymptomatic individuals show no symptoms for a long period and are only detected during large-scale testing, it is assumed that their isolation rate is the same as that of latent individuals. Undetected asymptomatic individuals carry antibodies, and the cure rate for this group is not considered. Due to limited medical resources, severely ill infected individuals and those in isolation are considered hospitalized, accounting for h. Recovered individuals are divided into two categories: those with mild symptoms who recover through basic treatment, and those with severe symptoms (i.e., hospitalized individuals) who recover through hospital ward treatment. Deaths are only considered from hospitalized individuals; deaths from non-infectious diseases are not considered.

[0082] The SEIRAQHCD model assumes the following: the environment of the target infectious disease is considered as a closed environment, ignoring the natural birth and death rates of the population as well as the immigration and emigration of the population, and that each individual has the same probability of contact and the same probability of being infected.

[0083] Due to limited medical resources, it is impossible to guarantee that all patients can receive inpatient treatment. Therefore, severely ill infected individuals are classified as hospitalized based on a certain proportion (this proportion is unknown and is constrained by parameters ranging from 0 to 1; the data for different viruses can be determined by fitting the parameters). The transmissibility of symptomatic and asymptomatic infected individuals differs. The emergence of super-spreaders is not considered. Infected individuals who are hospitalized or in isolation / observation are not infectious.

[0084] For infectious disease data with errors, a pre-fit correction process is performed. For example, regarding Virus A in this example, since February 12, 2022, with the deepening understanding of Virus A pneumonia and the accumulation of clinical experience, "clinical diagnosis" was added to the case diagnosis classification in a certain province. This indicates that the previous number of confirmed cases was lower than the actual value. Since the growth trend of confirmed cases can be approximated as an S-shaped biological growth trend, a nonlinear Richards regression model is used to correct the error in the confirmed data. This model simulates a biological growth curve and is suitable for fitting an approximate S-shaped curve image. Since the actual growth curve of the number of confirmed cases of Virus A is approximately S-shaped, this model is chosen to calculate and approximate the data before February 13th based on the jump point, i.e., the data after February 13th. This satisfies the consistency and coherence of the data before and after the jump, and also achieves the purpose of correcting the earlier data. The specific process is based on Richards' calculation formula and derived through MATLAB algorithm code.

[0085] A universal predictive device for infectious diseases in asymptomatic individuals, comprising:

[0086] Acquisition module: Used to acquire known epidemic data of the target infectious disease within a fixed time period;

[0087] Optimization module: used to optimize the basic SEIR model in terms of compartment structure and dynamic transmission process, to obtain the SEIRAQHCD model for predicting the infectivity of target infectious diseases;

[0088] Fitting and Prediction Module: This module is used to perform parameter estimation and model fitting of the SEIRAQHCD model according to the least squares algorithm. It obtains the fitting parameters and fitting image by verifying the model using known epidemic data, thereby enabling the prediction of epidemic data of the target infectious disease.

[0089] A computer-readable storage medium storing a computer program, characterized in that: when the computer program is executed by a processor, it implements a general prediction method for infectious diseases in asymptomatic individuals as described in any one of the claims.

[0090] An electronic device includes: one or more processors; a storage device for storing one or more programs; and when the one or more programs are executed by the one or more processors, the one or more processors implement a general prediction method for infectious diseases in asymptomatic individuals as described in any one of the claims.

[0091] To demonstrate the effectiveness and universality of this application, A virus transmission data, B virus transmission data, and H1N1 virus transmission data were used and fitted with SEIRAQHCD model evolution data.

[0092] Because the data reported in the early stages of the A virus outbreak was not entirely accurate. Figure 2 Image showing the number of confirmed COVID-19 cases in a certain province before correction; Figure 3 The image shows the corrected number of confirmed cases of Virus A in a certain province. After data correction, the curve of confirmed cases tends to be smoother and conforms to the basic trend of epidemic development. The fourth-order Runge-Kutta method and the least squares estimation method are used to fit the model-generated data with the actual data. The initial values ​​of the model are set to I = 293, R = 18, and D = 17.

[0093] Figure 4 Fit an image to the number of confirmed cases of Virus A in a certain province; Figure 5 A fitted image was created to represent the number of deaths from Virus A in a certain province. The fitted image shows a good fit for both cumulative confirmed cases and cumulative deaths, indicating that the model can describe the transmission process of Virus A. The fitted image suggests an incubation period of approximately 3 days and an observation period of approximately 14 days, consistent with the conclusion in relevant literature that the virus has a long incubation period. Figure 4 The results show that the number of confirmed cases rose slowly after 40 days, indicating that external interventions such as screening suspected close contacts and isolating potential infected persons, namely asymptomatic carriers and latent individuals, played a key role in controlling the source of infection. This also demonstrates the significance of introducing relevant parameters into the model.

