Novel coronavirus transmission risk assessment method and device, electronic equipment and medium

Abstract

The invention belongs to the technical field of virus propagation analysis, and discloses a novel coronavirus propagation risk assessment method and device, electronic equipment and a medium. Pathogen characteristic parameters, cabin environment parameters and passenger behavior matrix data are acquired; on the basis of the pathogen characteristic parameters, calculating the virus discharge amount discharged by the indication case in the cabin through breathing or speaking in the flight taking process; simulating a first infection risk value of the passenger under the influence of an aerosol transmission path of a first preset distance and a second infection risk value of the passenger under the influence of an aerosol transmission path of a second preset distance by using the cabin environment parameters and the virus discharge amount through a fluid mechanics method; simulating a third infection risk value of the passenger under the influence of the contact propagation path through a Markov chain model by using the passenger behavior matrix data; and the comprehensive infection risk value of the passenger is estimated by integrating the infection risk values. According to the invention, comprehensive risk assessment is carried out by integrating a multi-path propagation mechanism, so that a more accurate assessment result is obtained.

Description

Technical Field

[0001] The present application relates to the technical field of virus transmission analysis, and in particular to a novel coronavirus transmission risk assessment method, device, electronic equipment and medium. Background Art

[0002] The popularity of air travel has, to a certain extent, accelerated the spread of COVID-19 around the world, thereby expanding the scope of the epidemic and posing a serious threat to global public health security.

[0003] Aircraft cabins are high-risk environments for the spread of respiratory infectious diseases. Studying the multi-pathway transmission of the new coronavirus in passenger cabins is of great significance for preventing and controlling the cross-regional spread of the virus. Traditional epidemiological survey methods are mainly observational studies, relying on post-event interviews and video surveys to determine the transmission route by the distance between the susceptible person and the source of infection. This method is not only susceptible to recall bias, but also lacks rigor and accuracy in the collected data. In contrast, mathematical model methods can systematically analyze the transmission pathways and mechanisms by establishing precise mathematical formulas and assumptions, avoiding the limitations of relying on subjective memories and environmental conditions. Among the commonly used mathematical models, there are many models such as fluid mechanics calculus methods, Markov chains, and computational fluid dynamics (CFD). However, the assumptions of the above models are often oversimplified and difficult to apply directly to the complex environment of the cabin environment. In addition, the existing models simplify the assumptions of architectural environmental elements and human behavior, or make assumptions about the environment's behavior of touching surfaces and close contact with people to achieve a simplified model, which brings uncertainty to the calculation results. These models fail to fully consider the significant impact of close contact and surface touching behaviors among humans in the cabin on the risk of infection for susceptible populations, and therefore cannot accurately estimate the actual probability of infection in the cabin. Summary of the invention

[0004] To this end, the embodiments of the present application provide a method, device, electronic device and medium for assessing the transmission risk of the new coronavirus, which is based on the real behavioral data of passengers in the cabin, integrates multi-path transmission mechanisms, and provides a comprehensive transmission risk assessment through the joint application of CFD and mathematical models. It can obtain more accurate assessment results and solve the limitations of the traditional model's single transmission path.

[0005] In a first aspect, the present application provides a method for assessing the transmission risk of the new coronavirus.

[0006] This application is achieved through the following technical solutions:

[0007] A novel coronavirus transmission risk assessment method, comprising: obtaining pathogen characteristic parameters, cabin environment parameters, and passenger behavior matrix data, wherein the passenger behavior matrix data is contact behavior data between passengers and environmental surfaces in the cabin extracted from multiple flight simulation experiments conducted in a cabin simulator environment;

[0008] Based on the pathogen characteristic parameters, calculate the amount of virus excreted by the index case in the cabin through breathing or speaking during the flight;

[0009] Using the cabin environmental parameters and the virus discharge, a fluid mechanics method is used to simulate a first infection risk value of a passenger under the influence of an aerosol transmission pathway at a first preset distance in the cabin;

[0010] Using the cabin environment parameters and the virus discharge, a fluid mechanics method is used to simulate a second infection risk value of the passenger under the influence of an aerosol transmission pathway at a second preset distance in the cabin;

[0011] Using the passenger behavior matrix data, a Markov chain model is used to simulate the third infection risk value of passengers under the influence of contact transmission pathways in the cabin;

[0012] The first infection risk value, the second infection risk value and the third infection risk value are combined to estimate the comprehensive infection risk value of the passenger.

[0013] In a preferred example of the present application, it can be further set that the passenger behavior matrix data is represented as:

[0014]

[0015] Among them, N s is the number of environmental surfaces, N p is the number of passengers, ps(k) is the coefficient of passengers touching the environmental surface at time k, ps j,i (k) = 1 means that passenger i touches environmental surface j at time k, ps j,i (k)=0 means that passenger i did not touch environmental surface j at time point k.

[0016] In a preferred example of the present application, it can be further configured that, based on the pathogen characteristic parameters, the amount of virus discharged by the index case in the cabin through breathing or speaking during the flight is calculated, including:

[0017] Based on the pathogen characteristic parameters, the droplet concentration distribution data of the virus in the cabin is calculated using a bronchial-larynx-oral trimodal model;

[0018] Based on the droplet concentration distribution data and the median gene copy number of the virus, the amount of virus excreted by the index case in the cabin through breathing or speaking during the flight was calculated.

