CN112700884B - Epidemic situation prevention and control effectiveness determining method and device, electronic equipment and medium - Google Patents
Epidemic situation prevention and control effectiveness determining method and device, electronic equipment and medium Download PDFInfo
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Abstract
The embodiment of the disclosure provides an epidemic situation prevention and control effectiveness determining method, an epidemic situation prevention and control effectiveness determining device, electronic equipment and a computer readable medium; relates to the technical field of data processing. The epidemic situation prevention and control effectiveness determining method comprises the following steps: acquiring contactor data of a diagnosed patient, and calculating the proportion of related people in a first-level close contact of the diagnosed patient and related people of the diagnosed patient according to the contactor data; calculating the prevention and control effectiveness of the diagnosed patient and the first-level close connector through epidemic situation statistical data, and calculating the effectiveness of each prevention and control measure by combining the proportion of related people and the prevention and control effectiveness; and predicting the effectiveness of the control of the second-level packer of the diagnosed patient by the effectiveness of each control measure. The technical scheme of the embodiment of the disclosure can provide data support for epidemic prevention and control, and is beneficial to accurate control of epidemic.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to an epidemic prevention and control effectiveness determining method, an epidemic prevention and control effectiveness determining device, an electronic apparatus, and a computer readable medium.
Background
Infectious disease is a disease that can spread from person to person or from person to animal and is widely prevalent, for example, influenza, mumps, new coronavirus pneumonia, and the like. Because the pathogens of each infectious disease are different, the transmission routes are different, so that the diseases can be rapidly transmitted and spread once happening, and the diseases are greatly destroyed.
After epidemic situation happens, the country can quickly take various prevention and control measures to inhibit the development of the epidemic situation, but different prevention and control measures can have different effects, so that the quantification of the effectiveness of the prevention and control measures is important for the control of the epidemic situation.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The embodiment of the disclosure aims to provide an epidemic situation prevention and control effectiveness determining method, an epidemic situation prevention and control effectiveness determining device, electronic equipment and a computer readable medium, which can determine the effectiveness of each prevention and control measure according to statistical data in the epidemic situation development process, and further provide data support for epidemic situation prevention and control decision.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to a first aspect of embodiments of the present disclosure, there is provided a method for determining effectiveness of epidemic prevention and control, including: acquiring contactor data of a diagnosed patient, and calculating the proportion of related people in a first-level close contact of the diagnosed patient and related people of the diagnosed patient according to the contactor data; calculating the prevention and control effectiveness of the diagnosed patient and the first-level close connector through epidemic situation statistical data, and calculating the effectiveness of each prevention and control measure by combining the proportion of related people and the prevention and control effectiveness; and predicting the effectiveness of the control of the second-level packer of the diagnosed patient by the effectiveness of each control measure.
In an exemplary embodiment of the present disclosure, the prevention and control measures include at least centralized isolation, at home isolation.
In an exemplary embodiment of the disclosure, the calculating the effectiveness of each prevention and control measure in combination with the proportion of the related population and the prevention and control effectiveness includes: determining occurrence probabilities of the centralized isolation and the home isolation in the first-level close-connected person respectively; based on the occurrence probability, the relatives population proportion and the prevention and control effectiveness, which are respectively corresponding to the centralized isolation and the household isolation, a first effectiveness of the centralized isolation and a second effectiveness of the household isolation are calculated.
In an exemplary embodiment of the present disclosure, the prevention and control effectiveness is a reduction rate of the number of epidemic propagation people after taking the prevention and control measures, and predicting the prevention and control effectiveness for the second-level packer by the effectiveness of each prevention and control measure includes: determining a case probability of the first-level connector being transferred to a diagnosed patient; predicting a first reduction rate of the centralized isolation for the first-level closely-connected people and the second-level closely-connected people through the effectiveness and the case probability respectively corresponding to each prevention and control measure; predicting a second rate of reduction of the home isolation for both the first level of the fitter and the second level of the fitter; and predicting a third reduction rate of taking the centralized quarantine for the first level of contractors and the home quarantine for the second level of contractors.
In an exemplary embodiment of the present disclosure, after predicting the effectiveness of prevention for the second level of adhesion by the effectiveness of each prevention measure, further includes: and comparing the first reduction rate, the second reduction rate and the third reduction rate to send a comparison result to a user side as a decision suggestion for the epidemic situation prevention and control decision.
In an exemplary embodiment of the disclosure, the calculating the effectiveness of the prevention and control of the diagnosed patient and the first-level packer by epidemic statistics includes: acquiring a prevention and control time period for taking the prevention and control measures for the patient with the diagnosis and the patient with the diagnosis is closely contacted at the first level; calculating a first propagation rate before the prevention and control time period and a second propagation rate in the prevention and control time period through the epidemic situation statistical data; and determining the prevention and control effectiveness of the first-level adhesion based on the first propagation rate and the second propagation rate.
In an exemplary embodiment of the disclosure, the calculating the effectiveness of the prevention and control of the diagnosed patient and the first-level packer by epidemic statistics includes: collecting epidemic situation statistical data of a target area and crowd characteristics of the target area; and acquiring an effectiveness prediction model based on the crowd characteristics and the epidemic situation statistical data so as to determine the prevention and control effectiveness of the first-level closely-connected person through the effectiveness prediction model.