[0094] Table 1. Predicted Number of Confirmed Cases of Virus A in a Certain Province

[0095]

[0096] Table 2. Predicted Number of Deaths from Virus A in a Certain Province

[0097]

[0098] As can be seen from the prediction error rates in Tables 1 and 2, the model of this invention has an average error rate of 0.52% in predicting the number of confirmed cases of Virus A in a certain province and an average error rate of 0.57% in predicting the number of deaths, indicating that the prediction results of the model proposed in this invention are relatively accurate.

[0099] The model was validated using a virus B from a certain administrative region. The fourth-order Runge-Kutta method and the least squares estimation method were used to fit the model-generated data with the real data. The initial values ​​were set as I = 102, R = 0, and D = 0.

[0100] Figure 6 A graph was fitted to represent the number of confirmed cases of Virus B in a certain administrative region. Figure 7 A fitted image of the number of deaths from Virus B in a certain administrative region was generated. The fitted image shows that the model can accurately fit confirmed cases and deaths, thus predicting the development trend of Virus B. The fitted image indicates that the incubation period of the disease is approximately 2 days, consistent with the rapid outbreak characteristic of this virus. The fitted parameters show that the proportion of asymptomatic infections is relatively low, which is consistent with the actual characteristics of the virus.

[0101] Table 3. Predicted Number of Confirmed Cases of Virus B in a Certain Administrative Region

[0102]

[0103] Table 4. Predicted Number of Deaths from Virus B in a Certain Administrative Region

[0104]

[0105] As can be seen from the prediction error rates in Tables 3 and 4, the model of this invention has an average error of 1.55% in predicting the number of confirmed cases of Virus B in a certain administrative region and an average error of 3.51% in predicting the number of deaths from Virus B in a certain administrative region, indicating that the model's prediction results are relatively accurate.

[0106] The model was validated using the Mexican H1N1 virus. The fourth-order Runge-Kutta method and the least squares estimation method were used to fit the model-generated data with the real data. The initial values ​​were set as I=18, R=0, and D=7.

[0107] Figure 8 The image is fitted to the number of confirmed H1N1 virus cases in Mexico. Figure 9 The fitted image for the number of deaths from the H1N1 virus in Mexico shows that the model's evolution data is largely consistent with the actual situation and closely resembles the development trend of the H1N1 virus. The fitted parameters indicate an incubation period of approximately 4 days, which aligns with relevant literature descriptions of the virus's characteristics. Due to insufficient basic medical facilities in Mexico, hospitals have limited capacity, leading to bed shortages, which is consistent with the low hospitalization rate predicted in the fitted parameters.

[0108] Table 5. Predicted Number of Confirmed Cases of H1N1 Virus in Mexico

[0109]

[0110] As can be seen from the prediction error rate in Table 5, the model of this invention has an average error of 12.3% in predicting the number of confirmed cases of H1N1 virus in Mexico. The prediction trend is consistent, indicating that the model's prediction results are worth referencing.

[0111] In summary, by fitting data from three types of viruses with asymptomatic carriers, Figures 4-9 The results show that the model's predictions agree well with the actual results. Although the error rates vary for different viruses, the overall error rate is low, verifying that the proposed transmission model can be used to describe the transmission process of various diseases, demonstrating strong accuracy and universality. For infectious diseases characterized by asymptomatic carriers, this model can effectively predict the future development trend of the disease, providing a certain reference for disease prevention and control.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A general method for predicting infectious diseases with asymptomatic carriers, characterized by: Comprise the following steps: Obtain known epidemic data of the target infectious disease within a fixed time period; Optimize the basic SEIR model on the warehouse structure and dynamic propagation process to obtain the SEIRAQHCD model for predicting the infectious ability of the target infectious disease; The expression of the SEIRAQHCD model is as follows: （6） （7） （8） （9） （10） （11） （12） （13） （14） （15） Wherein: S is susceptible, E is latent, latent period will evolve into symptomatic and asymptomatic infected, I is diagnosed symptomatic infected, R is cured, A is asymptomatic infected, refers to the population who has been infected with the disease but has not appeared obvious disease symptoms, this kind of population has certain transmission ability, Q is isolated, refers to the suspected cases found by various means, this kind of population is in monitoring state, temporarily does not have the ability to spread, H is inpatient, refers to the patients who have been infected and reached the critical state, need to be treated in hospital, C is suspected close contact, refers to the population related to the trajectory of the infected person, whether infected or not cannot be determined, D is dead, refers to the patients who died of disease; β represents the transmission rate, symptomatic and asymptomatic infected people have different degrees of transmission ability, t inc represents the incubation period, refers to the time experienced from the incubator to the infected person, during which the incubator will evolve into two states, one is a symptomatic infected person, the other is an asymptomatic infected person, According to the least square algorithm, the parameters of the SEIRAQHCD model are estimated and the model is fitted, the fitting parameters and the fitting image are obtained by model verification with known epidemic data, and the prediction of the epidemic data of the target infectious disease is realized. represents the observation period of suspected close contacts, a represents the proportion of asymptomatic infected people among incubators, h represents the proportion of serious illness among infected people, f represents the mortality rate, c represents the number of potential contacts, q represents the isolation rate, represents the cure rate of inpatients, represents the cure rate of non-inpatients, m represents the transmission ability adjustment factor of asymptomatic infected people; The epidemic data includes the cumulative number of confirmed cases and the cumulative number of deaths.

