A method and device for assessing the risk of transmission of a novel coronavirus, an electronic device and a medium

Abstract

The application belongs to the technical field of virus transmission analysis, and discloses a novel coronavirus transmission risk assessment method and device, electronic equipment and medium. By acquiring pathogen characteristic parameters, cabin environment parameters and passenger behavior matrix data; based on the pathogen characteristic parameters, the virus discharge amount of the indicated case through breathing or speaking in the cabin during the process of taking the machine is calculated; by using the cabin environment parameters and the virus discharge amount, the first infection risk value under the influence of the aerosol transmission path of the passenger through the first preset distance and the second infection risk value under the influence of the aerosol transmission path of the passenger through the second preset distance are simulated by the fluid mechanics method; by using the passenger behavior matrix data, the third infection risk value under the influence of the contact transmission path of the passenger is simulated by the Markov chain model; and the comprehensive infection risk value of the passenger is estimated by comprehensively considering the infection risk values. The application comprehensively evaluates the risk in all directions by comprehensively considering the multi-path transmission mechanism, so as to obtain a more accurate evaluation result.

Description

TECHNICAL FIELD

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

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

[0003] The aircraft cabin is a high-risk environment for the transmission of respiratory infectious diseases, and studying the multi-path transmission of the novel coronavirus in the passenger cabin is of great significance for preventing and controlling the cross-regional transmission of the virus. Traditional epidemiological investigation methods mainly rely on observational studies, rely on post-event interviews and video surveys, and determine the transmission path by the distance between the susceptible person and the source of infection. This method is not only susceptible to recall bias, but also lacks rigorousness and accuracy in the collected data. In contrast, mathematical modeling methods can systematically analyze the transmission path and mechanism by establishing precise mathematical formulas and assumptions, avoiding the limitations of relying on subjective recall and environmental conditions. Among commonly used mathematical models, there are various 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 directly apply to the complex environment of the cabin environment. Moreover, existing models simplify assumptions by simplifying building environmental factors and human behavior, or make assumptions about surface touching behavior and close contact behavior in the environment to simplify the model, which brings uncertainty to the calculation results. These models fail to fully consider the significant impact of close contact behavior and surface touching behavior of humans in the cabin on the infection risk of susceptible populations, and thus cannot accurately estimate the actual infection probability in the cabin. SUMMARY

[0004] To this end, the embodiments of the present application provide a novel coronavirus transmission risk assessment method, device, electronic equipment and medium, which realizes real behavior data of passengers in the cabin, comprehensively considers multi-path transmission mechanisms, and provides all-round transmission risk assessment through the joint application of CFD and mathematical models, to obtain more accurate assessment results and solve the limitations of single transmission path of traditional models.

[0005] In a first aspect, the application provides a novel coronavirus transmission risk assessment method.

[0006] The application is achieved by the following technical solutions:

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

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

[0009] Using the cabin environment parameters and the virus emission amount, simulating a first infection risk value of passengers in the cabin under the influence of the aerosol transmission pathway through a first preset distance by a fluid mechanics method;

[0010] Using the cabin environment parameters and the virus emission amount, simulating a second infection risk value of passengers in the cabin under the influence of the aerosol transmission pathway through a second preset distance by a fluid mechanics method;

[0011] Using the passenger behavior matrix data, simulating a third infection risk value of passengers in the cabin under the influence of the contact transmission pathway by a Markov chain model;

[0012] Combining the first infection risk value, the second infection risk value, and the third infection risk value, estimating a comprehensive infection risk value of the passengers.

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

[0014]

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

[0016] In a preferred example of the present application, based on the pathogen characteristic parameters, calculating the virus emission amount emitted by the index case through breathing or speaking in the cabin during the flight, comprises:

[0017] Based on the pathogen characteristic parameters, calculating the droplet concentration distribution data of the virus in the cabin by a bronchus-larynx-oral cavity three-mode model;

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

[0019] In a preferred example of the present application, based on the pathogen characteristic parameters, the calculation formula of the droplet concentration distribution data of the virus in the cabin is calculated by using a trachea-larynx-oral cavity three-mode model, which is expressed as:

[0020]

[0021] wherein f(d0) represents the droplet concentration distribution data, d0 represents the droplet particle size, CMD z , GSD z is a model parameter of the trachea-larynx-oral cavity three-mode model.