According to a second aspect of the embodiments of the present disclosure, an epidemic prevention and control effectiveness determining apparatus is provided, which may include a data acquisition module, an effectiveness determining module, and a prevention and control decision determining module.
The data acquisition module is used for: and acquiring contactor data of the diagnosed patient, and calculating the proportion of related people in the first-level close-connected patients of the diagnosed patient, which have related relations with the diagnosed patient, according to the contactor data.
The validity determination module is used for: and calculating the prevention and control effectiveness of the diagnosed patient and the first-level close connector through epidemic situation statistical data, and calculating the effectiveness of each prevention and control measure by combining the proportion of related people and the prevention and control effectiveness.
And the prevention and control decision determining module is used for predicting the prevention and control effectiveness of the second-level packer of the diagnosed patient through the effectiveness of each prevention and control measure.
In an exemplary embodiment of the present disclosure, the prevention and control measures include at least centralized isolation, at home isolation.
In an exemplary embodiment of the present disclosure, the validity determining module may include a probability calculating unit, and a validity calculating unit.
Wherein the probability calculation unit is used for: and determining occurrence probabilities of the centralized isolation and the home isolation in the first-level close-connected persons respectively.
The validity calculation unit is used for: based on the occurrence probability, the relatives population proportion and the prevention and control effectiveness, which are respectively corresponding to the centralized isolation and the household isolation, a first effectiveness of the centralized isolation and a second effectiveness of the household isolation are calculated.
In an exemplary embodiment of the present disclosure, the prevention and control effectiveness is a reduction rate of the number of epidemic situation spread people after taking the prevention and control measures, and the prevention and control decision determining module may include a case probability calculating unit, a first index calculating unit, a second index calculating unit, and a third index calculating unit.
Wherein, case probability calculation unit is used for: the probability of the first-level connector going to a case of the diagnosed patient is determined.
The first index calculation unit is used for: and predicting a first reduction rate of centralized isolation for the first-level closely-connected people and the second-level closely-connected people through the effectiveness and the case probability respectively corresponding to each prevention and control measure.
The second index calculation unit is used for: a second rate of reduction of the home isolation is predicted to be taken for both the first level of huggers and the second level of huggers.
The third index calculation unit is used for: predicting a third reduction rate of taking the centralized isolation for the first level of connectors and the home isolation for the second level of connectors.
In an exemplary embodiment of the present disclosure, the prevention decision determination module is configured to: and comparing the first reduction rate, the second reduction rate and the third reduction rate to send a comparison result to a user side as a decision suggestion for the epidemic situation prevention and control decision.
In an exemplary embodiment of the present disclosure, the validity determination module may specifically include a prevention time acquisition unit, a disease propagation rate calculation unit, and a prevention validity calculation unit.
The prevention and control time acquisition unit is used for: and acquiring a prevention and control time period for taking the prevention and control measures for the patient with the diagnosis and the patient with the first level close together.
The disease transmission rate calculation unit is configured to: calculating a first propagation rate before the prevention and control time period and a second propagation rate in the prevention and control time period through the epidemic situation statistical data.
The prevention and control effectiveness calculating unit is used for: and determining the prevention and control effectiveness of the first-level adhesion based on the first propagation rate and the second propagation rate.
In an exemplary embodiment of the present disclosure, the validity determination module may specifically include a target region data acquisition unit and a model prediction unit.
Wherein the target area data acquisition unit is used for: collecting epidemic situation statistical data of a target area and crowd characteristics of the target area.
The model prediction unit is used for: and acquiring an effectiveness prediction model based on the crowd characteristics and the epidemic situation statistical data so as to determine the prevention and control effectiveness of the first-level closely-connected person through the effectiveness prediction model.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic device, comprising: one or more processors; and a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the epidemic prevention and control effectiveness determination method according to the first aspect in the above embodiment.
According to a fourth aspect of the embodiments of the present disclosure, there is provided a computer readable medium having stored thereon a computer program, which when executed by a processor, implements the epidemic prevention and control effectiveness determination method according to the first aspect of the embodiments described above.
