CN117457231A

CN117457231A - Virus propagation risk calculation method and device based on Markov chain model

Info

Publication number: CN117457231A
Application number: CN202311415539.9A
Authority: CN
Inventors: 肖胜蓝; 王怀彬; 黄茜; 舒跃龙
Original assignee: Sun Yat Sen University
Current assignee: Sun Yat Sen University
Priority date: 2023-10-27
Filing date: 2023-10-27
Publication date: 2024-01-26

Abstract

The invention discloses a method and a device for calculating virus propagation risk based on a Markov chain model, wherein the method comprises the following steps: acquiring parameter information, wherein the parameter information comprises: environmental characteristic parameters of the environment to be detected, specific parameters of viruses to be detected and behavior parameters of users in the environment to be detected; and carrying out dose calculation by adopting the parameter information based on a preset Markov chain model to obtain propagation dose values of viruses in different propagation paths, and calculating propagation risk values by adopting the propagation dose values. According to the invention, the environmental characteristic parameters, the specific parameters and the behavior parameters can be respectively acquired, the Markov chain model is utilized to comprehensively evaluate and calculate the environmental, virus specific characteristics and the behavior parameters, the doses of viruses in different transmission paths are obtained through calculation, and then the risks of the viruses are evaluated by combining the doses of the viruses in different paths, so that the deviation of evaluation and calculation can be reduced, and the calculation precision is improved.

Description

Virus propagation risk calculation method and device based on Markov chain model

Technical Field

The invention relates to the technical field of virus risk assessment, in particular to a method and a device for calculating virus transmission risk based on a Markov chain model.

Background

Influenza virus, abbreviated influenza virus, is an RNA virus responsible for influenza in humans and animals, and belongs to the orthomyxoviridae family in taxonomy, which causes acute upper respiratory tract infections and is rapidly transmitted by air, often with periodic pandemics around the world.

Influenza viruses spread mainly by droplets or object contact in the air and spread very rapidly, especially in densely populated, poorly ventilated and poorly air-ventilated rooms or in closed places. In order to avoid large-scale infections, it is necessary to assess the risk of viral transmission in the room or in a closed place, and thus to be able to deduce the number of people infected at different risks. The current common risk assessment method is to create a differential equation model of the environment area and then use specific parameters of the combined virus in the environment (such as survival rate, survival temperature, etc.) to assess the risk of the virus spreading indoors.

However, the current common evaluation method has the following technical problems: in different environment areas, the number of people and the environment conditions can change along with the time, and the activities or behaviors of different people in the environment are different; while different environmental conditions and different behaviors may affect the viral propagation rate. Therefore, only differential equation model and virus specific parameters in the environment are used for evaluation, the evaluation result has larger deviation from the actual, and the accuracy is lower.

Disclosure of Invention

The invention provides a method and a device for calculating virus propagation risk based on a Markov chain model.

A first aspect of an embodiment of the present invention provides a method for calculating a risk of viral propagation based on a markov chain model, the method including:

acquiring parameter information, wherein the parameter information comprises: environmental characteristic parameters of the environment to be detected, specific parameters of viruses to be detected and behavior parameters of users in the environment to be detected;

and carrying out dose calculation by adopting the parameter information based on a preset Markov chain model to obtain propagation dose values of viruses in different propagation paths, and calculating propagation risk values by adopting the propagation dose values.

In a possible implementation manner of the first aspect, the calculating the dose based on the preset markov chain model using the parameter information to obtain propagation dose values of the virus in different propagation paths includes:

after a plurality of propagation stages of virus propagation are determined, acquiring propagation rates of viruses in a plurality of different preset virus propagation states in each propagation stage, wherein the preset virus propagation states are states in the process of closely or remotely propagating a virus source, and each propagation stage corresponds to an environment characteristic parameter of an environment to be detected in different time intervals;

Calculating a state transition propagation rate between two states by using a plurality of propagation rates, wherein the state transition propagation rate is a propagation rate of virus from a current preset virus propagation state to another preset virus propagation state;

constructing a Markov chain probability transition matrix corresponding to each propagation stage by utilizing a plurality of state transition propagation rates;

and carrying out dose calculation by adopting a plurality of Markov chain probability transition matrixes to obtain a propagation dose value corresponding to the virus in an aerosol path or a contact path.