[0022] In a preferred example of the present application, the first infection risk value of the passenger in the cabin under the influence of the aerosol transmission pathway through the first preset distance is simulated by a fluid mechanics method, which comprises:

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

[0024] The discrete grid is introduced into the Ansys Fluent software, and the Navier-Stokes partial differential equation of fluid flow is converted into a discrete algebraic equation set;

[0025] The closed control equation of the SST k-ω turbulence model is used to solve the algebraic equation set, and the first exposure dose of the passenger i under the influence of the aerosol transmission through the first preset distance is calculated

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

[0027] In a preferred example of the present application, the first exposure dose is calculated by the formula:

[0028]

[0029] wherein, represents the first exposure dose of the passenger i under the influence of the aerosol transmission through the first preset distance, V is the cabin volume, ACH is the cabin air exchange rate, χ a is the inactivation rate of the virus, χ dis the deposition rate of droplets containing viruses, n(d0, t) is the virus emission amount carried by droplets with a release particle size of d0 at time t, 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 ) represents the deposition efficiency of droplets with a diameter of d r in the respiratory tract, is the filtration efficiency with a mask, is an indicator indicating whether the i-th passenger wears a mask.

[0030] In a preferred example of the present application, the second infection risk value of the passenger in the cabin under the influence of the aerosol propagation path through the second preset distance can be further simulated by using a fluid mechanics method, comprising:

[0031] The cabin environment parameters are imported into the CFD software to construct a geometric model of the cabin;

[0032] According to different body positions and facial orientations of the passengers, the amount of viruses inhaled by each passenger through inhalation and the amount of viruses inhaled through deposition are simulated;

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

[0034] In a second aspect, the present application provides a novel coronavirus transmission risk assessment device.

[0035] The present application is realized by the following technical scheme: a novel coronavirus transmission risk assessment device for executing the method of the first aspect, comprising:

[0036] A data acquisition module is configured to acquire pathogen characteristic parameters, cabin environment parameters, and passenger behavior matrix data, wherein the passenger behavior matrix data is contact behavior data of passengers with the environment surface in the cabin extracted from multiple flight simulation experiments in a cabin simulator environment; and is further configured to calculate the virus emission amount emitted by an index case through breathing or speaking in the cabin during the flight based on the pathogen characteristic parameters;

[0037] A first transmission assessment module is configured to simulate a first infection risk value of a passenger in a cabin under the influence of an aerosol propagation path through a first preset distance by using a fluid mechanics method based on the cabin environment parameters and the virus emission amount;

[0038] a second propagation evaluation module, configured to simulate, by using the cabin environment parameters and the virus discharge amount, a second infection risk value of a passenger in the cabin under the influence of an aerosol transmission path for a second preset distance by a fluid mechanics method;

[0039] a third propagation evaluation module, configured to simulate, by using the passenger behavior matrix data, a third infection risk value of the passenger in the cabin under the influence of a contact transmission path by a Markov chain model;

[0040] a risk evaluation module, configured to estimate a comprehensive infection risk value of the passenger in combination with 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, comprising: at least one processor; and a memory in communication connection with the at least one processor;

[0042] The memory stores computer program instructions executable 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 novel coronavirus propagation risk evaluation method of the first aspect.

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

[0044] The present application is realized by the following technical solutions:

[0045] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of any one of the novel coronavirus propagation risk evaluation methods.

[0046] Compared with the prior art, the technical solutions provided by the embodiments of the present application have at least the following beneficial effects:

[0047] The present application breaks through the simplifying assumptions of the traditional model, uses real behavior data of passengers in the cabin, and directly applies these data to the construction of the propagation model, ensuring that the simulation results are closer to the actual situation and greatly improving the accuracy and practical significance of the application; the computational fluid dynamics is applied to accurately simulate the complex air flow field in the cabin, which can truly reflect the aerosol diffusion path in aerodynamics. Through CFD modeling, the diffusion and deposition of virus particles can be accurately calculated in a specific physical scene, and the actual dynamic situation of air transmission can be comprehensively analyzed; the aerosol transmission, droplet transmission and contact transmission multiple transmission paths are combined, and the CFD and mathematical model are jointly used to provide comprehensive transmission risk evaluation, solving the limitation of single transmission path of the traditional model. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 A flowchart of a new coronavirus transmission risk assessment method provided by an embodiment of the present application is shown in the figure;

[0049] Figure 2 A passenger seat distribution map in the CA868 cabin within a close range provided by an embodiment of the present application is shown in the figure;

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

[0051] Figure 4 A structural diagram of a new coronavirus transmission risk assessment device provided by an embodiment of the present application is shown in the figure;

[0052] Explanation of reference signs:

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

[0054] The specific embodiments are merely illustrative of the present application, and are not intended to limit the present application. Those skilled in the art can make modifications to the embodiments without creative contribution after reading the present specification, and the modifications are within the scope of the present application as long as they are within the scope of the claims of the present application.

[0055] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative contribution are within the scope of protection of the present application.