According to the epidemic situation prevention and control effectiveness determining method, device, electronic equipment and computer readable medium provided by the embodiment of the disclosure, the prevention and control effectiveness of people who have taken prevention and control, namely the diagnosed patient and the first-level closely connected person can be calculated through epidemic situation statistical data, so that the effectiveness of prevention and control and the proportion of relatives in the data of the contact person, which have relatives with the diagnosed patient, can be combined, the effectiveness corresponding to various prevention and control measures can be calculated, the quantification of the effectiveness of epidemic situation prevention and control is realized, and the accuracy of prevention and control measure control is improved; the effectiveness of prevention and control can be predicted by the effectiveness of each prevention and control measure, and the prevention and control effectiveness of prevention and control can be adopted for the second-level close-connected patients of the confirmed patients, so that data support is provided for epidemic prevention and control decision, and epidemic control is facilitated; and the cost of epidemic situation prevention and control can be conveniently controlled through the prevention and control effectiveness, and prevention and control resources are saved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort. In the drawings:
FIG. 1 schematically illustrates an exemplary system architecture diagram of an epidemic prevention and control effectiveness determination method or an epidemic prevention and control effectiveness determination device applied to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a method of determining effectiveness of epidemic prevention and control in accordance with an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flowchart of steps for calculating a prevention and control effectiveness in accordance with an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flowchart of steps for calculating a prevention and control effectiveness in accordance with another embodiment of the present disclosure;
FIG. 5 schematically illustrates a flowchart of steps for calculating the effectiveness of various prevention and control measures in accordance with an embodiment of the present disclosure;
FIG. 6 schematically illustrates a flowchart of steps for calculating the effectiveness of various prevention and control measures in accordance with another embodiment of the present disclosure;
FIG. 7 schematically illustrates a block diagram of an epidemic prevention and control effectiveness determination device according to an embodiment of the present disclosure;
fig. 8 shows a schematic diagram of a computer system suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
In the present specification, the terms "a," "an," "the," "said" and "at least one" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising," "including," and "having" are intended to be inclusive and mean that there may be additional elements/components/etc., in addition to the listed elements/components/etc.; the terms "first," "second," "third," and the like are used merely as labels, and are not intended to limit the number of subjects.
The following describes example embodiments of the present disclosure in detail with reference to the accompanying drawings.
FIG. 1 illustrates a schematic diagram of a system architecture of an exemplary application environment that may be applied to an epidemic prevention effectiveness determination method or an epidemic prevention effectiveness determination device of an embodiment of the present disclosure.
As shown in fig. 1, the system architecture 100 may include one or more of the terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including, but not limited to, desktop computers, portable computers, smart phones and tablets, wearable devices, virtual reality devices, smart homes, etc.
The server 105 may be a server providing various services, such as a background management server providing support for devices operated by users with the terminal devices 101, 102, 103. The background management server can analyze and process the received data such as the request and the like, and feed back the processing result to the terminal equipment.
For example, the server 105 may, for example, obtain contactor data for a diagnosed patient; calculating and determining the proportion of relatives in the first-level close-contact patients of the patients according to the contactor data, wherein the relatives have relatives with the diagnosed patients; calculating the prevention and control effectiveness of the first-level closely-connected people through epidemic situation statistical data; calculating the effectiveness of each prevention and control measure by combining the proportion of relatives and people and the prevention and control effectiveness; the server 105 may predict the effectiveness of the control over the second level of the fitter of the diagnosed patient, for example, by the effectiveness of the individual control measures.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, the server 105 may be a server cluster formed by a plurality of servers.
The methods provided by the embodiments of the present disclosure are generally performed by the server 105, and accordingly, the structured-based text addressing apparatus is generally disposed in the server 105. It will be readily understood by those skilled in the art that the text addressing method based on structuring provided in the embodiments of the present disclosure may also be performed by the terminal devices 101, 102, 103, and accordingly, the text addressing device based on structuring may also be provided in the terminal devices 101, 102, 103, which is not particularly limited in the present exemplary embodiment.
The novel coronavirus pneumonia (hereinafter referred to as "new crown") is an infectious disease, and before the vaccine is not popularized and used, prevention and control measures are taken to inhibit the transmission of the vaccine from the aspect of transmission ways, so that the development of the disease can be effectively slowed down and controlled. At present, prevention and control measures which mainly reduce the contact rate of people are adopted for new crown diseases, such as closing schools, workplaces, limiting parties, isolating at home and the like, so that the transmission of diseases is reduced.
However, once an epidemic situation occurs, the propagation speed is very fast, and the implementation of the prevention and control measures requires a lot of labor and material costs, and the prevention and control effects obtained by adopting different prevention and control measures are different, for example, when the contactor of the patient to be diagnosed is subjected to centralized isolation, a lot of management costs are required, and when the contactor is subjected to home isolation, although the cost is low, whether a better effect can be obtained cannot be determined. The prevention and control measures cannot be easily withdrawn once the prevention and control measures are started, so that the effectiveness of taking various prevention and control measures is quantified before the prevention and control measures are implemented, and the method has important reference significance for prevention and control management staff.
Based on the above, the embodiment of the disclosure provides a technical scheme of an epidemic situation prevention and control effectiveness determining method, which can quantify various prevention and control measures by using epidemic situation statistical data generated by taking prevention and control measures so as to provide data support for epidemic situation prevention and control decisions.
As shown in fig. 2, the epidemic prevention and control effectiveness determining method provided by the embodiment of the present disclosure may include step S21, step S22, and step S23.
In step S21, contactor data of the diagnosed patient is acquired, and the proportion of the relative population in the first-level close-contact person of the diagnosed patient to the relative relationship of the diagnosed patient is calculated according to the contactor data.
