Infectious disease transmission scale simulation method, device and electronic equipment

Abstract

The present disclosure provides an infectious disease transmission scale simulation method, device and electronic equipment. The infectious disease transmission scale simulation method comprises: obtaining an aggregation activity effective reproductive number and a community transmission effective reproductive number of a target infectious disease according to preset data; determining the number of participants in transmission on the day when a target aggregation activity ends according to the aggregation activity effective reproductive number and the number of infected persons participating in the target aggregation activity; and determining the daily number of infectious persons after the target aggregation activity ends according to the number of participants in transmission on the day when the target aggregation activity ends, the community transmission effective reproductive number, a preset control parameter and the number of community transmission days. The embodiment of the present disclosure can accurately simulate and calculate the change of the number of infectious persons after the aggregation activity is held.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and more specifically, to a method, apparatus, and electronic device for simulating the scale of infectious disease transmission. Background Technology

[0002] Outbreaks of infectious diseases have a huge impact on social activities. How to maintain the normal operation of society as much as possible while controlling the development of the epidemic is an urgent problem to be solved in this field.

[0003] In related technologies, the SEIR model (Mathematical Models of Epidemic Diseases) is typically used at the regional level to analyze regional epidemic development data, such as changes in the number of susceptible, exposed, infected, and recovered individuals after an outbreak. It estimates the effective reproduction number R0 (the average number of people an infected person can infect during their illness cycle in an environment entirely composed of susceptible individuals, without intervention) for other gatherings based on certain already occurred events. However, this method considers limited factors and struggles to provide accurate predictions when forecasting the impact of unexpected social gatherings on epidemic development. Furthermore, this method can only offer suggestions on whether to allow activities, and cannot draw different conclusions based on different restrictions on activity openings.

[0004] Therefore, there is a need for a method that can accurately predict the impact of potential social gatherings on changes in the number of infections and provide recommendations for opening restrictions on such activities.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this disclosure is to provide a method, apparatus, and electronic device for simulating the scale of infectious disease transmission, which at least to some extent overcomes the problem of being unable to accurately predict the impact of non-occurring social gatherings on changes in the number of people involved in the spread of a target infectious disease due to limitations and defects in related technologies.

[0007] According to a first aspect of the present disclosure, a method for simulating the scale of infectious disease transmission is provided, comprising: obtaining, based on preset data, the effective reproduction number of a cluster activity and the effective reproduction number of community transmission of a target infectious disease; determining, based on the effective reproduction number of the cluster activity and the number of infected persons participating in the target cluster activity, the number of people involved in transmission on the day the target cluster activity ends; and determining, based on the number of people involved in transmission on the day the target cluster activity ends, the effective reproduction number of community transmission, preset control parameters, and the number of days of community transmission, the number of people involved in transmission each day after the target cluster activity ends.

[0008] In one exemplary embodiment of this disclosure, the preset data includes the number of people infected with the target infectious disease corresponding to multiple gatherings, the total number of close contacts of infected individuals, and the total number of confirmed cases after the gatherings. Obtaining the effective reproduction number of the target infectious disease in the gatherings and the effective reproduction number in the community transmission includes determining the effective reproduction number of the gatherings and the effective reproduction number in the community transmission according to the following formulas: Where L is the effective transmission distance of the target infectious disease, d is the recommended social distance, and N is the distance between the two points. ini N is the number of people infected with the target infectious disease who participated in the aforementioned multiple gatherings. c N is the total number of close contacts of the infected individuals corresponding to the aforementioned multiple gatherings. m R1 is the total number of confirmed cases after the gatherings, R2 is the effective reproduction number of the gatherings, and R2 is the effective reproduction number of community transmission.

[0009] In one exemplary embodiment of this disclosure, determining the number of participants in the transmission on the day the target gathering activity ends, based on the effective reproduction number of the gathering activity and the number of infected people who participated in the target gathering activity, includes determining the number of participants in the transmission on the day the target gathering activity ends according to the following formula: Wherein, N1 is the number of people involved in the transmission on the day the target gathering activity ends, N0 is the number of people infected who participated in the target gathering activity, T is the average time from infection to diagnosis of the target infectious disease, and R1 is the effective reproduction number of the gathering activity.

