CN113077907B - Novel infectious disease road infection probability calculation model - Google Patents
Novel infectious disease road infection probability calculation model Download PDFInfo
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Abstract
The invention provides a travel infection probability calculation method suitable for novel coronaviruses, which comprises the following steps: establishing a road infection probability database of the selected area; constructing a novel infectious disease model SEIRK based on the SEIR model; building a street pedestrian flow model; according to the road infection probability database, a novel infectious disease model SEIRK and a street pedestrian flow model are synthesized, a novel infectious disease road infection probability calculation model is constructed, and further the travel infection probability of the novel coronavirus is predicted and calculated. Compared with the traditional virus diffusion model for calculating the infection probability of the infectious viruses, the method comprehensively considers the influence of the density of pedestrians on the road, the wearing of the mask by the pedestrians, the distance from the living place of the ill person and the like on the novel infectious viruses, can reduce the risk of the pedestrians going out to be infected to a greater extent, and has important significance in the aspects of inhibiting epidemic spread and the like.
Description
Technical Field
The invention belongs to the field of infectious disease model construction, and particularly relates to a travel infection probability calculation method suitable for novel coronaviruses.
Background
There are many practical virus diffusion models, however, most of these models are not very specific to viruses, and the main problems are as follows:
1. the diffusion model considers that the content is too single, focuses on the virus itself and ignores the influence of the external environment on the virus, so that the simulated virus diffusion is biased;
2. the diffusion model concentrates on the change of the number of people transmitted along with time, ignores the influence caused by free population flow and national preventive measures, and cannot react according to the data of virus real-time transformation;
3. the diffusion model focuses on macroscopic infection of viruses, and based on evolution of specific data at specific time, the transformation probability of the virus cannot be specified to a certain time;
disclosure of Invention
In order to solve the technical problems, the invention provides a method for calculating the infection probability of a novel coronavirus on a road, which is applicable to a plurality of similar infectious diseases including 2019-nCoV (2019 novel coronavirus), aims to solve the problem that the existing infectious disease spread prediction model can not truly predict the infection probability of the infectious disease on the road, and provides reference for preventing the infectious disease.
The technical scheme for solving the problems is as follows: the travel infection probability calculation method suitable for the novel coronavirus comprises the following steps of:
1) Establishing a street infection probability database of the selected area;
2) Constructing a novel infectious disease model SEIRK based on the SEIR model according to the acquired data to simulate novel infectious disease infection;
3) Constructing a street pedestrian flow model according to the acquired data;
4) And constructing a novel infectious disease traveling infection probability calculation model according to the traveling infection probability database and the comprehensive infection model SEIRK and the street pedestrian flow model.
1. The method for calculating the traveling infection probability applicable to the novel coronavirus comprises the following specific steps of:
1-1) survey the number of patients in the region to be surveyed and the geographical position of the residence thereof according to the time line;
1-2) drawing a distance-infection rate simulation curve according to the data obtained by investigation;
1-2-1) acquisition of patient affected areas
Taking the living place of the patient in the data as the circle center, and taking the infection inner diameter r as the radius to form a circular area: s=pi r 2 This area is the patient affected area.
1-2-2) construction of street infection Rate model
Investigation of the patient affected area as a subject has found that the risk of infection with new coronavirus in the area within 1 meter of the place where the infected person resides is reduced by a factor of 80%; the risk of infection with new coronavirus in the area 1 m or more from the place where the infected person resides is reduced by 50% by index, so that a calculation formula of a street infection rate model (x is distance (unit: m), and Y is infection rate) is obtained as follows:
1-2-3) distance-infection rate simulation curves were plotted
Distance-infection rate simulated curves were plotted with distance on the x-axis and infection rate on the y-axis.
1-3) establishing a road infection probability database according to the data obtained by the distance-infection rate simulation curve, wherein if the influence areas of the patients coincide, the influence values are accumulated.