[0019] In a preferred example of the present application, it can be further set that, based on the pathogen characteristic parameters, the calculation formula for the droplet concentration distribution data of the virus in the cabin is calculated using the bronchial-larynx-oral trimodal model as follows:

[0020]

[0021] Among them, f(d 0 ) represents the droplet concentration distribution data, d 0 represents the droplet size, CMD z , GSD z are the model parameters of the trachea-larynx-oral cavity tri-modal model.

[0022] In a preferred example of the present application, it can be further configured to simulate the first infection risk value of the passenger under the influence of the aerosol transmission path of the first preset distance in the cabin by a fluid mechanics method, including:

[0023] Import the cabin environment parameters and the continuous fluid region of the human body into the ICEM CFD software to create a discrete grid for numerical calculation;

[0024] Import the discrete grid into Ansys Fluent software and convert the Navier-Stokes partial differential equations of fluid flow into a discretized algebraic equation system;

[0025] The closed control equation of the SST k-ω turbulence model is used to solve the algebraic equations and calculate the first exposure dose of aerosol transmission of passenger i at the first preset distance.

[0026] Based on the first exposure dose The first infection risk value of passenger i affected by aerosol transmission at a first preset distance in the cabin is estimated by a negative exponential dose-response model.

[0027] In a preferred example of the present application, it can be further set that the first exposure dose The calculation formula is:

[0028]

[0029] in, represents the first exposure dose of passenger i affected by aerosol transmission at the first preset distance, V is the cabin volume, ACH is the cabin air exchange rate, χ ais the virus inactivation rate, χ d is the deposition rate of virus-containing droplets, n(d 0 ,t) is the released particle size d 0 The amount of virus carried by the droplet at time t, R i is the steady-state CO in the mouth area of ​​passenger i 2 Concentration and cabin CO 2 concentration ratio, IR is the inhalation rate, E(d r ) indicates the diameter is d r The deposition efficiency of droplets in the respiratory tract, is the filtration efficiency of the mask, An indicator indicating whether the i-th passenger wears a mask.

[0030] In a preferred example of the present application, it can be further configured to use a fluid mechanics method to simulate the second infection risk value of passengers under the influence of aerosol transmission pathways at a second preset distance in the cabin, including:

[0031] Import the cabin environment parameters into the CFD software to build the cabin geometry model;

[0032] Based on the different body positions and facial orientations of the passengers, the amount of virus ingested by each passenger through inhalation and the amount of virus ingested through deposition were simulated;

[0033] Based on the amount of virus ingested by inhalation and the amount of virus ingested by deposition, a negative exponential dose-response model is used to estimate the second infection risk value of the passenger under the influence of aerosol transmission at a second preset distance in the cabin.

[0034] In a second aspect, the present application provides a device for assessing the transmission risk of the new coronavirus.

[0035] The present application is implemented through the following technical solution: a novel coronavirus transmission risk assessment device, used to execute the method described in the first aspect above, comprising:

[0036] a data acquisition module, for acquiring pathogen characteristic parameters, cabin environment parameters and passenger behavior matrix data, wherein the passenger behavior matrix data is contact behavior data between passengers and environmental surfaces in the cabin extracted from multiple flight simulation experiments conducted in a cabin simulator environment; and for calculating the amount of virus discharged by the index case in the cabin through breathing or speaking during the flight based on the pathogen characteristic parameters;

[0037] A first transmission assessment module is used to simulate a first infection risk value of a passenger under the influence of an aerosol transmission pathway of a first preset distance in the cabin by using the cabin environment parameters and the virus discharge through a fluid mechanics method;

[0038] A second transmission assessment module is used to simulate a second infection risk value of a passenger under the influence of an aerosol transmission pathway at a second preset distance in the cabin by a fluid mechanics method using the cabin environment parameters and the virus discharge;

[0039] A third transmission assessment module is used to use the passenger behavior matrix data to simulate the third infection risk value of the passenger under the influence of the contact transmission route in the cabin through a Markov chain model;

[0040] The risk assessment module is used to estimate the comprehensive infection risk value of the passenger by combining the first infection risk value, the second infection risk value and the third infection risk value.

[0041] In a third aspect, the present application provides an electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor;

[0042] In which, the memory stores computer program instructions that can be executed by the at least one processor, and the computer program instructions are executed by the at least one processor so that the at least one processor performs the steps of the new coronavirus transmission risk assessment method described in the first aspect.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium.

[0044] This application is achieved through the following technical solutions:

[0045] A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of any one of the above-mentioned methods for assessing the risk of transmission of the novel coronavirus.