[0056] In addition, the term “and / or” in the present application is merely to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can mean that there are three cases of A alone, A and B together, and B alone. In addition, the character “ / ” in the present application generally represents an “or” relationship between the associated objects unless otherwise specified.

[0057] The terms “first”, “second”, and the like in the present application are used to distinguish the same items or similar items with basically the same function and purpose, and it should be understood that there is no logical or time sequence dependence between “first”, “second”, and “n”, and the quantity and execution order are not limited.

[0058] The embodiments of the present application are further described in detail below with reference to the accompanying drawings. As shown in the drawings Figure 1 The present application proposes a new coronavirus transmission risk assessment method, which comprises the following steps:

[0059] S10: Obtain pathogen characteristic parameters, cabin environment parameters, and passenger behavior matrix data, which are contact behavior data of passengers and environment surfaces in the cabin extracted from multiple flight simulation experiments in a cabin simulator environment.

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

[0061]

[0062] Wherein, N s is the number of environment surfaces, N p is the number of passengers, ps(k) is the coefficient of passengers touching the environment surface at k time, ps j,i (k) = 1 indicates that passenger i touches environment surface j at k time, ps j,i (k) = 0 indicates that passenger i does not touch environment surface j at k time.

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

[0064] The index case refers to the nucleic acid test conducted after the flight arrives at the destination, and the test result is positive, and shows high homology with the SARS-CoV-2 strain of multiple other passengers.

[0065] Specifically, based on the pathogen characteristic parameters, the droplet concentration distribution data of the virus in the cabin is calculated by using the bronchial-laryngeal-oral three-mode model; based on the droplet concentration distribution data and the median gene copy number of the virus, the virus discharge amount of the index case through breathing or speaking in the cabin during the flight is calculated.

[0066] Wherein, based on the pathogen characteristic parameters, the droplet concentration distribution data of the virus in the cabin is calculated by using the bronchial-laryngeal-oral three-mode model (BLO, Bronchiolar-Laryngeal-Oral tri-modal model), and the calculation formula is represented as:

[0067]

[0068] In the formula, f(d0) represents the droplet concentration distribution data, d0 represents the droplet particle size, CMD z , GSD zModel parameters of the tricomponent model of the trachea-larynx-oral cavity.

[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. Droplets generated during normal breathing belong to the bronchial film burst mode (also referred to as the B mode), the larynx mode (also referred to as the L mode) is most active during conversation, and the oral mode (also referred to as the O mode) is also relatively active during conversation. Therefore, when indicating the case to only breathe, the single B model is used, which means that only the results of the B mode during breathing need to be calculated; when indicating the case to talk, the complete BLO model is used, which means that the values of the B mode, the L mode, and the O mode need to be calculated and accumulated. In the BLO model, z corresponds to the B mode when z is 1, at which time the model parameters are 0.054, the CMD z (μm) is 1.64, and the GSD z is 1.3; z corresponds to the L mode when z is 2, at which time the model parameters are 0.0684, the CMD z (μm) is 2.4, and the GSD z is 1.66; z corresponds to the O mode when z is 3, at which time the model parameters are 0.00126, the CMD z (μm) is 144.67, and the GSD z 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 multiplication process, the calculation formula of the virus discharge amount n(d0, t) discharged by the indicating case through breathing or speaking at time t is as follows:

[0071]

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

[0073]

[0074] The median gene copy number L a of the virus in the exhaled droplets of the indicating case is 2.73×10 7 mRNA copies / mL (the minimum value is 3.98×10 3 mRNA copies / mL, and the maximum value is 2.09×10 11 mRNA copies / mL).

[0075] S30: Utilizing the cabin environment parameters and the virus emission amount, a first infection risk value of the passenger in the cabin under the influence of the aerosol transmission route of the first preset distance is simulated by a fluid mechanics method.

[0076] The aerosol transmission route of the first preset distance is referred to as a long-distance aerosol transmission route, and generally refers to a distance far away from the source of infection (more than 1.5 meters). The virus-containing droplets or aerosols (diameter < 10 μm) are suspended in the air for a long time, and the susceptible person is infected with the virus by inhaling these aerosols.

[0077] The specific steps of simulating the first infection risk value of the passenger in the cabin under the influence of the aerosol transmission route of the first preset distance are as follows:

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

[0079] S302: The discrete grid is imported into the Ansys Fluent software, and the Navier-Stokes partial differential equation of fluid flow is converted into a discrete algebraic equation system.

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

[0081] S303: The algebraic equation system is solved by using the closure control equation of the SST k-ω turbulence model, and the first exposure dose of the passenger i under the influence of the aerosol transmission route of the first preset distance is calculated. Considering that the indoor environment is often in a turbulent state, the closure control equation of the SST k-ω turbulence model is adopted. This turbulence model has wide applicability and high numerical stability, which is beneficial to improve the accuracy of calculating the exposure dose.