2. The method for calculating the traveling infection probability applicable to the novel coronavirus comprises the following specific steps of:
2-1) determining model considerations:
2-1-1) S-a susceptible person, i.e., a population susceptible to infection but not yet suffering from disease;
2-1-2) E-viral latency carrier, the latency being the group of people with disease latency who are ill but asymptomatic, the novel coronavirus also being infectious during latency;
2-1-3) I-the infected person, the infected person is the infected person;
2-1-4) R is a rehabilitating person, the rehabilitating person heals the patient, and the model considers that the rehabilitating person cannot be infected again;
2-1-5) K, which is a resistent, namely the number of people wearing the mask in the crowd, and the model considers that the wearing of the mask in epidemic situation can inhibit the spread of viruses to a certain extent, thereby effectively reducing the risk of infecting new coronaviruses;
2-2) according to the consideration, the virus diffusion simulation is carried out by taking time t as a variable, and the formula is calculated as follows:
2-2-1) counting the number of susceptible people on day t
2-2-2) counting the number of people with latent form on day t
2-2-3) counting the number of infected persons on day t
2-2-4) calculating the number of rehabilitative persons on day t
2-2-5) calculating the number of preventable persons on day t
Wherein S (t) is the number of susceptible persons on the t th day, R (t) is the number of recovered persons on the t th day, I (t) is the number of infected persons on the t th day, E (t) is the number of latent persons on the t th day, R is the number of persons contacting the patient when no strict measures are taken in the initial stage, R2 is the number of persons contacting the patient when strict measures are taken after epidemic situation occurs, b is the infection probability when no strict measures are taken in the initial stage, b2 is the infection probability when strict measures are taken after epidemic situation occurs, N is the current time pedestrian flow obtained according to a street pedestrian flow model, y is the recovery probability, and c is the probability of having resistant persons in the crowd (namely the probability of wearing a mask in the crowd).
3. The method for calculating the traveling infection probability applicable to the novel coronavirus comprises the following specific steps of:
3-1) obtaining street traffic
3-1-1) acquiring the number of user locations
First, dividing a street into n regionsA field recording the range O of each area i (i=1, 2, …, n), after the user allows to obtain his longitude and latitude coordinates, looking at which area the user coordinates are in at this time, so as to obtain the people flow data of each area of the street;
3-1-2) directly obtaining the flow of street people
And transmitting the shot video pictures to a server by using the existing cameras of each traffic road section, obtaining the number of pedestrians through a set of embedded intelligent video screen analysis algorithm, and carrying out density conversion according to the number of pedestrians obtained through recognition.
The number of people conversion formula is as follows:
N＝m*c
m＝p/m 0
wherein N is the total population number obtained by conversion, m is the number of people in a unit length (pedestrian density), p is the average number of people which can be identified by all cameras entering the current street, and m 0 The street length taken by the camera is calculated, and c is the actual street length, so that the people flow number is finally calculated.
3-2) correcting the people flow data with a linear least squares method
3-2-1) software and hardware data combining
The people flow data obtained by software and the people flow data obtained by hardware are combined into a group of two-dimensional data (x t ,y t )， t＝1,2,…,l，x t The time t is represented, the people flow data counted by the user positioning is obtained by software, and x is t Different from each other, y t The time t is represented by the people flow data acquired by the hardware equipment, and the time l is represented by the time of acquiring the last group of data; the least square method is applied to obtain a function y=f (x) so that the sum of the square difference of the least square method result and the actual hardware obtained flow of people in the recorded momentMinimum;
3-2-2) the people flow data x obtained by the software at the time t+1 t+1 Substituting f (x), y t+1 Acquiring people flow data for the corrected software;
3-3) time series prediction
The people flow of each street is time, the people flow is predicted by adopting a 'simple sequence time average method' in a time sequence prediction method, and the calculation steps are as follows:
3-3-1) collecting time series sample data a at l' times daily over m days i′j′ Where i 'denotes the date number, i' =1, 2, …, m, j 'denotes the time number, j' =1, 2, …, l, and a is recovered after correction i′j′ ；
3-3-2) calculating the arithmetic mean of the m days l' time instants
3-3-3) predictive computation
When the time series is listed by time, the people flow predictive value y at the j-th time of the predictive date m+1 day m+1,j The method comprises the following steps:
3-4) using the obtained people flow data after the hardware acquired people flow and the software system acquired people flow are processed by the least square method as the result of the people flow model.