A diagnosed patient refers to a patient who has been diagnosed with an infectious disease, such as a new crown disease. In this exemplary embodiment, the contactor data may include data of the first-level contact person of the patient to be diagnosed, specifically, may be identification information of the first-level contact person, such as a name, a contact way, a two-dimensional code, and the like, and may further include other data, such as an activity track record of the patient to be diagnosed, such as a place where the patient to be diagnosed has been visited before the doctor, and the like, and further, for example, vehicle information on which the patient to be diagnosed is seated, disease information of the patient to be diagnosed, and the like, and the embodiment is not limited thereto.
Where a first-level close-fitting person of a diagnosed patient may refer to a population in contact with the diagnosed patient, a person who is typically co-located with the diagnosed patient during the same time period may be considered in contact with the diagnosed patient, e.g., all people who have arrived at the supermarket during a particular time period, people who have ridden with the same vehicle as the diagnosed patient, etc. A second level of fitter of a diagnosed patient may refer to a population in contact with the first level of fitter.
In some embodiments, when the user confirms the doctor, the identification information of the first-level close contact person in contact with the user can be obtained through the active recall of the user, and the activity track of the user can be queried through the identification information of the user, so that the data of the contact person in coincidence with the activity track can be screened out.
After the contactor data of the diagnosed patient is obtained, people with relatives between the first-level close contact person and the diagnosed patient in the contactor data can be counted, for example, for the diagnosed patient A, after all people contacted with the patient A are obtained, the relationship between the patient A and the people contacted with the patient A is determined according to the family relationship of the patient A, so that the people with relatives to the patient A are determined. The population of the first-level close-connected patients with the relatives is determined, and the total population of the first-level close-connected patients is used for calculating the proportion of the relatives, for example, the number of the first-level close-connected patients is 10, the number of the first-level close-connected patients is 1000, the number of the first-level close-connected patients with the relatives is 50, and 50/1000=0.05, namely, the proportion of the relatives is 0.05.
In step S22, the effectiveness of the prevention and control measures for the diagnosed patient and the first-level close-connected patient is calculated by epidemic situation statistics, and the effectiveness of each prevention and control measure is calculated by combining the proportion of the related population and the effectiveness of the prevention and control.
Epidemic statistics data may include data related to epidemic development counted in various countries and regions after an epidemic occurs, for example, total number of diagnosed patients, total number of newly-increased contactors, suspected patients, total number of cured patients, total number of newly-increased cured patients, etc., which is not limited in this embodiment.
In general, certain prevention and control measures are taken for the area where the epidemic situation occurs after the epidemic situation occurs, so that the prevention and control effectiveness obtained by the taken prevention and control measures can be calculated by using the epidemic situation statistical data of the area where the prevention and control measures are taken. In some embodiments, the prevention and control measures taken include at least centralized isolation and home isolation. By way of example, the rate of increase of the patient before the prevention and control measures are carried out and the rate of increase of the patient after the prevention and control measures are carried out can be calculated by means of epidemic situation statistical data, and the rate of decrease of the rate of increase is calculated and taken as the prevention and control effectiveness. For example, 100 patients are newly added in one week before the prevention and control measures are implemented, 50 patients are newly added in one week after the prevention and control measures are implemented, and the prevention and control effectiveness can be obtained by calculating (100-50)/7 days.
In some embodiments, the effectiveness of the prevention may be calculated by the steps shown in fig. 3.
As shown in fig. 3, in step S31, a prevention and control period for taking prevention and control measures for the diagnosed patient and the first-level close-connected patient of the diagnosed patient is acquired.
In general, various preventive measures are taken for the diagnosed patient and the first-level closely-connected patient, and an arbitrary time period from the time when the preventive measure is taken to the current time can be selected as the preventive control time period. The prevention and control period may be, for example, 3 days, 1 week, 15 days, 30 days, etc., which is not limited in this embodiment.
In step S32, a first propagation rate before the prevention and control period and a second propagation rate during the prevention and control period are calculated from the epidemic statistics.
The first propagation rate may refer to the propagation rate of the disease in an environment that is all susceptible to intervention without prevention and control; the second propagation rate may refer to a propagation rate of a disease equivalent to that in the real world under the influence of external factors such as prevention and control measures, changes in individual behaviors, and the like, without prevention and control intervention. The number of newly increased patients before prevention and control are adopted can be determined through epidemic situation statistical data, and then the first transmission rate is calculated through the number of newly increased patients and the transmission time. For example, the first transmission rate may be 20 people/day for a total of 10 days after the epidemic situation occurs and before the prevention and control measures are taken, and the number of newly increased patients is 200. Accordingly, the second propagation rate may be calculated by increasing the number of patients in the prevention and control period and the prevention and control period, for example, the prevention and control period is 1 month, the number of patients newly increased in the one month is 100, and the second propagation rate may be 3 persons/day.
In some embodiments, the first propagation rate may also be an average of one patient's number of people that are contagious, e.g., for patient a, determining the number of people in patient a who are in close contact at the first level that are in contact with the contact to a patient for diagnosis, may result in the number of people of patient a who are contagious, and so on, calculating the number of people of each patient who are contagious, and averaging the number of people of patients who are contagious. Accordingly, the second propagation rate may be the average number of people one patient can infect after taking the prevention and control measures. In addition, methods of calculating disease transmission rates before and after taking prevention and control measures by other means are within the scope of the present disclosure.