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

[0083] Wherein, the tracer gas carbon dioxide (CO2) is introduced as a substitute for simulating virus transmission in the present application, which is used to simulate the mixed gas containing virus and air exhaled by the case. On this basis, the steady-state CO2 concentration C seatm,i at the mouth area of each susceptible passenger under the stable state is defined as the ratio R of the cabin CO2 concentration C cabin,i ​i :

[0084]

[0085] The virus concentration C i virus,t satisfies:

[0086]

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

[0088]

[0089] In the equilibrium state:

[0090] Accordingly, the formula for calculating the first exposure dose can be expressed as:

[0091]

[0092] where represents the first exposure dose of passenger i affected by the aerosol transmission of the first preset distance, V is the cabin volume, ACH is the cabin air exchange rate, χ a is the inactivation rate of the virus, χ d is the deposition rate of the virus-containing droplet, n(d0,t) is the virus output carried by the droplet with a release particle size of d0 at time t, 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 ) represents the deposition efficiency of the droplet with a diameter of d r in the respiratory tract, is the filtration efficiency with a mask, represents an index indicating whether the i-th passenger wears a mask, and equals 1 if wearing a mask, otherwise equals 0. It should be noted that considering that after evaporation, the virus viability in the droplet drops sharply to one-fourth of the initial value, and then slowly decreases, therefore, in the formula, it is assumed that the final viable virus concentration in the droplet is one-fourth of the initial concentration.

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

[0094] where η r is the respiratory tract of the susceptible person, and the value is 0.246, Da a first exposure dose for the susceptible passenger to be ingested through the aerosol route.

[0095] S40: Simulate a second infection risk value of the passenger in the cabin under the influence of the second preset distance aerosol transmission route by a fluid mechanics method using the cabin environment parameters and the virus discharge amount.

[0096] The second preset distance aerosol transmission route is also called the close-range transmission route, which occurs in a region close to the source of infection (within 1.5 meters), including two sub-routes. The virus-containing droplets discharged by the index case are inhaled by the susceptible person, leading to the infection of the susceptible person, and this route is also called the close-range air transmission route or the close-range aerosol transmission route; the virus-containing droplets discharged by the index case are directly deposited on the mucous membranes of the susceptible person (such as the eye, nose and mouth mucous membranes, etc.), leading to the infection of the susceptible person, and this sub-route is called the droplet transmission route or the large droplet transmission route.

[0097] The specific execution steps for simulating the second infection risk value of the passenger in the cabin under the influence of the second preset distance aerosol transmission route are as follows:

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

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

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

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

[0102] Considering the different body positions and facial orientations, the degree of decay of virus-containing droplets in the close-range is different. In this application, there are three body positions of the susceptible passenger, which are: 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 face each other. For example, as shown in Figure 2As shown, assuming the center point of the cabin seat is where the passenger's mouth is located, all susceptible passengers within the close-range (1.5 m) of the index case and the distance between the susceptible passengers and the index case are indicated, assuming the passengers are always facing the front in the cabin of the aircraft Figure 2 The circles on the middle seats represent passengers, and the solid squares above the circles represent the direction of the faces of the passengers), wherein the red passenger in the center is the index case 51C. If a susceptible passenger is within a 90° range on both sides of the index case (the blue passengers 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 a susceptible passenger is within a 90° range behind the index case (the purple passengers 52B and 52C in the figure), the susceptible passenger is considered to be behind the index case; and if a susceptible passenger is within a 90° range in front of the index case and the susceptible passenger and the index case are facing the same direction, the index case and the susceptible passenger are considered to be facing each other.

[0103] In the close-range, the amount of virus inhaled by each susceptible passenger and the amount of deposited inhaled virus 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) + g Br,in L a (d) η s,Br,in (d)(1-P Ta ) η fo,Br,in (d,s),

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

[0106] wherein the distance attenuation of the virus-containing droplets in the air is different due to different activities of the index case to expel the droplets, different distance attenuation coefficients of small droplets and large droplets, and different ways of being inhaled or deposited by the susceptible passengers, and the distance attenuation coefficients of the droplets when inhaled or deposited by speaking or breathing are respectively recorded as: ηs,Ta,in (d), η s,Br,in (d), η s,Ta,de (d), η s,Br,de (d).

[0107] The attenuation coefficients of the body orientation and the face orientation of the fine droplets and the large droplets generated by the index case through breathing and talking are also different, and the attenuation coefficients of the body orientation and the face orientation of the fine droplets and the large droplets generated by the index case through breathing are η fo,Br (d,s), and the attenuation coefficients of the body orientation and the face orientation of the fine droplets and the large droplets generated by the index case through talking are η fo,Ta (d,s), and are also divided into two intake modes of inhalation or deposition.