[0046] In summary, compared with the prior art, the technical solution provided in the embodiment of the present application has at least the following beneficial effects:

[0047] This application breaks through the simplified assumptions of traditional models, uses the real behavior data of passengers in the cabin, and directly applies these data to the construction of the transmission model, ensuring that the simulation results are closer to the actual situation, greatly improving the accuracy and practical significance of the application; the application of computational fluid dynamics to accurately simulate the complex airflow field in the cabin can truly reflect the aerosol diffusion path in aerodynamics. Through CFD modeling, the diffusion and deposition of virus particles can be accurately calculated in specific physical scenarios, and the actual dynamic situation of air transmission can be comprehensively analyzed; it combines multiple transmission pathways such as aerosol transmission, droplet transmission and contact transmission, and through the joint use of CFD and mathematical models, it provides a full range of transmission risk assessments, solving the limitations of the traditional model's single transmission pathway. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of a flow chart of a novel coronavirus transmission risk assessment method provided in one embodiment of the present application;

[0049] Figure 2 A distribution diagram of passenger seats within a short distance in the cabin of CA868 provided in one embodiment of the present application;

[0050] Figure 3 A schematic diagram of a sub-community in a cabin provided in an embodiment of the present application;

[0051] Figure 4 A schematic diagram of the structure of a novel coronavirus transmission risk assessment device provided in one embodiment of the present application;

[0052] Description of reference numerals:

[0053] Data acquisition module-100, first propagation assessment module-200, second propagation assessment module-300, third propagation assessment module-400, risk assessment module-500. DETAILED DESCRIPTION

[0054] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] In addition, the term "and / or" in this application is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this application, unless otherwise specified, generally indicates that the associated objects before and after are in an "or" relationship.

[0057] In this application, the terms "first", "second", etc. are used to distinguish identical or similar items with basically the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there any limitation on quantity and execution order.

[0058] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Figure 1 As shown, a novel coronavirus transmission risk assessment method proposed in this application comprises the following steps:

[0059] S10: Obtain pathogen characteristic parameters, cabin environment parameters and passenger behavior matrix data, where the passenger behavior matrix data is the contact behavior data between passengers and environmental surfaces in the cabin extracted from multiple flight simulation experiments conducted in a cabin simulator environment.

[0060] Specifically, the passenger behavior matrix data is expressed as:

[0061]

[0062] Among them, N s is the number of environmental surfaces, N p is the number of passengers, ps(k) is the coefficient of passengers touching the environmental surface at time k, ps j,i (k) = 1 means that passenger i touches environmental surface j at time k, ps j,i (k)=0 means that passenger i did not touch environmental surface j at time point k.

[0063] S20: Based on the pathogen characteristic parameters, calculate the amount of virus discharged by the index case in the cabin through breathing or speaking during the flight.

[0064] The index case refers to a case in which a nucleic acid test was conducted just after the flight arrived at the destination, and the test result was positive and showed high homology with the SARS-CoV-2 strains of many other passengers.

[0065] Specifically, based on the characteristic parameters of the pathogen, the bronchial-laryngeal-oral trimodal model was used to calculate the droplet concentration distribution data of the virus in the cabin; based on the droplet concentration distribution data and the median gene copy number of the virus, the amount of virus discharged by the index case in the cabin through breathing or speaking during the flight was calculated.

[0066] Among them, based on the characteristic parameters of the pathogen, the calculation formula of the droplet concentration distribution data of the virus in the computer cabin is expressed as follows using the bronchiolar-laryngeal-oral tri-modal model (BLO):

[0067]

[0068] In the formula, f(d 0 ) represents the droplet concentration distribution data, d 0 represents the droplet size, CMD z, GSD z are the model parameters of the trachea-larynx-oral cavity tri-modal model.

[0069] It should be noted that the BLO model divides the droplet generation process into three modes, corresponding to different parts of the respiratory tract: the lower respiratory tract, the larynx and the upper respiratory tract; the droplets generated during normal breathing belong to the bronchial liquid film burst mode (also called the B mode), the laryngeal mode (also called the L mode) is most active during conversation, and the oral mode (also called the O mode) is also more active during conversation. Therefore, when the index case is only breathing, a separate B model is used, which means that when breathing, only the results of the B mode need to be calculated; when the index case is speaking, the complete BLO model is used, which means that the values ​​of the B mode, L mode, and O mode need to be calculated and accumulated. In the BLO model, when z is 1, it corresponds to the B mode, and the model parameter is 0.054, CMD z (μm) is 1.64, GSD z is 1.3; when z is 2, it corresponds to L mode, and the model parameters 0.0684, CMD z (μm) is 2.4, GSD z is 1.66; when z is 3, it corresponds to the O mode, and the model parameters 0.00126, CMD z (μm) is 144.67, GSD z It is 1.795.

[0070] After calculating the droplet concentration distribution data, a single exhaled droplet is regarded as a sphere, and the volume of a single exhaled droplet is During the flight, the amount of virus excreted by the index case through breathing or speaking at time t (d 0 ,t) is calculated as:

[0071]

[0072] According to the calibration model of Ct value (Cycle threshold) and gene copy number, the viral gene copy number L a Satisfies the following formula:

[0073]

[0074] Median gene copy number of virus in exhaled droplets of index case L a 2.73×10 7 mRNA copies / mL (minimum value is 3.98×10 3 mRNA copies / mL, the maximum value is 2.09×1011 mRNA copies / mL).

[0075] S30: Using cabin environmental parameters and virus emission, a fluid mechanics method is used to simulate the first infection risk value of passengers under the influence of aerosol transmission through a first preset distance in the cabin.