In step S33, the control availability for the first-level adhesion is determined based on the first propagation rate and the second propagation rate.
In some embodiments, the prevention effectiveness may be a rate of decrease in the propagation rate, e.g., R0 is a first propagation rate, R1 is a second propagation rate, and T is a prevention period, then the prevention effectiveness may be calculated by (R1-R2)/T.
In some embodiments, the effectiveness of the prevention may be calculated by the steps shown in fig. 4. As shown in fig. 4, in step S41, epidemic situation statistics data of a target region and crowd characteristics of the target region are collected.
Crowd characteristics refer to personality-specific target areas, and may specifically include population density, economic development indexes, such as people average GDP, etc., cultures, such as religious beliefs, etc., and other characteristics, such as aging duty ratio, geographic location, etc., which are not limited in this embodiment. Because the actual world is influenced by factors such as population density, social habits and the like, the adopted prevention and control measures in each region may be different, and the corresponding prevention and control effectiveness may also be different, so that epidemic situation statistical data of the target region can be acquired in a targeted manner in order to accurately determine the prevention and control effectiveness. The target area may be any area such as a country, a province, or a city, and the present embodiment is not limited thereto.
In step S42, a validity prediction model is obtained based on the crowd characteristics and epidemic situation statistical data, so as to determine the prevention and control validity of the first-level close-contact person through the validity prediction model.
For example, epidemic situation statistical data of each day and crowd characteristics of the target area on the same day can be used as training samples, regression tree models are adopted to train the training samples, and characteristics of newly added patients changing along with crowd characteristics are learned through the models, so that a trained effectiveness prediction model is obtained. Then, the crowd characteristics of the area where the first-level packer is located are input into the model, and the prevention and control effectiveness of the area where the first-level packer is located can be predicted by using the model. Moreover, in other embodiments, the validity prediction model may include other types of machine learning models, such as linear regression models, logistic regression models, and the like, which are within the scope of the present disclosure.
After the prevention and control effectiveness is obtained, the effectiveness of each prevention and control measure can be calculated by combining the proportion of related people and the prevention and control effectiveness. In particular, the method may include steps as shown in fig. 5.
As shown in fig. 5, in step S51, occurrence probabilities of the centralized isolation and the home isolation in the first-level close-connected person are determined.
Because the prevention and control measures of home isolation and centralized isolation are adopted for the diagnosed patients and the first-level closely-connected persons, the number of people in the home isolation and the number of people in the centralized isolation can be counted. The ratio of the number of people in the home isolation to the total number of closely connected people in the first level can be used as the occurrence probability corresponding to the home isolation; the ratio of the number of people in the centralized isolation to the total number of people in the first-level close-contact people can be used as the occurrence probability corresponding to the centralized isolation. For example, in the first-level close-connected people, the number of people in the centralized isolation is 200 people, the number of people in the home isolation is 500 people, and the occurrence probability corresponding to the centralized isolation is 200/(200+500) =2/7, and the number of people in the home isolation is 500/(200+500) =5/7.
In step S52, a first validity of the centralized isolation and a second validity of the home isolation are calculated based on occurrence probabilities, relative population proportions, and prevention and control validity, respectively, corresponding to the centralized isolation and the home isolation.
For example, assuming that the first validity is x1, the second validity is x2, the proportion of relatives can be P (A), the occurrence probability of centralized isolation is P (B), the occurrence probability of household isolation is 1-P (B), and the prevention and control validity is R, the method can determine
P (B) ×1+ [1-P (B) ]×2=r (formula one).
Since the average number of contacts per diagnosed patient can be determined in positive correlation with the effectiveness of the prevention and control measures, the relationship between the centrally isolated average number of contacts and the home isolated average number of contacts can be determined consistent with the relationship between the first effectiveness and the second effectiveness. The reduced average number of contacts is the non-related population relative to the centralized isolation, that is, the effectiveness achieved in the first-level close-fitting home isolation can only be effective for the non-related population, thereby:
x 1-P (a) ]=x2 (formula two).
Further, by using the first and second formulas, P (B) ×1+ [1-P (B) ]×1×1-P (a) ]=r, x1 can be calculated from the formula, and further by using the second formula, x2 can be calculated, thereby obtaining the first effectiveness of centralized isolation and the second effectiveness of home isolation.
In step S23, the effectiveness of the control of the second level of adhesion to the diagnosed patient is predicted by the effectiveness of the individual control measures.
Because taking prevention and control measures for the second-level fitter means taking prevention and control measures for the patient to be diagnosed and the first-level fitter, the prevention and control for the second-level fitter can comprise various combinations of different prevention and control measures, and various prevention and control measure combinations with higher feasibility can be listed in advance; or, various prevention and control measures can be provided through the visual interface for the manager to select, for example, all feasible prevention and control measures are displayed in the interface, and the user can select one or more other prevention and control measures through clicking and selecting, so that the prevention and control measures selected by the user are stored as prevention and control measure combinations. For example, taking control of the second level of the fitter may include taking control measures for both the first level of the fitter and the second level of the fitter, such as taking centralized isolation for both the first level of the fitter and the second level of the fitter; centralized isolation is adopted for the first-level close-connected people, and home isolation is adopted for the second-level close-connected people; and house isolation is adopted for the first-level closely-connected person and the second-level closely-connected person at the same time.