[0010] In one exemplary embodiment of this disclosure, determining the number of participants in the spread of the virus each day after the end of the target gathering activity based on the number of participants on the day the target gathering activity ended, the effective recurrence count of community transmission, preset control parameters, and the number of days of community transmission includes: determining the number of new infections on the nth day after the end of the target gathering activity according to a preset formula: Where, N new N is the number of new infections on the nth day after the conclusion of the target gathering activity. n-1The following parameters are used: N1 is the number of participants in the transmission on the (n-1)th day after the end of the target gathering activity; T is the average time from infection to diagnosis for the target infectious disease; R2 is the effective reproduction number in community transmission; and n is an integer greater than 1. The detection probability of newly infected individuals is determined by detection methods based on preset control parameters, including the isolation time after the first gathering activity and the first detection ratio. The number of newly involved in the transmission on the nth day after the end of the target gathering activity is determined by multiplying the detection probability by the number of newly infected individuals. The number of participants in the transmission corresponding to the nth day after the end of the target gathering activity is determined by combining the number of newly involved in the transmission with the number of participants in the transmission on the (n-1)th day after the end of the target gathering activity.

[0011] In an exemplary embodiment of this disclosure, determining the detection probability of finding new infected persons through detection means based on the preset control parameters includes: obtaining the average incubation period and standard deviation of the incubation period of the target infectious disease based on statistical data of the incubation period days of the target infectious disease; determining a normal distribution formula based on the average incubation period and the standard deviation of the incubation period days; determining the probability of illness of the infected person during the isolation period based on the normal distribution formula and the isolation time after the first gathering activity; and determining the detection probability based on the product of the probability of illness, the first detection ratio, and the detection accuracy.

[0012] In one exemplary embodiment of this disclosure, the method further includes: obtaining the gathering activity period of the target gathering activity and determining the number of community dissemination days based on the gathering activity period; determining the number of participants in the dissemination after the end of one activity period based on the number of community dissemination days; obtaining the number of gathering activities of the target gathering activity and determining the number of participants in the dissemination after the end of multiple activity periods based on the number of gathering activities.

[0013] In one exemplary embodiment of this disclosure, the method further includes: determining a target control parameter based on the number of target community transmission days corresponding to the target number of participants in the transmission after the target gathering activity ends, wherein the target control parameter includes a second post-gathering activity isolation time and a second detection ratio.

[0014] According to a second aspect of the present disclosure, an infectious disease transmission scale simulation device is provided, comprising: an infectious disease parameter determination module configured to acquire, based on preset data, the effective reproduction number of a cluster activity and the effective reproduction number of community transmission of a target infectious disease; an infection number determination module configured to determine, based on the effective reproduction number of the cluster activity and the number of infected persons who participated in the target cluster activity, the number of people involved in transmission on the day the target cluster activity ends; and a transmission number determination module configured to determine, based on the number of people involved in transmission on the day the target cluster activity ends, the effective reproduction number of community transmission, preset control parameters, and the number of days of community transmission, the number of people involved in transmission each day after the end of the target cluster activity.

[0015] According to a third aspect of this disclosure, an electronic device is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to perform the method as described in any of the preceding methods based on instructions stored in the memory.

[0016] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a program stored thereon that, when executed by a processor, implements the infectious disease spread scale simulation method as described in any of the preceding claims.

[0017] This disclosed embodiment can calculate the daily change in the number of participants in the spread of the virus after a gathering is held based on preset data, the number of infected people participating in the target gathering, preset control parameters, and the number of days of community transmission. It can predict the impact of the gathering on the spread of the epidemic under various parameters such as the number of days of home isolation and the scope of post-meeting testing, and output accurate predicted values, thereby providing reasonable suggestions for holding gatherings.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0020] Figure 1 This is a flowchart of a method for simulating the scale of infectious disease transmission in one embodiment of this disclosure.

[0021] Figure 2 This is a schematic diagram illustrating the method for calculating the average number of close contacts of an infected person in this embodiment of the present disclosure.

[0022] Figure 3This is a sub-flowchart of the infectious disease transmission scale simulation method in the embodiments of this disclosure.

[0023] Figure 4 This is a schematic diagram illustrating how the probability of an infected person developing the disease during the isolation period is determined based on the isolation time following a gathering activity, according to an embodiment of this disclosure.

[0024] Figure 5 This is a flowchart of a method for simulating the scale of infectious disease transmission in another embodiment of this disclosure.

[0025] Figure 6 This is a block diagram of an infectious disease transmission scale simulation device in an embodiment of this disclosure.