4. The method for calculating the traveling infection probability of the novel coronavirus comprises the following specific steps of:
4-1) determining model parameters:
w: acquiring the safety distance between people according to the investigation;
x: pedestrian position is based on the nearest patient residence distance;
t: predicting the occurrence time of the infectious disease on the same day;
pp: acquiring road infection probability data from the travel infection probability database;
i (t): the number of people infected on the t-th day obtained from the new infectious disease model SEIRK;
k (t): the number of preventable persons on the t-th day obtained from the new infectious disease model SEIRK;
e (t): the number of people with latent people on the t-th day obtained from a new infectious disease model SEIRK;
n: and obtaining the pedestrian traffic at the current moment according to the street pedestrian traffic model.
4-2) dynamic and static combination calculation of the probability of travel infection of novel coronaviruses
If x > w, the calculation formula is as follows:
P＝(0.5*(2-w)+1)*P′
otherwise, the calculation formula is as follows:
the invention has the beneficial effects that:
1. the method comprehensively considers the influence of multiple factors such as environment, people flow, distance, wearing condition of the mask of pedestrians and the like on the spread of viruses of the infectious diseases, and improves the accuracy of calculating the probability of virus infection.
2. The model is based on the SEIR model, and provides a SEIRK model, and new parameters (resistant) are added to perform diffusion simulation, so that the predicted result is more approximate to the actual value.
3. The pedestrian identifier is introduced to detect pedestrians, the images shot by the cameras are processed mainly by the cameras at the entrances and exits of each street, and the number of passenger flows is calculated by means of density conversion of pedestrians.
4. The street traffic is calculated and corrected by adopting a mode of combining hardware and software, so that the credibility of error increasing data can be reduced.
5. The method can be applied to construction and simulation of a complex virus propagation prediction model, can reduce the risk of pedestrian outing infection, and has important significance in the aspects of inhibiting epidemic spread and the like.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a flowchart of a street infection probability database for creating a selected area;
FIG. 3 is a graph of distance-infection rate modeling of the present invention;
FIG. 4 is a flow chart for constructing a novel infection model SEIRK;
FIG. 5 is a simulated graph of an infectious disease transmission curve in an embodiment of the present invention;
FIG. 6 is a flow chart for building a street pedestrian traffic model;
FIG. 7 is a flow chart of a method model for constructing a traveling infection probability calculation for a novel coronavirus.
Detailed description of the preferred embodiments
The invention is further described below with reference to the drawings and examples.
As shown in fig. 1, the present invention provides a method for constructing a method for calculating a probability of traveling infection applicable to a novel coronavirus, comprising the steps of:
1) Building a street infection probability database of the selected area, as shown in fig. 2:
1-1) survey the number of patients in the region to be surveyed and the geographical position of the residence thereof according to the time line;
1-2) drawing a distance-infection rate simulation curve according to the data obtained by investigation;
1-2-1) acquisition of patient affected areas
Taking the living place of the patient in the data as the circle center, and taking the infection inner diameter r as the radius to form a circular area: s=pi r 2 This area is the patient affected area.
1-2-2) construction of street infection Rate model
Investigation of the patient affected area as a subject has found that the risk of infection with new coronavirus in the area within 1 meter of the place where the infected person resides is reduced by a factor of 80%; the risk of infecting a new coronavirus in an area 1 meter or more from the place where the infected person resides is reduced by an index of 50%, so that a street infection rate model calculation formula (x is distance (unit: m), Y is infection rate) is obtained as follows:
1-2-3) distance-infection rate simulation curves were plotted
As shown in fig. 3, a distance-infection rate simulation curve is drawn with the distance as the x-axis and the infection rate as the y-axis.
1-3) establishing a road infection probability database according to the data obtained by the distance-infection rate simulation curve, wherein if the influence areas of the patients coincide, the influence values are accumulated.
2) And constructing a novel infection model SEIRK based on the SEIR model according to the acquired data, wherein the specific flow is shown in figure 4.
2-1) determining model considerations
2-1-1) S-a susceptible person, i.e., a population susceptible to infection but not yet suffering from disease;
2-1-2) E-viral latency carrier, the latency being the group of people with disease latency who are ill but asymptomatic, the novel coronavirus also being infectious during latency;
2-1-3) I-the infected person, the infected person is the infected person;
2-1-4) R is a rehabilitating person, the rehabilitating person heals the patient, and the model considers that the rehabilitating person cannot be infected again;
2-1-5) K, which is a resistent, the resistent is the number of people wearing the mask in the crowd, and the model considers that the wearing of the mask in epidemic situation can inhibit the spread of virus to a certain extent, thereby effectively reducing the risk of infecting new coronavirus.