[0108] d is the initial diameter of the droplets, s is the distance between the index case and the susceptible passenger within a close range, P Ta is the talking frequency, that is, the proportion of the talking time of the index case to the total flight time, and is generally assumed to be 20%; g Ta (d) is the rate of the droplets generated by the index case through talking, g Br (d) is the rate of the droplets generated by the index case through breathing, L a is the mRNA copy number of the virus in the droplets.

[0109] The calculation formula of the second infection risk value is:

[0110]

[0111] Among them, D in , D de are the total virus amount taken in through the inhalation sub-pathway in the droplet route and the total virus amount taken in through the deposition sub-pathway in the droplet route.

[0112] S50: Using the passenger behavior matrix data, the third infection risk value of the passengers in the cabin under the influence of the contact transmission route is simulated by a Markov chain model.

[0113] Among them, the passenger behavior matrix data is represented as:

[0114]

[0115] Among them, N s is the number of environmental surfaces, N p is the number of passengers, ps(k) is the coefficient of the passenger touching the environmental surface at k time, ps j,i (k) = 1 indicates that the passenger i touches the environmental surface j at the k time point, and ps j,i (k) = 0 indicates that the passenger i does not touch the environmental surface j at the k time point.

[0116] Definition Virus concentration on the surface of the environment j at time k (TCID50 / m2) 50 or mRNA copies / m 2 ), Virus concentration on the surface of the environment j at time k (TCID50 / m2) Virus concentration on the surface of the environment j at time k (TCID50 / m2) satisfies:

[0117]

[0118] where A hs is the area of the hand contacting the surface of the environment (m 2 ), which is 0.0042 m 2 , is the area of the surface of the environment j (m 2 ). The areas of different surfaces of the environment are measured or estimated according to the actual situation. In this application, the surfaces of the environment are divided into porous surfaces (such as textiles) and non-porous surfaces (such as metals, plastics, etc.), and the transfer efficiency and inactivation rate of viruses on the two surfaces are different: τ sh is the transfer efficiency of viruses from the surface of the environment to the hand, τ hs is the transfer efficiency of viruses from the hand to the surface of the environment, b s is the inactivation rate of SARS-CoV-2 virus, is the virus concentration in the adjacent space of the surface of the object j at time k, D ep is the settling rate of virus-containing particles; is the volume of the adjacent space of the surface of the object j.

[0119] The vector pm i (k) is the vector of the behavior of passenger i touching his own mucous membrane. Similar to the Ps matrix, if at time k, passenger i touches his own mucous membrane, pm i (k) = 1, otherwise pm i (k) = 0. This study assumes that the passenger only uses the same finger of the same hand to touch his own mucous membrane and the surrounding surface of the environment, and when the virus is transferred from the hand to the mucous membrane, it is all absorbed by the mucous membrane. Accordingly, after a time interval of ΔT, the virus concentration on the hand of passenger i at time (k) Vh satisfies:

[0120]

[0121] A h is the area of the palm of the hand (m 2 ), which is 0.0203 m 2 ​, The area (m 2 ) that the hand contacts with the mucous membrane is assumed to be 0.0001 m 2 . h is the inactivation rate of the virus on the hand, τ hm is the transfer efficiency of the virus from the hand to the mucous membrane.

[0122] At the initial state (k = 0), the concentration of the virus on the surface of the environment is 0. The total amount of the virus that the passenger i intakes through the contact route can be recorded as:

[0123]

[0124] where N t is the total number of time intervals, the total flight time T is 12.8 hours, and the time interval

[0125] S60: Estimate the comprehensive infection risk value of the passenger by synthesizing the first infection risk value, the second infection risk value and the third infection risk value.

[0126] Specifically, the comprehensive infection risk value of the passenger i is P t :

[0127]

[0128] where P a represents the first infection risk value, P c represents the second infection risk value, and P f represents the third infection risk value.

[0129] Specifically, the input data of the passenger behavior in the contact route in the present application is simulated based on the passenger behavior data (including the cabin crew) in the real cabin. The passenger behavior data is derived from five experiments conducted in the simulated cabin. The experiments were conducted in an aircraft cabin simulator environment, which completely replicated the front entrance area and the interior space of the economy cabin of a Boeing 737 aircraft. In the five experiments, 48, 48, 48, 48 and 45 subjects were recruited respectively, who entered the aircraft cabin simulator as passengers for a five-hour simulated flight, and two dedicated cabin crew provided services for the passengers, such as helping the passengers to place their luggage, distributing beverages and meals. The aircraft model for simulating the CA868 flight in the present application is a Boeing 777 large aircraft with 9 seats in each 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 is simulated according to the epidemic event condition setting in the present application.