[0076] Among them, the aerosol transmission pathway of the first preset distance is called the long-distance aerosol transmission pathway, which usually refers to a distance far away from the source of infection (greater than 1.5 meters). Droplets or aerosols containing the virus (diameter <10μm) are suspended in the air for a long time, and susceptible people are infected with the virus by inhaling these aerosols.

[0077] The specific steps of simulating and calculating the first infection risk value of the passenger under the influence of the aerosol transmission pathway at the first preset distance in the cabin are:

[0078] S301: Import the cabin environment parameters and the continuous fluid region of the human body into the ICEM CFD software to create a discrete grid for numerical calculation.

[0079] S302: Import the discrete grid into Ansys Fluent software and convert the Navier-Stokes partial differential equations of fluid flow into a discretized set of algebraic equations.

[0080] Ansys Fluent software is used to model and analyze the processes of fluid flow, heat transfer and mass exchange in the cabin environment. The Navier-Stokes partial differential equations describing the fluid flow are converted into discretized algebraic equations and solved.

[0081] S303: Using the closed control equation of the SST k-ω turbulence model, the algebraic equations are solved to calculate the first exposure dose of passenger i affected by aerosol transmission at the first preset distance. Considering that the indoor environment is often in a turbulent state, the closed control equation of the SST k-ω turbulence model is used. This turbulence model has wide applicability and high numerical stability, which is conducive to improving the accuracy of calculating exposure dose.

[0082] S304: Based on the first exposure dose The first infection risk value of passengers affected by aerosol transmission at a first preset distance in the cabin is estimated by a negative exponential dose-response model.

[0083] Among them, this application introduces tracer gas carbon dioxide (CO 2 ) is used as a substitute for simulating virus transmission to simulate the mixture of virus and air exhaled by the index case. On this basis, the steady-state CO in the mouth area of ​​each susceptible passenger is defined in a steady state.2 Concentration C seatm,i With the entire cabin CO 2 Concentration C cabin,i The ratio R i :

[0084]

[0085] Then the virus concentration C in the mouth area of ​​each passenger is i virus,t satisfy:

[0086]

[0087] Among them, C virus,t is the instantaneous concentration of the virus released by the index case at time t, obtained from the macroscopic mass balance equation:

[0088]

[0089] In equilibrium:

[0090] In summary, the first exposure dose The calculation formula can be expressed as:

[0091]

[0092] in, represents the first exposure dose of passenger i affected by aerosol transmission at the first preset distance, V is the cabin volume, ACH is the cabin air exchange rate, χ a is the virus inactivation rate, χ d is the deposition rate of virus-containing droplets, n(d 0 ,t) is the released particle size d 0 The amount of virus carried by the droplet at time t, R i is the steady-state CO in the mouth area of ​​passenger i 2 Concentration and cabin CO 2 concentration ratio, IR is the inhalation rate, E(d r ) indicates the diameter is d r The deposition efficiency of droplets in the respiratory tract, is the filtration efficiency of the mask, The indicator indicating whether the i-th passenger is wearing a mask is 1 if wearing a mask, otherwise it is 0. It should also be noted that, considering that after evaporation, the activity of the virus in the droplets drops sharply to one-fourth of the initial value, and then slowly decreases, so it is assumed in the formula that the final concentration of live viruses in the droplets is one-fourth of the initial concentration.

[0093] The first exposure dose is calculated The first infection risk value of passengers affected by aerosol transmission at the first preset distance in the cabin is estimated by a negative exponential dose-response model:

[0094] where η r The respiratory tract of susceptible people has a value of 0.246, D a This is the first exposure dose ingested by susceptible passengers through the aerosol route.

[0095] S40: Using cabin environmental parameters and virus emission, a fluid mechanics method is used to simulate the second infection risk value of passengers under the influence of aerosol transmission through a second preset distance in the cabin.

[0096] The second preset distance aerosol transmission pathway is also called the close-range transmission pathway, which occurs in areas close to the source of infection (within 1.5 meters) and includes two sub-pathways. The virus-containing droplets discharged by the index case are inhaled by susceptible persons, causing the susceptible persons to be infected. This pathway is also called the close-range air transmission pathway or the close-range aerosol transmission pathway; the virus-containing droplets discharged by the index case are directly deposited on the mucous membranes of susceptible persons (such as the eye, nose, and mouth mucosa), causing the susceptible persons to be infected. This sub-pathway is called the droplet transmission pathway or the large droplet transmission pathway.

[0097] The specific execution steps for simulating and calculating the second infection risk value of passengers under the influence of the aerosol transmission pathway at the second preset distance in the cabin are:

[0098] S401: Importing cabin environment parameters into CFD software to construct a geometric model of the cabin;

[0099] S402: simulating the amount of virus ingested by each passenger through inhalation and the amount of virus ingested by each passenger through deposition according to different body positions and facial orientations of the passengers;

[0100] S403: Based on the amount of virus ingested by inhalation and the amount of virus ingested by deposition, a negative exponential dose-response model is used to estimate the second infection risk value of the passenger under the influence of aerosol transmission at a second preset distance in the cabin.

[0101] It should be noted that in the close-range transmission pathway (referring to 1.5m), due to the different gravity and evaporation rates, the droplet size affects its attenuation in the droplet transmission pathway. In this application, droplets with a diameter of 0-10μm discharged from the index case are regarded as fine droplets (fa), and droplets of 10-150μm are regarded as large droplets (ld).