[0026] Figure 7 This is a block diagram of an electronic device according to an embodiment of this disclosure. Detailed Implementation

[0027] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0028] Furthermore, the accompanying drawings are merely illustrative of this disclosure, and the same reference numerals in the drawings denote the same or similar parts, thus repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0029] The exemplary embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0030] Figure 1 A flowchart illustrating an exemplary embodiment of the present disclosure of a method for simulating the scale of infectious disease transmission is shown. (Reference) Figure 1 The method 100 for simulating the scale of infectious disease transmission may include:

[0031] Step S102: Obtain the effective reproduction number of clustered activities and the effective reproduction number of community transmission of the target infectious disease based on preset data;

[0032] Step S104: Determine the number of participants in the transmission on the day the target gathering activity ends, based on the effective reproduction number of the gathering activity and the number of infected people who participated in the target gathering activity.

[0033] Step S106: Determine the number of participants in the dissemination each day after the target gathering activity ends, based on the number of participants on the day the target gathering activity ends, the number of effective regenerations in the community dissemination, the preset control parameters, and the number of days of community dissemination.

[0034] This disclosed embodiment can calculate the daily change in the number of participants in the spread of the virus after a gathering is held based on preset data, the number of infected people participating in the target gathering, preset control parameters, and the number of days of community transmission. It can predict the impact of the gathering on the spread of the epidemic under various parameters such as the number of days of home isolation and the scope of post-meeting testing, and output accurate predicted values, thereby providing reasonable suggestions for holding gatherings.

[0035] The following is a detailed explanation of each step in the method 100 for simulating the scale of infectious disease transmission.

[0036] In step S102, the effective reproduction number of clustered activities and the effective reproduction number of community transmission of the target infectious disease are obtained based on preset data.

[0037] In this embodiment of the disclosure, the preset data may include the number of people infected with the target infectious disease corresponding to multiple gathering activities obtained from previous statistical data, the total number of close contacts of the infected persons, and the total number of confirmed cases after the gathering activities.

[0038] In one embodiment, the effective reproduction number R1 of clustered activities and the effective reproduction number R2 of community transmission can be determined based on the preliminary statistical data corresponding to the target infectious disease and the following formula:

[0039]

[0040] Where L is the effective transmission distance of the target infectious disease, which is professional data derived from epidemiological studies; d is the recommended social distance; and N... ini This refers to the number of infected individuals who participated in these multiple gatherings, N. c N is the total number of close contacts of the infected individuals associated with these multiple gatherings. m R1 represents the total number of confirmed cases following the multiple gatherings, R2 represents the effective reproduction number of the gatherings, and R1 represents the effective reproduction number of community transmission. The effective reproduction number of the gatherings, R1, represents the number of new infections on the day the event was held, and the effective reproduction number of community transmission, R2, represents the number of new infections in the community after the event ended.

[0041] The calculation principle of formula (1) is explained below.

[0042] The formula for calculating the effective reproduction number is known to be:

[0043] R = k × b × T (2)

[0044] Where k is the average number of close contacts of an infected person, b is the transmission rate of the target infectious disease, and T is the transmission time.

[0045] According to formula (2), the effective regeneration number R1 of the aggregation activity is:

[0046] R1=k1×b×T (3)

[0047] The effective reproduction number R2 in community transmission is:

[0048] R2=k2×b×T (4)

[0049] Then we have:

[0050]

[0051] Therefore, we can conclude that:

[0052]

[0053] refer to Figure 2 Assuming the effective transmission distance of the target infectious disease is L, then in a square with side length L, the average number of close contacts k1 of people participating in a gathering activity is (L / d+1). 2 -1. When assuming the effective transmission area of ​​the target infectious disease is circular or other shapes, it can be based on... Figure 2 The calculation principle shown determines the value of k1, but this disclosure is not limited thereto. It is understood that for a target infectious disease, a unique effective transmission distance and a unique recommended social distance can be set.

[0054] The average number of close contacts of infected individuals after a cluster activity, k2, and the effective reproduction number in the community, R2, are statistical values ​​determined based on previous statistical data. That is, the calculation of k2 and R2 is based on the average value derived from the statistical data of the target infectious disease.

[0055] Using the above formulas, based on the determined k2, R2, and k1, we can obtain the effective reproduction number R1 of the cluster activity and the effective reproduction number R2 of the community transmission corresponding to the target infectious disease.

[0056] Step S104: Determine the number of participants in the transmission on the day the target gathering activity ends, based on the effective reproduction number of the gathering activity and the number of infected people who participated in the target gathering activity.

[0057] In one embodiment of this disclosure, the number of participants N1 on the last day of the target gathering activity can be determined according to the following formula:

[0058]

[0059] Where N0 is the number of infected people who participated in the target gathering activity, T is the average time from infection to diagnosis of the target infectious disease, and R1 is the effective reproduction number of the gathering activity.