The first effectiveness of centralized isolation and the second effectiveness of household isolation are obtained through the calculation, and the prevention and control effectiveness of various epidemic prevention and control decisions is calculated. The prevention and control effectiveness refers to the reduction rate of the number of people spreading epidemic situation after taking prevention and control measures. In particular, the method of calculating the effectiveness of prevention and control for the second level of adhesion of the diagnosed patient may include the steps shown in FIG. 6.
As shown in fig. 6, in step S61, the case probability that the first-level packer turns to a diagnosed patient is determined.
In the process of taking prevention and control measures on the first-level closely connected people, the first-level closely connected people can be diagnosed as patients, the number of people converted into the patients in all the current first-level closely connected people is counted, and then the case probability of the diagnosed patients in the first-level closely connected people is obtained. For example, the total number of first-level correspondents is 200, and the total number of patients to be diagnosed is 50, and the case probability of the first-level correspondents is 50/200=0.25.
In step S62, the first reduction rate of the centralized isolation is predicted for the first-level and second-level closely-connected people, which are people closely contacted with the first-level closely-connected people, by the effectiveness and the case probability respectively corresponding to the prevention and control measures.
If the prevention and control measures are also taken for the second-level fitter of the first-level fitter, the number of people infected with the confirmed cases in the first-level fitter can be avoided, that is, if the case probability of the first-level fitter being converted to the confirmed patients is P (C), the effectiveness can be improved 1+P (C) when the prevention and control measures are taken for both the first-level fitter and the second-level fitter. Therefore, when the centralized isolation prevention and control measures are adopted for the first-level close connector and the second-level close connector at the same time, the first reduction rate can be obtained as follows: [1+P (C) ]. X1.
In step S63, a second rate of reduction of the home isolation is predicted to be taken for both the first level of the fitter and the second level of the fitter.
For example, if the case probability of the patient to be diagnosed in the first-level packer is P (C), the reduction rate of the prevention and control decision can be obtained when the first-level packer and the second-level packer are isolated at home at the same time: [1+P (C) ]. X2.
In step S64, a third rate of reduction of the centralized isolation for the first level of connectors and the home isolation for the second level of connectors is predicted.
As can be seen from the contactor data of the diagnosed patient, the population having the probability of P (a) in the first-level contactor has a relationship with the diagnosed patient, and the second-level contactor also has a relationship with the first-level contactor according to the probability, so if the case probability of the first-level contactor being converted into the diagnosed patient is P (C), the reduction rate of collecting isolation for the first-level contactor and collecting isolation for the second-level contactor is: x is x 1 *[1+P(C)]*[1-P(A)]。
After the prevention and control effectiveness corresponding to various prevention and control measure combinations is obtained, the prevention and control measure combinations can be compared, and the comparison result is sent to the client corresponding to the manager for reasonable decision making by the user, so that the maximum benefit is obtained, the prevention and control cost is better controlled, and the consumption of resources is reduced.
For example, the first reduction rate, the second reduction rate and the third reduction rate may be compared, and the comparison result may be sent to the user side as a decision suggestion for epidemic prevention and control decision. The comparison result may include differences between the reduction rates, and may further include various factors such as difficulty and cost of implementing each control decision. Moreover, the recommended combination of prevention and control measures may be given according to the difference between the respective reduction rates, for example, since the cost of several kinds of isolation is high relative to the at-home isolation, if it is calculated that the first reduction rate at which the centralized isolation is simultaneously taken is not much different from the second reduction rate at which the at-home isolation is simultaneously taken, the at-home isolation may be recommended. In addition, in some embodiments, the comparison result of the prevention and control measure combination of each region can be calculated according to the different prevention and control effectiveness of each region, so that decision suggestions for different regions are further displayed.
In some exemplary embodiments, the reduction rate of each epidemic prevention measure combination can be converted into a visual chart to be displayed on the user side. For example, the reduction rate may be converted into a bar graph, a line graph, a pie graph, etc., and different epidemic prevention and control measures may be combined in the same graph to be displayed differently, e.g., in the bar graph, the first reduction rate may be displayed as blue, the second reduction rate may be displayed as red, the third reduction rate may be displayed as green, etc. Through visual display, a user can more intuitively see the effectiveness difference of different epidemic situation prevention and control measure combinations, so that more intuitive reference is provided for a decision maker.
The following describes embodiments of the apparatus of the present disclosure that may be used to perform the above-described epidemic prevention and control effectiveness determination methods of the present disclosure. Referring to fig. 7, an epidemic prevention and control effectiveness determining apparatus 70 provided by an embodiment of the present disclosure may include: a data acquisition module 71, a validity determination module 72 and a prevention decision determination module 73.
The data acquisition module 71 is configured to acquire contactor data of a patient to be diagnosed, and calculate a proportion of relatives of the patient to be diagnosed in a first-level close-connected patient of the patient to be diagnosed according to the contactor data.