2-2) according to the consideration, the virus diffusion simulation is carried out by taking time t as a variable, and the formula is calculated as follows:
2-2-1) counting the number of susceptible people on day t
2-2-2) counting the number of people with latent form on day t
2-2-3) counting the number of infected persons on day t
2-2-4) calculating the number of rehabilitative persons on day t
2-2-5) calculating the number of preventable persons on day t
Wherein S (t) is the number of susceptible persons on the t th day, R (t) is the number of recovered persons on the t th day, I (t) is the number of infected persons on the t th day, E (t) is the number of latent persons on the t th day, R is the number of persons contacting the patient when no strict measures are taken in the initial stage, R2 is the number of persons contacting the patient when strict measures are taken after epidemic situation occurs, b is the infection probability when no strict measures are taken in the initial stage, b2 is the infection probability when strict measures are taken after epidemic situation occurs, N is the current time pedestrian flow obtained according to a street pedestrian flow model, y is the recovery probability, c is the probability of having resistant persons in the crowd (i.e. the probability of wearing a mask in the crowd), and a specific simulation curve is shown in FIG. 5.
3) The street pedestrian traffic model is constructed according to the acquired data, as shown in fig. 6, and the specific steps are as follows:
3-1) obtaining street traffic
3-1-1) acquiring the number of user locations
Firstly dividing a street into n areas, and recording the range O of each area i (i=1, 2, …, n), after the user allows to obtain his longitude and latitude coordinates, looking at which area the user coordinates are in at this time, so as to obtain the people flow data of each area of the street;
3-1-2) directly obtaining the street traffic:
and transmitting the shot video pictures to a server by using the existing cameras of each traffic road section, obtaining the number of pedestrians through a set of embedded intelligent video screen analysis algorithm, and carrying out density conversion according to the number of pedestrians obtained through recognition.
The number of people conversion formula is as follows:
N＝m*c
m＝p/m 0
wherein N is the total population number obtained by conversion, m is the number of people in a unit length (pedestrian density), p is the average number of people which can be identified by all cameras entering the current street, and m 0 The street length taken by the camera is c is the real street length, and finally the people flow number is calculated;
3-2) correcting the people flow data with a linear least squares method
3-2-1) software and hardware data combining
The people flow data obtained by software and the people flow data obtained by hardware are combined into a group of two-dimensional data (x t ,y t )， t＝1,2,…,l，x t The time t is represented, the people flow data counted by the user positioning is obtained by software, and x is t Different from each other, y t The time t is represented by the people flow data acquired by the hardware equipment, and the time l is represented by the time of acquiring the last group of data; the least square method is applied to obtain a function y=f (x) so that the sum of the square difference of the least square method result and the actual hardware obtained flow of people in the recorded momentMinimum;
3-2-2) the people flow data x obtained by the software at the time t+1 t+1 Substituting f (x), y t+1 Acquiring people flow data for the corrected software;
3-3) time series prediction
The people flow of each street is time, the people flow is predicted by adopting a 'simple sequence time average method' in a time sequence prediction method, and the calculation steps are as follows:
3-3-1) collecting time series sample data a at l' times daily over m days i′j′ I' represents a date number,i ' =1, 2, & m, j ' represents the time sequence number, j ' =1, 2, & l, and a is retrieved after correction i′j′ ；
3-3-2) calculating the arithmetic mean of the m days l' time instants
3-3-3) predictive computation
When the time series is listed by time, the people flow predictive value y at the j-th time of the predictive date m+1 day m+1 The method comprises the following steps:
3-4) using the obtained people flow data after the hardware acquired people flow and the software system acquired people flow are processed by the least square method as the result of the people flow model.