[0060] In this embodiment, the number of people involved in the spread refers to the number of infected individuals who have been infected with the target infectious disease but have not yet been identified. Because these infected individuals are undetected and cannot be isolated, they will participate in the spread of the target infectious disease, causing the spread of the infectious disease to expand. In this embodiment, the number of people involved in the spread each day is referred to as the spread scale of the target infectious disease on that day. In formula (7), since the number of infected individuals participating in the target gathering cannot be accurately known (if it is known that the person is infected, then the person will not be invited to the meeting), and the method of this embodiment can be used to estimate the impact of non-occurring gatherings on the spread of the epidemic, N0 can be the hypothetical number of infected individuals corresponding to non-occurring gatherings.

[0061] Step S106: Determine the number of participants in the dissemination each day after the target gathering activity ends, based on the number of participants on the day the target gathering activity ends, the number of effective regenerations in the community dissemination, the preset control parameters, and the number of days of community dissemination.

[0062] Figure 3 This is a sub-step diagram of step S106 in one embodiment of this disclosure.

[0063] refer to Figure 3 Step S106 may include:

[0064] Step S1061: Determine the number of new infections on day n after the end of the target gathering activity according to a preset formula;

[0065] Step S1062: Determine the detection probability of newly infected persons by detection means according to the preset control parameters. The preset control parameters include the isolation time after the first gathering activity and the first detection ratio.

[0066] Step S1063: Determine the number of new participants in the spread on the nth day after the end of the target gathering activity based on the product of the detection probability and the number of newly infected individuals;

[0067] Step S1064: Determine the number of participants on the nth day after the end of the target gathering activity based on the number of newly added participants and the number of participants on the (n-1)th day after the end of the target gathering activity.

[0068] In step S1061, the number of new infections N on day n is determined according to the following formula. new :

[0069]

[0070] Where, N n-1 N1 is the number of participants in the spread of the target gathering activity on the (n-1)th day after its conclusion, T is the average time from infection to diagnosis of the target infectious disease, R2 is the effective reproduction number of the community transmission, and n is an integer greater than 1.

[0071] In step S1062, the preset control parameters may include, for example, the isolation time t after the first aggregation activity and the first detection ratio β.

[0072] The mean incubation period and standard deviation of the target infectious disease can be obtained from statistical data on the incubation period days. Then, a normal distribution formula is determined based on the mean incubation period and standard deviation. Next, the probability of infection (p) during the isolation period is determined using the normal distribution formula and the isolation time (t) after the first cluster activity. Finally, the detection probability is determined by multiplying the probability of infection (p), the initial detection rate (β), and the detection accuracy (x). In this step, the isolation time (t) after the first cluster activity and the initial detection rate (β) are assumed values.

[0073] Figure 4 This disclosure includes a schematic diagram illustrating the determination of the probability of an infected person developing the disease during the isolation period based on the isolation time following a gathering activity, as shown in one embodiment.

[0074] refer to Figure 4 The isolation time *t* after a gathering determines whether a potential infection is exposed. Here, it is assumed that the incubation period follows a normal distribution. Based on current epidemic data, the average incubation period for the target infectious disease is *μ* days, with a standard deviation of σ. Using the standard normal distribution table, combined with the average incubation period *μ* days and the isolation time *t* after the first gathering, the probability value of φ (x <= t) is calculated, which is the probability *p* of an infected person developing the disease during the isolation period. Since calculating the relationship between *t* and *p* using a normal distribution is a commonly used mathematical formula, it will not be elaborated upon here.

[0075] By transforming the impact of the quarantine period on the number of people involved in the spread into a normal distribution probability problem of the quarantine period relative to the disease transmission cycle, the accuracy of the output prediction results can be improved.

[0076] Because the number of days of home isolation is factored in, the number of newly confirmed cases (i.e., newly infected individuals detected through testing) will be positively correlated with the probability p of these infected individuals developing symptoms during their isolation period. Furthermore, the number of newly confirmed cases is also correlated with the testing rate β among close contacts. Based on current statistical data, the accuracy of testing is estimated as x. Combining this with the existing number of new infections and the testing rate β, the number of newly confirmed cases that can be detected can be obtained:

[0077]

[0078] Where, N out This represents the number of infected individuals diagnosed on day n. These individuals, having been diagnosed, will be isolated and unable to participate in further transmission of the epidemic. Therefore, the number of newly infected individuals participating in transmission on day n can be obtained by subtracting these exposed individuals from the current number of new infections. n :

[0079]

[0080] Next, we will calculate the number of new participants in the dissemination on day n. n The number of participants N on day n-1 n-1 By adding them together, we can obtain the number of participants N on day n. n :

[0081] N n =NEW n +N n-1 (11)

[0082] The number of participants in the transmission on day n can be referred to as the scale of the transmission of the target infectious disease on day n after the end of the target gathering activity. It is understandable that, since the infectivity of an infectious disease lasts for a limited time (assumed to be Tc days), when maintaining the total number of infectious infections each time, only the data from the most recent Tc days can be selected for calculation, because those infected before Tc days are no longer infectious. That is, N calculated in formula (9) n Based on this, the number of new participants in the dissemination for each day prior to Tc is deleted.