[0102] Considering the different body positions and facial orientations, the virus-containing droplets attenuate at different levels at close range. In this application, there are three body positions of susceptible passengers: the susceptible passenger is on the side of the index case, the susceptible passenger is on the back of the index case, and the index case and the susceptible passenger are facing each other. Figure 2 As shown, assuming that the center point of the cabin seat is the position of the passenger's mouth, the distance between all susceptible passengers within the close range of the index case (1.5m) and the distance between the susceptible passengers and the index case, assuming that the passengers are always facing forward in the aircraft cabin ( Figure 2 The circle on the middle seat represents the passenger, and the solid square above the circle represents the person's facial orientation), among which the red person in the center is the index case 51C. When the susceptible passenger is within 90° on both sides of the index case (blue persons 50A, 51A, 51B, 52A, 50D, 50E, 51D and 51E in the figure), the susceptible passenger is considered to be on the side of the index case; if the susceptible passenger is within 90° behind the index case (purple persons 52B and 52C in the figure), the susceptible passenger is considered to be on the back of the index case; if the susceptible person is 90° in front of the index case and the susceptible person and the index case sit in the same direction, the index case and the susceptible passenger are considered to be facing each other.

[0103] Within a close range, the amount of virus ingested by each susceptible passenger through inhalation and deposition can be recorded as D in (d,s) and D de (d,s):

[0104] D in (d,s)=g Ta,in (d)L a η s,Ta,in (d)P Ta η fo,Ta,in (d,s)

[0105] +g Br,in L a (d)η s,Br,in (d)(1-P Ta )η fo,Br,in (d,s),

[0106] D de (d,s)=g Ta,de (d)L a η s,Ta,de (d)P Ta η fo,Ta,de (d,s)

[0107] +g Br,de L a (d)η s,Br,de (d)(1-P Ta)η fo,Br,de (d,s),

[0108] Among them, the droplets containing the virus have different distance attenuation degrees in the air due to the different activities of the index case in discharging droplets, the different distance attenuation coefficients of small droplets and large droplets, and the different ways of being ingested by susceptible passengers (inhalation or deposition). Therefore, the distance attenuation coefficients of the droplets generated by talking and breathing when inhaled and deposited are recorded as: η s,Ta,in (d), η s,Br,in (d), η s,Ta,de (d), η s,Br,de (d).

[0109] The attenuation coefficients of the body position and face orientation of the index case's small droplets produced by breathing and talking are also different from those of large droplets. The attenuation coefficients of the body position and face orientation during breathing are η fo,Br (d,s), the attenuation coefficient of body position and facial orientation during conversation is η fo,Ta (d,s), which is also divided into two intake modes: inhalation or deposition.

[0110] d is the initial diameter of the droplet, s is the distance between the index case and the susceptible passenger in close range, P Ta is the conversation frequency, i.e. the ratio of the index case’s conversation time to the total flight time, which is generally assumed to be 20%; Ta (d) is the rate at which droplets are generated during the index case’s conversation, g Br (d) is the rate of droplets produced by the index case’s breathing, L a is the number of viral mRNA copies in the droplet.

[0111] The calculation formula for the second infection risk value is:

[0112]

[0113] Among them, D in , D de They are the total amount of virus ingested through the inhalation sub-pathway in the droplet pathway and the total amount of virus ingested through the deposition sub-pathway in the droplet pathway, respectively.

[0114] S50: Using the passenger behavior matrix data, a Markov chain model is used to simulate the third infection risk value of passengers under the influence of contact transmission in the cabin.

[0115] Among them, the passenger behavior matrix data is expressed as:

[0116]

[0117] Among them, N s is the number of environmental surfaces, N pis the number of passengers, ps(k) is the coefficient of passengers touching the environmental surface at time k, ps j,i (k) = 1 means that passenger i touches environmental surface j at time k, ps j,i (k)=0 means that passenger i did not touch environmental surface j at time point k.

[0118] definition is the virus concentration on environmental surface j at time k (TCID 50 or mRNA copies / m 2 ), is the virus concentration on passenger i’s hand at time k. After a time interval of ΔT, the virus concentration on the environmental surface j at a certain time k is and the virus concentration on the surface j of the environment k+1 at the next moment Satisfy between:

[0119]

[0120] Among them A hs is the contact area between the hand and the surrounding surface (m 2 ), the value is 0.0042m 2 , is the area of ​​the environmental surface j (m 2 ), the area of ​​different environmental surfaces is measured or estimated according to the actual situation. In this application, environmental surfaces are divided into porous surfaces (such as textiles) and non-porous surfaces (such as metals, plastics, etc.). The transfer efficiency and inactivation rate of viruses on the two surfaces are different: τ sh is the transfer efficiency of virus from environmental surfaces to hands, τ hs is the transfer efficiency of the virus from hands to environmental surfaces, b s is the inactivation rate of SARS-CoV-2 virus, is the concentration of the virus in the space near the surface of object j at time k, D ep is the sedimentation rate of virus-containing particles; is the volume of the space region adjacent to the surface of object j.