The effectiveness determining module 72 is configured to calculate effectiveness of prevention and control measures for the diagnosed patient and the first-level close-connected patient according to epidemic situation statistics, and calculate effectiveness of each prevention and control measure according to the relative population proportion and the effectiveness of prevention and control.
A prevention decision determination module 73 for predicting the effectiveness of prevention for the second level of adhesion of the diagnosed patient by the effectiveness of the respective prevention measure.
In an exemplary embodiment of the present disclosure, the prevention and control measures include at least centralized isolation, at home isolation.
In an exemplary embodiment of the present disclosure, the validity determination module 72 may include a probability calculation unit, and a validity calculation unit.
Wherein the probability calculation unit is used for: and determining occurrence probabilities of the centralized isolation and the home isolation in the first-level close-connected persons respectively.
The validity calculation unit is used for: based on the occurrence probability, the relatives population proportion and the prevention and control effectiveness, which are respectively corresponding to the centralized isolation and the household isolation, a first effectiveness of the centralized isolation and a second effectiveness of the household isolation are calculated.
In an exemplary embodiment of the present disclosure, the prevention and control effectiveness is a reduction rate of the number of epidemic situation spread people after taking the prevention and control measures, and the prevention and control decision determining module 73 may include a case probability calculating unit, a first index calculating unit, a second index calculating unit, and a third index calculating unit.
Wherein, case probability calculation unit is used for: the probability of the first-level connector going to a case of the diagnosed patient is determined.
The first index calculation unit is used for: and predicting a first reduction rate of centralized isolation for the first-level closely-connected people and the second-level closely-connected people through the effectiveness and the case probability respectively corresponding to each prevention and control measure.
The second index calculation unit is used for: predicting a second rate of reduction of the home isolation for both the first level of the fitter and the second level of the fitter;
the third index calculation unit is used for: predicting a third reduction rate of taking the centralized isolation for the first level of connectors and the home isolation for the second level of connectors.
In an exemplary embodiment of the present disclosure, the prevention decision determination module 73 is configured to: and comparing the first reduction rate, the second reduction rate and the third reduction rate to send a comparison result to a user side as a decision suggestion for the epidemic situation prevention and control decision.
In an exemplary embodiment of the present disclosure, the validity determination module 72 may specifically include a prevention time acquisition unit, a disease propagation rate calculation unit, and a prevention validity calculation unit.
The prevention and control time acquisition unit is used for: and acquiring a prevention and control time period for taking the prevention and control measures for the patient with the diagnosis and the patient with the first level close together.
The disease transmission rate calculation unit is configured to: calculating a first propagation rate before the prevention and control time period and a second propagation rate in the prevention and control time period through the epidemic situation statistical data.
The prevention and control effectiveness calculating unit is used for: and determining the prevention and control effectiveness of the first-level adhesion based on the first propagation rate and the second propagation rate.
In an exemplary embodiment of the present disclosure, the validity determination module 72 may specifically include a target region data acquisition unit and a model prediction unit.
Wherein the target area data acquisition unit is used for: collecting epidemic situation statistical data of a target area and crowd characteristics of the target area.
The model prediction unit is used for: and acquiring an effectiveness prediction model based on the crowd characteristics and the epidemic situation statistical data so as to determine the prevention and control effectiveness of the first-level closely-connected person through the effectiveness prediction model.
Since each functional module of the epidemic prevention and control effectiveness determining apparatus of the exemplary embodiment of the present disclosure corresponds to a step of the exemplary embodiment of the above-described epidemic prevention and control effectiveness determining method, for details not disclosed in the embodiment of the apparatus of the present disclosure, please refer to the embodiment of the above-described epidemic prevention and control effectiveness determining of the present disclosure.
Referring now to FIG. 8, there is illustrated a schematic diagram of a computer system 800 suitable for use in implementing an electronic device of an embodiment of the present disclosure. The computer system 800 of the electronic device shown in fig. 8 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 8, the computer system 800 includes a Central Processing Unit (CPU) 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for system operation are also stored. The CPU 1201, ROM 802, and RAM 803 are connected to each other through a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, mouse, etc.; an output portion 807 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 808 including a hard disk or the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. The drive 810 is also connected to the I/O interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as needed so that a computer program read out therefrom is mounted into the storage section 808 as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809, and/or installed from the removable media 811. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 801.
It should be noted that the computer readable medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs that, when executed by one of the electronic devices, cause the electronic device to implement the epidemic prevention and control effectiveness determination method as described in the above embodiments.
For example, the electronic device may implement the method as shown in fig. 2: step S21, acquiring contactor data of a diagnosed patient, and calculating the proportion of relative population in a first-level close contact of the diagnosed patient and having relative relationship with the diagnosed patient according to the contactor data; step S22, calculating the prevention and control effectiveness of the diagnosed patient and the first-level close connector through epidemic situation statistical data, and calculating the effectiveness of each prevention and control measure by combining the proportion of the relatives and the prevention and control effectiveness; step S23, predicting the control effectiveness of the second-level close-fitting person of the diagnosed patient through the effectiveness of each control measure.