2. A general prediction method of infectious diseases with asymptomatic carriers according to claim 1, characterized in that: The SEIRAQHCD model assumes that the environment of the target infectious disease is a closed environment, ignores the natural birth and death rate of the population and the immigration and emigration of the population, and has the same contact probability between each individual and the same infection probability; 3. A general prediction method of infectious diseases with asymptomatic carriers according to claim 1, characterized in that: Due to the limited medical resources, not all patients can receive hospital treatment, the infected patients with serious illness are divided according to a certain proportion, the transmission ability of symptomatic and asymptomatic infected is different, the appearance of super spreader is not considered, and the infected patients after hospitalization and the infected patients in isolation and observation state do not have infectivity. It also includes using nonlinear Richards regression model to correct the error of epidemic data.

4. The general prediction method of infectious diseases with asymptomatic carriers according to claim 1, characterized in that: Comprise:

5. A general prediction device for infectious diseases with asymptomatic carriers, characterized by: Acquisition module: for obtaining known epidemic data of the target infectious disease within a fixed time period; Optimization module: for optimizing the basic SEIR model on the warehouse structure and dynamic propagation process to obtain the SEIRAQHCD model for predicting the infectious ability of the target infectious disease; The expression of the SEIRAQHCD model is as follows: Wherein: S is susceptible, E is latent, latent period will evolve into symptomatic and asymptomatic infected, I is diagnosed symptomatic infected, R is cured, A is asymptomatic infected, refers to the population who has been infected with the disease but has not appeared obvious disease symptoms, this kind of population has certain transmission ability, Q is isolated, refers to the suspected cases found by various means, this kind of population is in monitoring state, temporarily does not have the ability to spread, H is inpatient, refers to the patients who have been infected and reached the critical state, need to be treated in hospital, C is suspected close contact, refers to the population related to the trajectory of the infected person, whether infected or not cannot be determined, D is dead, refers to the patients who died of disease; （6） （7） （8） （9） （10） （11） （12） （13） （14） （15） According to the least square algorithm, the parameters of the SEIRAQHCD model are estimated and the model is fitted, the fitting parameters and the fitting image are obtained by model verification with known epidemic data, and the prediction of the epidemic data of the target infectious disease is realized. β represents the transmission rate, symptomatic and asymptomatic infected people have different degrees of transmission ability, t inc represents the incubation period, refers to the time experienced from the incubator to the infected person, during which the incubator will evolve into two states, one is the symptomatic infected person, the other is the asymptomatic infected person, ​ represents the observation period of suspected close contacts, a represents the proportion of asymptomatic infected people among incubators, h represents the proportion of serious illness among infected people, f represents the mortality rate, c represents the number of potential contacts, q represents the isolation rate, represents the cure rate of inpatients, represents the cure rate of non-inpatients, m represents the transmission ability adjustment factor of asymptomatic infected people; The fitting prediction module is used for performing parameter estimation and model fitting on the SEIRAQHCD model according to a least square algorithm, and obtaining fitting parameters and a fitting image through model verification on known epidemic data, so as to realize prediction of epidemic data of a target infectious disease.

6. A computer readable storage medium storing a computer program, characterized in that: The computer program, when executed by a processor, implements the method of any one of claims 1-4.

7. An electronic device, comprising: Comprising: one or more processors; a memory storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-4.

Images