[0121] Define the vector pm i (k) is the behavior vector of passenger i touching his own mucous membrane. Similar to the Ps matrix, if passenger i touches his own mucous membrane at time point k, then pm i (k)=1, otherwise pm i (k) = 0. This study assumes that passengers only touch their mucous membranes and surrounding surfaces with the same finger of the same hand, and that when the virus is transferred from the hand to the mucous membrane, it is completely absorbed by the mucous membrane. Therefore, after a time interval of ΔT, the virus concentration on the hand of passenger i at a certain time (k) is and the virus concentration of passenger i at the next moment (k+1) satisfy:

[0122]

[0123] A h is the palm area (m 2 ), which is 0.0203m 2 , is the contact area between hand and mucous membrane (m 2 ), assuming 0.0001m 2 . b h is the inactivation rate of the virus on the hand, τ hm It is the transfer efficiency of the virus from hands to mucous membranes.

[0124] In the initial state (k = 0), the concentration of the virus on the environmental surface is 0. The total amount of virus ingested by passenger i through contact It can be written as:

[0125]

[0126] Among them, N t is the total time interval. The total flight time T is 12.8 hours.

[0127] S60: Estimate the comprehensive infection risk value of the passenger by combining the first infection risk value, the second infection risk value and the third infection risk value.

[0128] Specifically, the comprehensive infection risk value of passenger i is P t :

[0129]

[0130] Among them, P a represents the first infection risk value, P c Represents the second infection risk value, P f Represents the third infection risk value.

[0131] Specifically, this application uses input data based on passenger behavior data (including crew members) in the real cabin to simulate passenger behavior in the contact path. The passenger behavior data comes from five experiments conducted in the simulated cabin. The experiments were conducted in an aircraft cabin simulator environment. The simulator exactly imitated the front entrance area of ​​the Boeing 737 aircraft and the interior space of the economy class. In the five experiments, 48, 48, 48, 48 and 45 subjects were recruited respectively. They entered the aircraft cabin simulator as passengers for a five-hour simulated flight, and there were two dedicated crew members to provide services to the passengers, such as helping passengers place luggage, distributing drinks and meals. This application simulates that the aircraft model of CA868 flight is a large Boeing 777 with 9 columns per row, and the flight time is 12 hours and 48 minutes. The touch behavior data in the real experiment is extracted and randomized, and the passenger behavior matrix data in the cabin in accordance with the epidemic event conditions of this application is simulated.

[0132] Furthermore, based on the complex network theory, the touch process between the surfaces in the cabin and the hands of passengers is regarded as a "surface contact network" in this application; wherein each surface in the cabin and each passenger's hand are regarded as a "node", and the touch between the surface and the hand is regarded as an "edge", and the virus will spread in the surface touch network with the complex touch behavior of people in the cabin. It is also considered that the surface contact network in the cabin has the community nature, that is, the three passengers on the same side of the aisle and the environmental surfaces around the three passengers form a "sub-community", and the surfaces contained in the sub-community are closely connected due to frequent touch behaviors (assuming that the passengers in the cabin sit in their seats most of the time); sub-communities are connected through the surfaces inside the toilet due to the touch behavior that occurs when passengers go to the toilet. Based on the properties of the sub-communities in the cabin, the experimental data characteristics of the personnel in the real cabin are refined to obtain the touch frequency of passengers at different positions in the sub-community touching each surface in the sub-community, and the surface touch network in each sub-community is simulated 1000 times with the touch frequency per hour as the average. After simplifying the touch behavior of some passengers on board, the schematic diagram of some sub-communities of the surface contact network is as follows Figure 3 shown.

[0133] Three adjacent passengers are regarded as a subcommunity (for example, passengers 47A, 47B, and 47C form the 47ABC subcommunity), and the passenger touch behavior is simulated in subcommunities. There are 90 groups in the economy class. There are two types of subcommunities in the economy class: 60 window subcommunities and 30 middle subcommunities. The window subcommunity contains one aisle passenger, one middle passenger, and one window passenger (for example, the 47ABC subcommunity and the 47JKL subcommunity), and the middle subcommunity contains two aisle passengers and one middle passenger (for example, the 47DEH subcommunity). There are 28 inner surfaces in each subcommunity, including cups (CP), meal trays (MP), seat armrests (AR), cushions (S), seat backs (SB), service buttons (PSU), etc. used by passengers.

[0134] The surfaces of non-sub-communities include the inner surfaces of the toilets and the public surfaces in the front of the economy class. Among them, there are 5 toilets in the economy class, and each toilet has 11 surfaces, including toilet door handles, toilet mirrors, and toilet buttons. Since the touch data in the toilet cannot be obtained in the experiment, it is assumed that passengers touch in the toilet in a specific order that conforms to the logic of real-world personnel behavior. For example, a touch order of passengers in the toilet is: toilet door handle (TLD), toilet shelf (TLI), toilet seat (TLQ), toilet cover (TLP), toilet left side (TLCL), toilet right side (TLCR), toilet tissue box (TLT), toilet handrail (TLAR), toilet seat (TLQ), toilet cover (TLP), toilet button (TLB), toilet mirror (TLM), faucet (TLW), toilet tissue box (TLT), toilet door handle (TLD). In addition, there are 5 public surfaces in the front of the economy class, including door handles, door curtains, and stewardess trays. Therefore, there are 2520 (90×28) subcommunity surfaces and 60 (11×5+5) non-subcommunity surfaces in the entire cabin, for a total of 2580 surfaces.