[0083] Figure 5 This is a flowchart of a method for simulating the scale of infectious disease transmission in another embodiment of this disclosure.

[0084] refer to Figure 5 In one embodiment, the number of participants in the dissemination can be further processed as follows:

[0085] Step S51: Obtain the gathering activity cycle of the target gathering activity and determine the number of community transmission days based on the gathering activity cycle;

[0086] Step S52: Determine the number of participants in the dissemination at the end of an activity cycle based on the number of community dissemination days;

[0087] Step S53: Obtain the number of gathering activities of the target gathering activity and determine the number of participants in the dissemination after the end of multiple activity cycles based on the number of gathering activities.

[0088] By calculating the frequency of targeted gatherings over time, the number of participants in the spread of the virus can be determined. For example, if the targeted gathering is held once a week, the number of participants on the last day of the gathering can be calculated on the first day of a cycle. Then, for the next six days, the number of participants can be calculated based on the previous day's data, until the seventh day, at which point the number of participants on that day is taken as the total number of infections at the end of the cycle. When projecting events over multiple cycles, the estimated number of infections at the end of the previous cycle can be used as the number of infected participants (N0) in the next cycle. This allows for the simulation of the impact of different event frequencies on the spread of the epidemic.

[0089] In one embodiment of this disclosure, a target output value can be set based on the model established above, and a suitable input value can be derived in reverse. That is, the target control parameters are determined based on the number of target participants in the dissemination corresponding to the number of days of dissemination in the target community after the end of the target gathering activity.

[0090] Based on the above methods, different models can be set up to illustrate the changes in the number of participants in the target gathering over time, considering factors such as the number of infected individuals attending the gathering, recommended social distancing, testing rate, post-gathering quarantine time, and activity frequency. Furthermore, these models can be used to identify combinations of conditions where the number of participants is lower than the preset number of participants for a given number of days of community transmission, serving as activity recommendations. For example, assuming a potential infection rate of γ among participants (i.e., the proportion of susceptible individuals attending the target gathering), setting the number of invited participants to M, recommended social distancing to d, testing rate to β, post-gathering quarantine time to t, activity frequency to 1 time, and target community transmission days to n, then the model can be established based on the above analysis as follows:

[0091] N n =f(Mγ,β,t,d,n) (12)

[0092] N n Let f be the number of participants in the transmission on day n after the end of the target activity, i.e., the number of infected persons who have not been detected and isolated. f is the input parameters established according to formulas (1) to (11) and N. n The relationship, i.e., the mathematical model, can be derived from the above formulas, and will not be elaborated further here. Based on the above analysis, it can be seen that N... nThe value is affected by the input parameters M, γ, β, t, d, and n.

[0093] Suppose that for a target infectious disease, a unique recommended social distance d is set, and that on day 5 (n=5), N n No more than 200 people (N) n =200), then d, n, N n Substituting three fixed values ​​into formula (12) yields multiple sets of [Mγ, β, t]. When the number of invited participants M is fixed and the estimated potential infection rate γ is fixed, one or more sets of [β, t] values ​​can be obtained, providing suggestions for the post-gathering isolation time and testing ratio. When the number of invited participants M is not fixed, one or more sets of [M, β, t] values ​​can be obtained, providing suggestions for the number of invited participants, the post-gathering isolation time, and the testing ratio.

[0094] The suggested value obtained at this time can be called the target control parameter, β in the suggested value can be called the second detection ratio, and t in the suggested value can be called the second post-aggregation activity isolation time.

[0095] Activities organized through such recommendations will maintain a low risk of COVID-19 transmission, thereby enabling society to function normally as much as possible while controlling the pandemic.

[0096] In summary, the embodiments disclosed herein can simulate the development trend of the epidemic under partial opening of economic or social activities, which helps to balance the controllability of the economy and the epidemic. For gathering activities with minimal impact, control can be lifted to restore the economy.