[0130] Further, based on the complex network theory, the touching process between the surfaces in the cabin and the passengers' hands is regarded as a "surface contact network" in this application; where each surface in the cabin and each passenger's hand is regarded as a "node", the touching between the surface and the hand is regarded as an "edge", and the virus spreads in the surface contact network along with the complex touching behavior of the passengers in the cabin. Meanwhile, it is also considered that the surface contact network in the cabin has community property, i.e. three passengers on the same side of the aisle and the environmental surfaces around the three passengers form a "subcommunity", the surfaces contained in the subcommunity are closely connected due to frequent touching behavior (assuming that the passengers in the cabin sit on the seats most of the time); the subcommunities are connected through the surfaces in the lavatory due to the touching behavior when the passengers go to the lavatory. Based on the community property of the cabin, the characteristics of the experimental data of the passengers in the real cabin are extracted, and the touching frequency of each surface in the subcommunity by the passengers at different positions in the subcommunity is obtained, and the surface contact network in each subcommunity is simulated 1000 times with the average touching frequency per hour. After simplifying part of the touching behavior of the passengers on board, a schematic diagram of part of the subcommunities of the surface contact network is shown in Figure 3 .

[0131] Three adjacent passengers are regarded as a subcommunity (for example, passengers 47A, 47B and 47C form a 47ABC subcommunity), and the touching behavior of the passengers is simulated in units of subcommunities, and 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, 47ABC subcommunity and 47JKL subcommunity), and the middle subcommunity contains two aisle passengers and one middle passenger (for example, 47DEH subcommunity). There are 28 community surfaces in each subcommunity, including cups (CP), meal plates (MP), seat armrests (AR), seat cushions (S), seat backs (SB), service buttons (PSU) and the like used by the passengers.

[0132] The non-subcommunity surfaces include two types: lavatory interior surfaces and economy class front public surfaces. Among them, there are 5 lavatories in the economy class, and each lavatory has 11 surfaces, including lavatory door handle, lavatory mirror, and toilet button, etc. Since the touch data in the lavatory cannot be obtained in the experiment, it is assumed that the passengers touch in a specific order that conforms to the logic of real-world personnel behavior in the lavatory. For example, one touch order of the passengers in the lavatory is as follows: lavatory door handle (TLD), lavatory shelf (TLI), toilet roll (TLQ), toilet cover (TLP), toilet left side (TLCL), toilet right side (TLCR), lavatory tissue box (TLT), lavatory handrail (TLAR), toilet roll (TLQ), toilet cover (TLP), toilet button (TLB), lavatory mirror (TLM), faucet (TLW), lavatory tissue box (TLT), lavatory door handle (TLD). In addition, there are 5 economy class front public surfaces, including door handle, door curtain, and flight attendant tray, etc. Therefore, there are a total of 2520 (90x28) subcommunity surfaces and 60 (11x5+5) non-subcommunity surfaces in the entire cabin, a total of 2580 surfaces.

[0133] The method proposed in the present application overcomes the bias problem in traditional epidemiological investigation: traditional epidemiological investigation relies on the subjective recall of infected persons, which is easily affected by memory bias and data inaccuracy; the present application introduces real behavior data to ensure the accuracy of the data, solving the problem of insufficient data rigor in epidemiological research. Solve the 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, the present technology can accurately predict the spread of viruses without relying on actual virus experiments, avoiding the ethical dilemma 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, usually cannot accurately simulate complex cabin environments, and their assumptions are too simple. By combining CFD technology with multi-path transmission mechanisms, the present technology can better adapt to the special airflow environment in enclosed spaces, especially in complex scenarios such as passenger cabins, providing more accurate risk prediction. Respond to the impact of close contact behavior and surface contact in the cabin: existing models do not adequately consider the impact of close contact between passengers and surface touch behavior, the present technology integrates behavior data to comprehensively analyze the potential impact of these behaviors on virus transmission, enabling more accurate assessment of passenger infection risk and optimizing prevention and control measures. Improve the quantitative analysis capability of virus transmission risk: traditional models have limited quantitative capabilities in complex multi-factor environments. The present technology combines CFD models and mathematical models to quantitatively analyze the contribution of each transmission pathway to the risk of infection, providing a more detailed transmission dynamic analysis tool and effectively improving the accuracy of risk assessment.