[0135] The method proposed in this application overcomes the bias problem in traditional epidemiological surveys: traditional epidemiological surveys rely on the subjective recollections of infected people and are easily affected by memory bias and data inaccuracy; this application ensures the accuracy of the data by introducing real behavioral data, and solves the problem of insufficient data rigor in epidemiological studies. Solve scientific and ethical challenges in experimental research: Experimental research on high-risk viruses often faces the dual challenges of ethics and safety. Through mathematical modeling and CFD simulation, this technology can accurately predict the spread of the virus without relying on actual virus experiments, avoiding the ethical difficulties of direct experiments. Fill the gap in the applicability of existing mathematical models in complex environments: traditional mathematical models, especially differential equations and Markov chain models, are usually unable to accurately simulate complex cabin environments, and their assumptions are too simple. Through CFD technology combined with multi-path transmission mechanisms, this technology can better adapt to the special airflow environment in enclosed spaces, especially in complex scenarios such as passenger cabins, to provide more accurate risk predictions. Addressing the impact of close contact and surface contact in the cabin: Existing models do not adequately consider the impact of close contact and surface touching between passengers. This technology integrates behavioral data and comprehensively analyzes the potential impact of these behaviors on virus transmission, which can more accurately assess passenger infection risks and optimize prevention and control measures. Improve the ability to quantitatively analyze virus transmission risks: Traditional models have limited quantitative capabilities in complex multi-factor environments. This technology combines CFD models with mathematical models to quantitatively analyze the contribution of each transmission route to the risk of infection, providing a more sophisticated transmission dynamics analysis tool and effectively improving the accuracy of risk assessment.

[0136] Another embodiment of the present application also provides a novel coronavirus transmission risk assessment device for executing the above method, such as Figure 4 As shown, the device includes: a data acquisition module 100, a first propagation assessment module 200, a second propagation assessment module 300, a third propagation assessment module 400, and a risk assessment module 500;

[0137] The data acquisition module 100 is used to acquire pathogen characteristic parameters, cabin environment parameters and passenger behavior matrix data, wherein the passenger behavior matrix data is the contact behavior data between passengers and environmental surfaces in the cabin extracted through multiple flight simulation experiments in a cabin simulator environment; and is also used to calculate the amount of virus discharged by the index case in the cabin through breathing or speaking during the flight based on the pathogen characteristic parameters;

[0138] A first transmission assessment module 200 is used to simulate a first infection risk value of a passenger under the influence of an aerosol transmission pathway of a first preset distance in the cabin by using cabin environmental parameters and virus discharge through a fluid mechanics method;

[0139] A second transmission assessment module 300 is used to simulate a second infection risk value of a passenger under the influence of an aerosol transmission pathway of a second preset distance in the cabin by a fluid mechanics method using cabin environment parameters and the virus discharge;

[0140] The third transmission assessment module 400 is used to simulate the third infection risk value of the passenger under the influence of the contact transmission pathway in the cabin through a Markov chain model using the passenger behavior matrix data;

[0141] The risk assessment module 500 is used to estimate the comprehensive infection risk value of the passenger by integrating the first infection risk value, the second infection risk value and the third infection risk value.

[0142] The specific definition of the new coronavirus transmission risk assessment device provided in this embodiment can be found in the embodiment of the new coronavirus transmission risk assessment method above, which will not be repeated here. Each module in the above-mentioned new coronavirus transmission risk assessment device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0143] An embodiment of the present application provides an electronic device, which may include a processor, a memory, a network interface, and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the processor executes the steps of the method for assessing the risk of transmission of the new coronavirus as in any of the above embodiments.

[0144] The working process, working details and technical effects of the computer equipment provided in this embodiment can be found in the embodiment of the novel coronavirus transmission risk assessment method mentioned above, and will not be described in detail here.

[0145] The embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the novel coronavirus transmission risk assessment method as described in any of the above embodiments are implemented. The computer-readable storage medium refers to a carrier for storing data, which may include but is not limited to a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive, and / or a memory stick, etc. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0146] The working process, working details and technical effects of the computer-readable storage medium provided in this embodiment can be found in the embodiment of the novel coronavirus transmission risk assessment method described above, and will not be elaborated here.

[0147] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0148] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0149] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system described in the present application is divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A method for assessing the risk of transmission of a novel coronavirus, characterized in that: The method comprises: Acquiring pathogen characteristic parameters, cabin environment parameters, and passenger behavior matrix data, wherein the passenger behavior matrix data is contact behavior data between passengers and environmental surfaces in the cabin extracted from multiple flight simulation experiments conducted in a cabin simulator environment; Based on the pathogen characteristic parameters, calculate the amount of virus excreted by the index case in the cabin through breathing or speaking during the flight; Using the cabin environmental parameters and the virus discharge, a fluid mechanics method is used to simulate a first infection risk value of a passenger under the influence of an aerosol transmission pathway at a first preset distance in the cabin; Using the cabin environment parameters and the virus discharge, a fluid mechanics method is used to simulate a second infection risk value of the passenger under the influence of an aerosol transmission pathway at a second preset distance in the cabin; Using the passenger behavior matrix data, a Markov chain model is used to simulate the third infection risk value of passengers under the influence of contact transmission pathways in the cabin; The first infection risk value, the second infection risk value and the third infection risk value are combined to estimate the comprehensive infection risk value of the passenger.