4) Constructing a novel infectious disease travel transmission according to a road infectious disease probability database and a comprehensive infectious disease model SEIRK and a street pedestrian flow model
As shown in fig. 7, the probability calculation model specifically includes the following steps:
4-1) determination of model parameters
W: acquiring the safety distance between people according to the investigation;
x: pedestrian position is based on the nearest patient residence distance;
t: predicting the occurrence time of the infectious disease on the same day;
pp: acquiring road infection probability data from the travel infection probability database;
i (t): the number of people infected on the t-th day obtained from the new infectious disease model SEIRK;
k (t): preventable persons on day t obtained from the new infectious disease model SEIRK;
e (t): the number of people with latent people on the t-th day obtained from a new infectious disease model SEIRK;
n: and obtaining the pedestrian traffic at the current moment according to the street pedestrian traffic model.
4-2) dynamic and static combination calculation of the probability of travel infection of novel coronaviruses
If x > w, the calculation formula is as follows:
P＝(0.5*(2-w)+1)*P′
otherwise, the calculation formula is as follows:
preferably, in step three, r=20, b=0.03, a=0.1, r2=30, b2=0.03, y=0.1.
Preferably, at step tetran=20, the probability of infection with infectious disease is highest.
Preferably, step five sets w to 2 meters.
The previous description of the embodiments is provided to facilitate a person of ordinary skill in the art in order to make and use the present invention. It will be apparent to those skilled in the art that various modifications can be readily made to these embodiments and the generic principles described herein may be applied to other embodiments without the use of the inventive faculty. Therefore, the present invention is not limited to the above-described embodiments, and those skilled in the art, based on the present disclosure, should make improvements and modifications without departing from the scope of the present invention.
Claims (3)
1. The travel infection probability calculation method suitable for the novel coronavirus comprises the following steps of:
1) Establishing a street infection probability database of the selected area;
2) Constructing a novel infectious disease model SEIRK based on the SEIR model according to the acquired data to simulate the infectious process of the novel infectious disease;
the step 2) comprises the following specific steps:
2-1) determining model considerations:
2-1-1) S-a susceptible person, i.e., a population susceptible to infection but not yet suffering from disease;
2-1-2) E-viral latency carrier, the latency being the group of people with disease latency who are ill but asymptomatic, the novel coronavirus also being infectious during latency;
2-1-3) I-the infected person, the infected person is the infected person;
2-1-4) R is a rehabilitating person, the rehabilitating person heals the patient, and the model considers that the rehabilitating person cannot be infected again;
2-1-5) K, which is a resistent, namely the number of people wearing the mask in the crowd, and the model considers that the wearing of the mask in epidemic situation can inhibit the spread of viruses to a certain extent, thereby effectively reducing the risk of infecting new coronaviruses;
2-2) according to the consideration, the virus diffusion simulation is carried out by taking time t as a variable, and the formula is calculated as follows:
2-2-1) counting the number of susceptible people on day t
2-2-2) counting the number of people with latent form on day t
2-2-3) counting the number of infected persons on day t
2-2-4) calculating the number of rehabilitative persons on day t
2-2-5) calculating the number of preventable persons on day t
Wherein S (t) is the number of susceptible persons on the t th day, R (t) is the number of recovered persons on the t th day, I (t) is the number of infected persons on the t th day, E (t) is the number of latent persons on the t th day, R is the number of persons contacting the patient when no strict measures are taken in the initial stage, R2 is the number of persons contacting the patient when strict measures are taken after epidemic situation occurs, b is the infection probability when no strict measures are taken in the initial stage, b2 is the infection probability when strict measures are taken after epidemic situation occurs, N is the current time pedestrian flow obtained according to a street pedestrian flow model, y is the recovery probability, and c is the probability of persons with resistance in the crowd, namely the probability of persons wearing masks in the crowd;
3) Constructing a street pedestrian flow model according to the acquired data;
4) According to the travel infection probability database, a comprehensive infection model SEIRK and a street pedestrian flow model are combined to construct a novel infectious disease travel infection probability calculation model;
the specific steps of the step 4) are as follows:
4-1) determination of model parameters
W: acquiring the safety distance between people according to the investigation;
x: pedestrian position is based on the nearest patient residence distance;
t: predicting the occurrence time of the infectious disease on the same day;
pp: acquiring road infection probability data from the travel infection probability database;