[0134] In another embodiment of the present application, a device for assessing the risk of transmission of the novel coronavirus is also provided for performing the method described above, as shown in the figure, the device comprises: a data acquisition module 100, a first transmission assessment module 200, a second transmission assessment module 300, a third transmission assessment module 400, and a risk assessment module 500. Figure 4

[0135] The data acquisition module 100 is configured to acquire pathogen characteristic parameters, cabin environment parameters, and passenger behavior matrix data, wherein the passenger behavior matrix data is contact behavior data of passengers with environmental surfaces in the cabin extracted through multiple flight simulation experiments in the cabin simulator environment; and to calculate the amount of virus expelled by a symptomatic passenger through breathing or speaking in the cabin during the flight based on the pathogen characteristic parameters.

[0136] The first transmission assessment module 200 is configured to simulate a first infection risk value of a passenger in the cabin under the influence of the aerosol transmission pathway for a first preset distance by using the cabin environment parameters and the amount of virus expelled.

[0137] The second transmission assessment module 300 is configured to simulate a second infection risk value of a passenger in the cabin under the influence of the aerosol transmission pathway for a second preset distance by using the cabin environment parameters and the amount of virus expelled.

[0138] The third transmission assessment module 400 is configured to simulate a third infection risk value of a passenger in the cabin under the influence of the contact transmission pathway by using the passenger behavior matrix data through a Markov chain model.

[0139] The risk assessment module 500 is configured to estimate a comprehensive infection risk value of the passenger by comprehensively considering the first infection risk value, the second infection risk value, and the third infection risk value.

[0140] The specific limitations of the device for assessing the risk of transmission of the novel coronavirus provided in the present embodiment can be referred to the embodiments of the method for assessing the risk of transmission of the novel coronavirus described above, which will not be repeated here. Each module in the device for assessing the risk of transmission of the novel coronavirus described above can be realized by software, hardware, and combinations thereof, in whole or in part. Each module described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0141] ​The electronic device provided by the embodiment of the present application can include a processor, a memory, a network interface and a database connected through a system bus. 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 through a network connection. The computer program is executed by the processor to make the processor execute the steps of the new coronavirus transmission risk assessment method according to any one of the above embodiments.

[0142] The working process, working details and technical effects of the computer device provided by the embodiment can be referred to the above embodiments of the new coronavirus transmission risk assessment method, and will not be repeated here.

[0143] The embodiment of the present application provides a computer readable storage medium, which stores a computer program. The computer program is executed by the processor to realize the steps of the new coronavirus transmission risk assessment method according to any one of the above embodiments. The computer readable storage medium refers to a carrier for storing data, which can include, but is not limited to, floppy disk, optical disk, hard disk, flash memory, USB flash disk and / or Memory Stick, etc. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0144] The working process, working details and technical effects of the computer readable storage medium provided by the embodiment can be referred to the above embodiments of the new coronavirus transmission risk assessment method, and will not be repeated here.

[0145] 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. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, storage, database or other medium used in each embodiment provided in the present 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 but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), 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), etc.

[0146] The technical features of the above embodiments can be combined in any manner. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present disclosure.