As another example, the electronic device may implement the various steps shown in fig. 3-6.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (7)
1. The epidemic situation prevention and control effectiveness determining method is characterized by comprising the following steps of:
acquiring contactor data of a diagnosed patient, and calculating the proportion of related people in a first-level close contact of the diagnosed patient and related people of the diagnosed patient according to the contactor data;
calculating the prevention and control effectiveness of the diagnosed patient and the first-level close-connected person through epidemic situation statistical data, and calculating the effectiveness of each prevention and control measure by combining the proportion of related people and the prevention and control effectiveness, wherein the prevention and control measures at least comprise centralized isolation and home isolation; wherein the calculating the effectiveness of each prevention and control measure comprises: determining occurrence probabilities of the centralized isolation and the home isolation in the first-level close-connected person respectively; calculating a first effectiveness of the centralized isolation and a second effectiveness of the home isolation based on occurrence probabilities, the relative population proportion and the prevention and control effectiveness, which correspond to the centralized isolation and the home isolation respectively;
Predicting the prevention and control effectiveness of the second-level packer of the diagnosed patient according to the effectiveness of each prevention and control measure, wherein the prevention and control effectiveness is the reduction rate of the number of epidemic situation spread people after the prevention and control measure is taken; wherein the predicting effectiveness of the prevention and control of the second level of adhesion of the diagnosed patient comprises: determining a case probability of the first-level connector being transferred to a diagnosed patient; predicting a first reduction rate of the centralized isolation for the first-level closely-connected people and the second-level closely-connected people through the effectiveness and the case probability respectively corresponding to each prevention and control measure; predicting a second rate of reduction of the home isolation for both the first level of the fitter and the second level of the fitter; and predicting a third reduction rate of taking the centralized quarantine for the first level of contractors and the home quarantine for the second level of contractors.
2. The method of claim 1, further comprising, after predicting the effectiveness of the prevention of the second level of adhesion to the diagnosed patient by the effectiveness of each prevention measure:
and comparing the first reduction rate, the second reduction rate and the third reduction rate to send a comparison result to a user side as a decision suggestion for the epidemic situation prevention and control decision.
3. The method of claim 1, wherein the calculating the effectiveness of control of the diagnosed patient and the first-level packer by epidemic statistics comprises:
acquiring a prevention and control time period for taking the prevention and control measures for the patient with the diagnosis and the patient with the diagnosis is closely contacted at the first level;
calculating a first propagation rate before the prevention and control time period and a second propagation rate in the prevention and control time period through the epidemic situation statistical data;
and determining the prevention and control effectiveness of the first-level adhesion based on the first propagation rate and the second propagation rate.
4. The method of claim 1, wherein the calculating the effectiveness of control of the diagnosed patient and the first-level packer by epidemic statistics comprises:
collecting epidemic situation statistical data of a target area and crowd characteristics of the target area;
and acquiring an effectiveness prediction model based on the crowd characteristics and the epidemic situation statistical data so as to determine the prevention and control effectiveness of the first-level closely-connected person through the effectiveness prediction model.
5. An epidemic prevention and control effectiveness determining device, comprising:
The data acquisition module is used for acquiring contactor data of the diagnosed patient, and calculating the proportion of relative population in the first-level close-contact person of the diagnosed patient and the relative relationship of the patient;
the effectiveness determining module is used for calculating the effectiveness of prevention and control on the diagnosed patient and the first-level close connector through epidemic situation statistical data, and calculating the effectiveness of each prevention and control measure by combining the proportion of related people and the effectiveness of prevention and control, wherein the prevention and control measures at least comprise centralized isolation and household isolation; wherein the calculating the effectiveness of each prevention and control measure comprises: determining occurrence probabilities of the centralized isolation and the home isolation in the first-level close-connected person respectively; calculating a first effectiveness of the centralized isolation and a second effectiveness of the home isolation based on occurrence probabilities, the relative population proportion and the prevention and control effectiveness, which correspond to the centralized isolation and the home isolation respectively;
the prevention and control decision determining module is used for predicting the prevention and control effectiveness of the second-level closely-connected person of the diagnosed patient according to the effectiveness of each prevention and control measure, wherein the prevention and control effectiveness is the reduction rate of the number of epidemic situation spread people after the prevention and control measure is taken; wherein the predicting effectiveness of the prevention and control of the second level of adhesion of the diagnosed patient comprises: determining a case probability of the first-level connector being transferred to a diagnosed patient; predicting a first reduction rate of the centralized isolation for the first-level closely-connected people and the second-level closely-connected people through the effectiveness and the case probability respectively corresponding to each prevention and control measure; predicting a second rate of reduction of the home isolation for both the first level of the fitter and the second level of the fitter; and predicting a third reduction rate of taking the centralized quarantine for the first level of contractors and the home quarantine for the second level of contractors.
6. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the epidemic prevention and control effectiveness determination method of any one of claims 1 to 4.
7. A computer-readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the epidemic prevention and control effectiveness determination method according to any one of claims 1 to 4.
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