i (t): the number of people infected on the t-th day obtained from the new infectious disease model SEIRK;
k (t): the number of preventable persons on the t-th day obtained from the new infectious disease model SEIRK;
e (t): the number of people with latent people on the t-th day obtained from a new infectious disease model SEIRK;
n: acquiring pedestrian traffic at the current moment according to the street pedestrian traffic model;
4-2) dynamic and static combination calculation of the probability of travel infection of novel coronaviruses
If x > w, the calculation formula is as follows:
P＝(0.5*(2-w)+1)*P′
otherwise, the calculation formula is as follows:
2. the method for calculating the traveling infection probability applicable to the novel coronavirus according to claim 1, wherein the specific steps of the step 1) are as follows:
1-1) survey the number of patients in the region to be surveyed and the geographical position of the residence thereof according to the time line;
1-2) drawing a distance-infection rate simulation curve according to the data obtained by investigation;
1-2-1) acquisition of patient affected areas
Taking the living place of the patient in the data as the circle center, and taking the infection inner diameter r as the radius to form a circular area: s=pi r 2 The region is the affected region of the patient;
1-2-2) construction of street infection Rate model
Investigation of the patient affected area as a subject has found that the risk of infection with new coronavirus in the area within 1 meter of the place where the infected person resides is reduced by a factor of 80%; the risk of infection with new coronavirus in the area 1 m or more from the place where the infected person resides is reduced by 50% by index, so that a calculation formula of a street infection rate model (x is distance (unit: m), and Y is infection rate) is obtained as follows:
1-2-3) distance-infection rate simulation curves were plotted
Drawing a distance-infection rate simulation curve by taking the distance as an x axis and the infection rate as a y axis;
1-3) establishing a road infection probability database according to the data obtained by the distance-infection rate simulation curve, wherein if the influence areas of the patients coincide, the influence values are accumulated.
3. The method for calculating the traveling infection probability applicable to the novel coronavirus according to claim 1, wherein the step 3) specifically comprises the following steps:
3-1) obtaining street traffic
3-1-1) acquiring the number of user locations
Firstly dividing a street into n areas, and recording the range O of each area i I=1, 2, …, n, after the user allows to obtain the longitude and latitude coordinates of the user, checking which area the user coordinates are in at the moment, and obtaining the people flow data of each area of the street;
3-1-2) hardware to obtain street traffic
Transmitting the shot video pictures to a server by using the existing cameras of each traffic road section, obtaining the number of pedestrians through a set of embedded intelligent video screen analysis algorithm, and carrying out density conversion according to the number of pedestrians obtained through recognition;
the density conversion formula is as follows:
N＝m*c
m＝p/m 0
wherein N is the total population number obtained by conversion, m is the number of pedestrians in unit length, namely the density of pedestrians, p is the average number of identifiable persons entering all cameras on the current street, and m 0 The street length taken by the camera is c is the real street length, and finally the people flow number is calculated;
3-2) correcting the people flow data with a linear least squares method
3-2-1) software and hardware data combining
The people flow data obtained by software and the people flow data obtained by hardware are combined into a group of two-dimensional data (x t ,y t )，t＝1,2,…,l，x i The time t is represented, the people flow data counted by the user positioning is obtained by software, and x is t Different from each other, y t The time t is represented by the people flow data acquired by the hardware equipment, and the time l is represented by the time of acquiring the last group of data; using least square method energyObtaining a function y=f (x) to enable the sum of the least square method result and the square difference of the flow of people obtained by the actual hardware in the recorded momentMinimum;
3-2-2) the people flow data x obtained by the software at the time t+1 t+1 Substituting f (x), y t+1 Acquiring people flow data for the corrected software;
3-3) time series prediction
The people flow of each street is time, the people flow is predicted by adopting a 'simple sequence time average method' in a time sequence prediction method, and the calculation steps are as follows:
3-3-1) collecting time series sample data a at l' times daily over m days i′j′ Where i 'denotes the date number, i' =1, 2, …, m, j 'denotes the time number, j' =1, 2, …, l, and a is recovered after correction i′j′ ；
3-3-2) calculating the arithmetic mean of the m days l' time instants
3-3-3) predictive computation
When the time series is listed by time, the people flow predictive value y at the j-th time of the predictive date m+1 day m+1,j The method comprises the following steps:
3-4) using the obtained people flow data after the hardware acquired people flow and the software system acquired people flow are processed by the least square method as the result of the people flow model.
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