[0147] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system 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 by, The method comprises: acquiring pathogen characteristic parameters, cabin environment parameters, and passenger behavior matrix data, the passenger behavior matrix data being contact behavior data of passengers with environmental surfaces in the cabin extracted from multiple flight simulation experiments conducted in a cabin simulator environment; calculating, based on the pathogen characteristic parameters, a virus discharge amount of an index case in the cabin during flight by breathing or speaking; simulate, by a method of fluid mechanics, a first infection risk value of a passenger in the cabin under the influence of an aerosol propagation path of a first preset distance, by using the cabin environment parameters and the virus discharge amount, wherein the continuous fluid region of the cabin environment parameters and the human body is introduced into ICEM CFD software to create a discrete grid for numerical calculation; the discrete grid is introduced into Ansys Fluent software, and the Navier-Stokes partial differential equation of fluid flow is converted into a discrete algebraic equation group; the closed control equation of the SST k-ω turbulence model is used to solve the algebraic equation group, and a first exposure dose of the passenger i under the influence of the aerosol propagation of the first preset distance is calculated based on the first exposure dose estimate a first infection risk value of the passenger i in the cabin under the influence of the aerosol propagation of the first preset distance by a negative exponential dose-response model; using the cabin environment parameters and the virus discharge amount, simulating, by a fluid mechanics method, a second infection risk value of a passenger in the cabin under the influence of an aerosol transmission pathway for a second preset distance, the second preset distance being smaller than the first preset distance, wherein the cabin environment parameters are imported into CFD software to construct a geometric model of the cabin; based on different body positions and facial orientations of the passengers, simulating a virus amount inhaled by each passenger and a virus amount deposited by each passenger; based on the virus amount inhaled and the virus amount deposited, estimating, by a negative exponential dose-response model, the second infection risk value of the passenger in the cabin under the influence of the aerosol transmission pathway for the second preset distance; using the passenger behavior matrix data, simulating, by a Markov chain model, a third infection risk value of the passenger in the cabin under the influence of a contact transmission pathway; combining the first infection risk value, the second infection risk value, and the third infection risk value, estimating a comprehensive infection risk value of the passenger. 2.The method of claim 1, wherein, The passenger behavior matrix data is represented as: Ps = (ps j,i (k)) Ns×Np , where N s is the number of environmental surfaces, N p is the number of passengers, ps j,i (k) is a coefficient indicating that passenger i touched environmental surface j at time k, ps j,i (k) = 1 indicates that passenger i touched environmental surface j at time k, ps j,i (k) = 0 indicates that passenger i did not touch environmental surface j at time k. 3.The method of claim 1, wherein, calculating, based on the pathogen characteristic parameters, a virus discharge amount of an index case in the cabin during flight by breathing or speaking, comprises: based on the pathogen characteristic parameters, calculating, by a bronchus-larynx-oral cavity three-mode model, droplet concentration distribution data of the virus in the cabin; based on the droplet concentration distribution data and a median gene copy number of the virus, calculating the virus discharge amount of the index case in the cabin during flight by breathing or speaking. 4.The method of claim 3, wherein, The calculation formula for calculating, based on the pathogen characteristic parameters, the droplet concentration distribution data of the virus in the cabin by the bronchus-larynx-oral cavity three-mode model is represented as: wherein f(d0) represents the droplet concentration distribution data, d0 represents the droplet size, CMD z , GSD z are model parameters of the trachea-larynx-oral three-mode model. 5.The method of claim 1, wherein, The first exposure dose The formula for calculating is: wherein, Ei(t) represents the first exposure dose of passenger i to aerosol propagation over the first predetermined distance, V is the cabin volume, ACH is the cabin air change rate, χ a is the inactivation rate of the virus, χ d is the deposition rate of the virus-containing droplet, n(d0, t) is the virus emission carried by the droplet with a release particle size of d0 at time t, R i is the ratio of the steady-state CO2 concentration at the mouth area of passenger i to the CO2 concentration in the cabin, IR is the inhalation rate, E(d r ) represents the deposition efficiency of the droplet with a diameter of d r in the respiratory tract, is the filtration efficiency with a mask, represents an index indicating whether the ith passenger wears a mask. 6.A device for assessing the risk of transmission of a novel coronavirus, characterized by The device for performing the method according to any one of claims 1 to 5 comprises: a data acquisition module configured to acquire pathogen characteristic parameters, cabin environment parameters, and passenger behavior matrix data, wherein the passenger behavior matrix data is contact behavior data of passengers with environmental surfaces in the cabin extracted from multiple flight simulation experiments conducted in a cabin simulator environment; and to calculate, based on the pathogen characteristic parameters, a virus discharge amount of an index case in the cabin during flight by breathing or speaking; The first propagation evaluation module is configured to simulate, by using the cabin environment parameters and the virus discharge amount, a first infection risk value of a passenger in the cabin under the influence of an aerosol propagation path of a first preset distance by a fluid mechanics method; wherein the continuous fluid region of the cabin environment parameters and the human body is introduced into ICEM CFD software to create a discrete grid for numerical calculation; the discrete grid is introduced into Ansys Fluent software to convert the Navier-Stokes partial differential equation of fluid flow into a discrete algebraic equation set; the closed control equation of the SST k-ω turbulence model is adopted to solve the algebraic equation set, and a first exposure dose of the passenger i under the influence of the aerosol propagation of the first preset distance is calculated Based on the first exposure dose The first infection risk value of the passenger i in the cabin under the influence of the aerosol propagation of the first preset distance is estimated by a negative exponential dose-response model. a second propagation evaluation module configured to simulate a second infection risk value of a passenger in the cabin under the influence of an aerosol propagation path of a second preset distance by a method of fluid mechanics, using the cabin environment parameters and the virus discharge amount; wherein the cabin environment parameters are imported into a CFD software to construct a geometric model of the cabin; according to different body positions and facial orientations of the passengers, the virus amount inhaled by each passenger by inhalation and the virus amount inhaled by deposition are simulated; based on the virus amount inhaled by inhalation and the virus amount inhaled by deposition, a negative exponential dose-response model is used to estimate the second infection risk value of the passenger in the cabin under the influence of the aerosol propagation of the second preset distance; a third propagation evaluation module configured to simulate a third infection risk value of a passenger in the cabin under the influence of a contact transmission path by a Markov chain model, using the passenger behavior matrix data; a risk evaluation module configured to estimate a 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.

7. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores computer program instructions executable by the at least one processor, and the computer program instructions are executed by the at least one processor to cause the at least one processor to perform the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 5.

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