[0097] This disclosure, through its detailed provision of policy parameters targeting gatherings, can demonstrate the impact of each gathering restriction on the development of the epidemic. By separately considering the impact of community + household transmission and transmission from gatherings, it is possible to simulate the increase in the number of new cases caused by periodic gatherings.

[0098] For necessary gatherings, the following can be specifically targeted: the frequency of gatherings, the number of initial infections, the proportion of close contacts of confirmed cases who undergo nucleic acid testing, the proportion of people participating in gatherings, and the duration of home isolation after participating in gatherings. The predicted trend of the epidemic should be observed, and policy parameters for gatherings under the condition of ensuring epidemic control should be selected as policy recommendations for carrying out necessary activities, thus ensuring both people's livelihoods and epidemic control.

[0099] Corresponding to the above method embodiments, this disclosure also provides an infectious disease transmission scale simulation device, which can be used to execute the above method embodiments.

[0100] Figure 6 The diagram schematically illustrates a block diagram of an infectious disease spread scale simulation device in an exemplary embodiment of the present disclosure.

[0101] refer to Figure 6 The infectious disease transmission scale simulation device 600 may include:

[0102] The infection parameter determination module 602 is configured to obtain the effective reproduction number of clustered activities and the effective reproduction number of community transmission of the target infectious disease based on preset data;

[0103] The infection number determination module 604 is configured to determine the number of participants in the transmission on the day the target gathering activity ends, based on the effective reproduction number of the gathering activity and the number of infected participants in the target gathering activity.

[0104] The dissemination number determination module 606 is configured to determine the daily dissemination number after the target gathering activity ends based on the number of participants on the day the target gathering activity ends, the effective regeneration number of the community dissemination, preset control parameters, and the number of community dissemination days.

[0105] In one exemplary embodiment of this disclosure, the infection parameter determination module 602 is configured to include determining the effective reproduction number of the gathering activity and the effective reproduction number of community transmission according to the following formula: Where L is the effective transmission distance of the target infectious disease, d is the recommended social distance, and N is the distance between the two points. ini N is the number of people infected with the target infectious disease who participated in the aforementioned multiple gatherings. c N is the total number of close contacts of the infected individuals corresponding to the aforementioned multiple gatherings. m R1 is the total number of confirmed cases after the gatherings, R2 is the effective reproduction number of the gatherings, and R2 is the effective reproduction number of community transmission.

[0106] In one exemplary embodiment of this disclosure, the infection number determination module 604 is configured to determine the number of participants in the transmission on the day the target gathering activity ends, according to the following formula: Wherein, N1 is the number of people involved in the transmission on the day the target gathering activity ends, N0 is the number of people infected who participated in the target gathering activity, T is the average time from infection to diagnosis of the target infectious disease, and R1 is the effective reproduction number of the gathering activity.

[0107] In one exemplary embodiment of this disclosure, the transmission number determination module 606 is configured to: determine the number of newly infected individuals on the nth day after the end of the target gathering activity according to a preset formula: Where, N new N is the number of new infections on the nth day after the conclusion of the target gathering activity. n-1The following parameters are used: N1 is the number of participants in the transmission on the (n-1)th day after the end of the target gathering activity; T is the average time from infection to diagnosis for the target infectious disease; R2 is the effective reproduction number in community transmission; and n is an integer greater than 1. The detection probability of newly infected individuals is determined by detection methods based on preset control parameters, including the isolation time after the first gathering activity and the first detection ratio. The number of newly involved in the transmission on the nth day after the end of the target gathering activity is determined by multiplying the detection probability by the number of newly infected individuals. The number of participants in the transmission on the nth day after the end of the target gathering activity is determined by combining the number of newly involved in the transmission with the number of participants on the (n-1)th day after the end of the target gathering activity.

[0108] In an exemplary embodiment of this disclosure, the transmission number determination module 606 is configured to: obtain the average incubation period and the standard deviation of the incubation period of the target infectious disease based on statistical data on the incubation period days of the target infectious disease; determine a normal distribution formula based on the average incubation period and the standard deviation of the incubation period days; determine the probability of infection of an infected person during the isolation period based on the normal distribution formula and the isolation time after the first gathering activity; and determine the detection probability based on the product of the probability of infection, the first detection ratio, and the detection accuracy.

[0109] In one exemplary embodiment of this disclosure, a periodic activity simulation module is further included, configured to: obtain the gathering activity period of the target gathering activity and determine the number of community dissemination days based on the gathering activity period; determine the number of participants in the dissemination after the end of one activity period based on the number of community dissemination days; obtain the number of gathering activities of the target gathering activity and determine the number of participants in the dissemination after the end of multiple activity periods based on the number of gathering activities.