2. The method for assessing the risk of transmission of a novel coronavirus according to claim 1, characterized in that: The passenger behavior matrix data is expressed as: Among them, N s is the number of environmental surfaces, N p is the number of passengers, ps(k) is the coefficient of passengers touching the environmental surface at time k, ps j,i (k) = 1 means that passenger i touches environmental surface j at time k, ps j,i (k)=0 means that passenger i did not touch environmental surface j at time point k.

3. The method for assessing the risk of transmission of a novel coronavirus according to claim 1, characterized in that: Based on the pathogen characteristic parameters, the amount of virus discharged by the index case in the cabin through breathing or speaking during the flight is calculated, including: Based on the pathogen characteristic parameters, the droplet concentration distribution data of the virus in the cabin is calculated using a bronchial-larynx-oral trimodal model; Based on the droplet concentration distribution data and the median gene copy number of the virus, the amount of virus excreted by the index case in the cabin through breathing or speaking during the flight was calculated.

4. The method for assessing the risk of transmission of a novel coronavirus according to claim 3, characterized in that: Based on the pathogen characteristic parameters, the calculation formula for the droplet concentration distribution data of the virus in the cabin using the bronchial-larynx-oral trimodal model is expressed as: Where f(d0) represents the droplet concentration distribution data, d0 represents the droplet size, CMD z , GSD z are the model parameters of the trachea-larynx-oral cavity tri-modal model.

5. The method for assessing the risk of transmission of a novel coronavirus according to claim 1, characterized in that: The first infection risk value of passengers under the influence of aerosol transmission through a first preset distance in the cabin is simulated by a fluid mechanics method, including: Import the cabin environment parameters and the continuous fluid region of the human body into the ICEM CFD software to create a discrete grid for numerical calculation; Import the discrete grid into Ansys Fluent software and convert the Navier-Stokes partial differential equations of fluid flow into a discretized algebraic equation system; The closed control equation of the SST k-ω turbulence model is used to solve the algebraic equations and calculate the first exposure dose of aerosol transmission of passenger i at the first preset distance. Based on the first exposure dose The first infection risk value of passenger i affected by aerosol transmission at a first preset distance in the cabin is estimated by a negative exponential dose-response model.

6. The method for assessing the risk of transmission of a novel coronavirus according to claim 5, characterized in that: The first exposure dose The calculation formula is: in, represents the first exposure dose of passenger i affected by aerosol transmission at the first preset distance, V is the cabin volume, ACH is the cabin air exchange rate, χ a is the virus inactivation rate, χ d is the deposition rate of virus-containing droplets, n(d0,t) is the amount of virus discharged by droplets with a release particle size of d0 at time t, and R i is the ratio of the steady-state CO2 concentration in the mouth area of ​​passenger i to the CO2 concentration in the cabin, IR is the inhalation rate, E(d r ) indicates the diameter is d r The deposition efficiency of droplets in the respiratory tract, is the filtration efficiency of the mask, An indicator indicating whether the i-th passenger wears a mask.

7. The method for assessing the risk of transmission of a novel coronavirus according to claim 1, characterized in that: The fluid mechanics method is used to simulate the second infection risk value of passengers under the influence of the aerosol transmission pathway at a second preset distance in the cabin, including: Import the cabin environment parameters into the CFD software to build the cabin geometry model; Based on the different body positions and facial orientations of the passengers, the amount of virus ingested by each passenger through inhalation and the amount of virus ingested through deposition were simulated; Based on the amount of virus ingested by inhalation and the amount of virus ingested by deposition, a negative exponential dose-response model is used to estimate the second infection risk value of passengers under the influence of aerosol transmission at a second preset distance in the cabin.

8. A novel coronavirus transmission risk assessment device, characterized in that: For executing the method according to any one of claims 1 to 7, the device comprises: a data acquisition module, for acquiring pathogen characteristic parameters, cabin environment parameters and passenger behavior matrix data, wherein the passenger behavior matrix data is the contact behavior data between passengers and environmental surfaces in the cabin extracted from multiple flight simulation experiments conducted in a cabin simulator environment; and for calculating the amount of virus discharged by the index case in the cabin through breathing or speaking during the flight based on the pathogen characteristic parameters; A first transmission assessment module is used to simulate a first infection risk value of a passenger under the influence of an aerosol transmission pathway of a first preset distance in the cabin by using the cabin environment parameters and the virus discharge through a fluid mechanics method; A second transmission assessment module is used to simulate a second infection risk value of a passenger under the influence of an aerosol transmission pathway at a second preset distance in the cabin by a fluid mechanics method using the cabin environment parameters and the virus discharge; A third transmission assessment module is used to use the passenger behavior matrix data to simulate the third infection risk value of the passenger under the influence of the contact transmission route in the cabin through a Markov chain model; The risk assessment module is used to estimate the comprehensive infection risk value of the passenger by combining the first infection risk value, the second infection risk value and the third infection risk value.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores computer program instructions that can be executed by the at least one processor, and the computer program instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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