[0110] In one exemplary embodiment of this disclosure, an activity parameter determination module is further included, configured to: determine target control parameters based on the target number of participants in the spread corresponding to the target community spread days after the end of the target gathering activity, wherein the target control parameters include a second post-gathering activity isolation time and a second detection ratio.

[0111] Since the functions of the device 600 have been described in detail in their respective method embodiments, they will not be repeated here.

[0112] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0113] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0114] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely hardware implementations, entirely software implementations (including firmware, microcode, etc.), or implementations combining hardware and software aspects, collectively referred to herein as “circuits,” “modules,” or “systems.”

[0115] The following reference Figure 7 To describe an electronic device 700 according to this embodiment of the present invention. Figure 7 The electronic device 700 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0116] like Figure 7 As shown, the electronic device 700 is manifested in the form of a general-purpose computing device. The components of the electronic device 700 may include, but are not limited to: at least one processing unit 710, at least one storage unit 720, and a bus 730 connecting different system components (including storage unit 720 and processing unit 710).

[0117] The storage unit stores program code that can be executed by the processing unit 710, causing the processing unit 710 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 710 can perform actions such as... Figure 1 The steps are shown in the figure.

[0118] Storage unit 720 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 7201 and / or cache memory 7202, and may further include a read-only memory (ROM) 7203.

[0119] The storage unit 720 may also include a program / utility 7204 having a set (at least one) program module 7205, such program module 7205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0120] Bus 730 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0121] Electronic device 700 can also communicate with one or more external devices 800 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 700, and / or with any device that enables electronic device 700 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 750. Furthermore, electronic device 700 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 760. As shown, network adapter 760 communicates with other modules of electronic device 700 via bus 730. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0122] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0123] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0124] The program product for implementing the above-described method according to embodiments of the present invention may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0125] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0126] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0127] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0128] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0129] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0130] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and concept of this disclosure are indicated by the claims.

Claims

1. A method for predicting the number of people affected by an infectious disease, characterized in that, include: The processor obtains the effective reproduction number of cluster activities and the effective reproduction number of community transmission of the target infectious disease based on preset data. The preset data includes the number of people infected with the target infectious disease corresponding to multiple cluster activities, the total number of close contacts of the infected persons, and the total number of confirmed cases after the cluster activities. The processor determines the number of participants in the transmission on the day the target gathering activity ends, based on the effective reproduction number of the gathering activity and the number of infected people who participated in the target gathering activity. The processor determines the number of participants in the dissemination each day after the target gathering activity ends based on the number of participants on the day the target gathering activity ends, the number of effective regenerations in the community dissemination, preset control parameters, and the number of days of community dissemination. The number of community dissemination days is determined based on the target gathering activity cycle. The number of participants in the dissemination corresponding to the number of community dissemination days after the target gathering activity ends is determined based on the number of participants in the dissemination each day after the target gathering activity ends. This is the number of participants in the dissemination after the end of one activity cycle. The number of participants in the dissemination after the end of multiple activity cycles is determined based on the number of target gathering activities. The step of determining the number of participants in the dissemination each day after the target gathering activity ends, based on the number of participants on the day the target gathering activity ends, the number of effective regenerations in community dissemination, preset control parameters, and the number of days of community dissemination, includes: The number of new infections on day n after the end of the target gathering activity is determined according to a preset formula, and the detection probability of new infections is determined by detection methods according to the preset control parameters, which include the isolation time after the first gathering activity and the first detection ratio. The number of new participants in the spread on day n after the end of the target gathering activity is determined by multiplying the detection probability by the number of new infections. The number of participants on day n after the end of the target gathering activity is determined by comparing the number of new participants on day n after the end of the target gathering activity with the number of participants on day n-1 after the end of the target gathering activity. The preset formula includes: ， in, This refers to the number of new infections on the nth day after the conclusion of the target gathering activity. It is the number of participants in the dissemination on the (n-1)th day after the end of the target gathering activity. The number of participants in the transmission on the day the target gathering activity ended, T is the average time from infection to diagnosis of the target infectious disease, R2 is the effective reproduction number of community transmission, and n is an integer greater than 1; obtaining the effective reproduction number of the gathering activity and the effective reproduction number of community transmission of the target infectious disease includes determining the effective reproduction number of the gathering activity and the effective reproduction number of community transmission according to the following formulas: ， Where L is the effective transmission distance of the target infectious disease, and d is the recommended social distance. It refers to the number of people infected with the target infectious disease who participated in the aforementioned multiple gatherings. It is the total number of close contacts of the infected persons corresponding to the aforementioned multiple gatherings. R1 is the total number of confirmed cases after the gatherings, R2 is the effective reproduction number of the gatherings, and R2 is the effective reproduction number of community transmission.

2. The method for estimating the number of people affected by an infectious disease as described in claim 1, characterized in that, The determination of the number of participants in the transmission on the last day of the target gathering activity, based on the effective reproduction number of the gathering activity and the number of infected people who participated in the target gathering activity, includes determining the number of participants in the transmission on the last day of the target gathering activity according to the following formula: ， in, It is the number of participants in the dissemination on the day the target gathering activity ended. R1 is the number of infected individuals who participated in the target gathering activity, T is the average time from infection to diagnosis of the target infectious disease, and R1 is the effective reproduction number of the gathering activity.

3. The method for estimating the number of people affected by an infectious disease as described in claim 1, characterized in that, The step of determining the detection probability of newly infected individuals through detection methods based on the preset control parameters includes: The average incubation period and standard deviation of the incubation period of the target infectious disease are obtained based on the statistical data of the incubation period days of the target infectious disease. The normal distribution formula is determined based on the average incubation period and the standard deviation of the incubation period in days. The probability of an infected person developing the disease during the isolation period is determined based on the normal distribution formula and the isolation time after the first gathering activity; The detection probability is determined by multiplying the incidence rate, the first detection ratio, and the detection accuracy.

4. A device for predicting the number of people affected by an infectious disease, characterized in that, include: The transmission parameter determination module is configured to allow the processor to obtain the effective reproduction number of cluster activities and the effective reproduction number of community transmission of the target infectious disease based on preset data. The preset data includes the number of people infected with the target infectious disease corresponding to multiple cluster activities, the total number of close contacts of the infected persons, and the total number of confirmed cases after the cluster activities. The infection number determination module is configured to determine the number of participants in the transmission on the day the target gathering activity ends, based on the effective reproduction number of the gathering activity and the number of infected participants in the target gathering activity. The module for determining the number of participants is configured so that the processor determines the number of participants on each day after the target gathering activity ends, based on the number of participants on the day the target gathering activity ends, the number of effective regenerations in the community, preset control parameters, and the number of days of community dissemination. The periodic activity simulation module is set to determine the number of community dissemination days based on the target gathering activity cycle, and to determine the number of participants in the community dissemination for the number of community dissemination days after the target gathering activity ends based on the number of participants in the dissemination each day after the target gathering activity ends, which is the number of participants in the dissemination after the end of one activity cycle. The number of participants in the dissemination after the end of multiple activity cycles is determined based on the number of target gathering activities. The module for determining the number of people to be disseminated is configured as follows: The number of new infections on day n after the end of the target gathering activity is determined according to a preset formula, and the detection probability of new infections is determined by detection methods according to the preset control parameters, which include the isolation time after the first gathering activity and the first detection ratio. The number of new participants in the spread on day n after the end of the target gathering activity is determined by multiplying the detection probability by the number of new infections. The number of participants on day n after the end of the target gathering activity is determined by comparing the number of new participants on day n after the end of the target gathering activity with the number of participants on day n-1 after the end of the target gathering activity. The preset formula includes: ， in, This refers to the number of new infections on the nth day after the conclusion of the target gathering activity. It is the number of participants in the dissemination on the (n-1)th day after the end of the target gathering activity. R2 is the number of participants in the transmission on the day the target gathering activity ends, T is the average time from infection to diagnosis of the target infectious disease, R2 is the effective reproduction number of community transmission, and n is an integer greater than 1. The acquisition of the effective reproduction number of clustered activities and the effective reproduction number of community transmission of the target infectious disease includes determining the effective reproduction number of clustered activities and the effective reproduction number of community transmission according to the following formulas: ， Wherein, L is the effective transmission distance of the target infectious disease, d is the recommended social distance, is the number of people infected with the target infectious disease who participated in the multiple gatherings, is the total number of close contacts of the infected persons corresponding to the multiple gatherings, is the total number of confirmed cases after the gatherings corresponding to the multiple gatherings, R1 is the effective reproduction number of the gatherings, and R2 is the effective reproduction number of community transmission.

5. An electronic device, characterized in that, include: Memory; as well as A processor coupled to the memory, the processor being configured to execute the infectious disease transmission number estimation method as described in any one of claims 1-3 based on instructions stored in the memory.

6. A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the method for estimating the number of people affected by an infectious disease as described in any one of claims 1-3.

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