In a possible implementation manner of the first aspect, the calculation of the propagation dose value of the virus corresponding to the aerosol path or the contact path is shown in the following formula:

D _k ＝[D _k-1 ×P ^(m) ]+N _k ；

in the above, P ^(m) Markov chain probability transition matrix of virus after m time steps, expressed as m p multiplications, D _k Represents the distribution of viral dose in each state after k times of viral discharge, N _k The amount of virus entering each state at the time of the kth virus discharge is shown.

In a possible implementation manner of the first aspect, the amount of virus entering each state when the virus is discharged is as follows:

in the above, d _o Is the initial diameter of the droplet, d _a Is the largest initial diameter of the aerosol droplets, L _a Is the concentration of live virus in the inhaled droplets of the susceptible individual, f (d) is the probability distribution function of the particle size of the initially expelled droplets, n _a Is the number of droplets produced by respiratory activity per unit time, T being the time.

In a possible implementation manner of the first aspect, the preset virus propagation state includes:

a near-field air state, a far-field air state, a near-field nonporous surface state, a near-field porous surface state, a far-field nonporous surface state, a near-field susceptible hand state, a near-field susceptible facial mucosa state, a near-field susceptible lower respiratory tract state, a far-field susceptible hand state, a far-field susceptible facial mucosa state, a far-field susceptible lower respiratory tract state, a virus discharge from the environment to be detected to an external state, and a virus inactivation state.

In a possible implementation manner of the first aspect, the calculating a propagation risk value using the propagation dose value includes:

calculating an in-vivo predicted dose value of a user after virus inhalation by using a preset breathing model and adopting the transmitted dose value;

The in vivo predicted dose value is used to calculate a propagation risk value using a negative exponential dose response model.

In a possible implementation manner of the first aspect, the preset breathing model is shown in the following formula:

wherein Cn is _i Is the concentration; CMD (CMD) _i The median diameter is expressed as the particle size of 50% of the total number of particles, when the particles are ordered by particle size, the number of particles larger than the particle size and smaller than the particle size; CSD (compact form factor d) _i Is the geometric standard deviation.

In a possible implementation manner of the first aspect, after the step of obtaining propagation dose values of viruses in different propagation paths, the method further includes:

performing a propagation analysis using the propagation dose value, the propagation analysis comprising: infection contribution ratio, risk comparison, infectious viral load, and viral transmission factor for each pathway.

A second aspect of an embodiment of the present invention provides a markov chain model-based virus propagation risk calculating apparatus, the apparatus including:

the acquisition module is used for acquiring parameter information, and the parameter information comprises: environmental characteristic parameters of the environment to be detected, specific parameters of viruses to be detected and behavior parameters of users in the environment to be detected;

And the risk calculation module is used for calculating the dose by adopting the parameter information based on a preset Markov chain model to obtain propagation dose values of viruses in different propagation paths, and calculating the propagation risk value by adopting the propagation dose values.

In a possible implementation manner of the second aspect, the calculating the dose based on the preset markov chain model by using the parameter information to obtain propagation dose values of the virus in different propagation paths includes:

In a possible implementation manner of the second aspect, the calculation of the propagation dose value of the virus corresponding to the aerosol path or the contact path is shown in the following formula:

D _k ＝[D _k-1 ×P ^(m) ]+N _k ；

In a possible implementation manner of the second aspect, the amount of virus entering each state when the virus is discharged is as follows:

In a possible implementation manner of the second aspect, the preset virus propagation state includes:

In a possible implementation manner of the second aspect, the calculating a propagation risk value using the propagation dose value includes:

In a possible implementation manner of the second aspect, the preset breathing model is shown in the following formula:

In a possible implementation manner of the second aspect, after the step of obtaining propagation dose values of viruses in different propagation paths, the method further includes:

Compared with the prior art, the method and the device for calculating the virus propagation risk based on the Markov chain model have the beneficial effects that: according to the invention, the environmental characteristic parameters, the specific parameters and the behavior parameters can be respectively acquired, the Markov chain model is utilized to comprehensively evaluate and calculate the environmental, virus specific characteristics and the behavior parameters, the doses of viruses in different transmission paths are obtained through calculation, and then the risks of the viruses are evaluated by combining the doses of the viruses in different paths, so that the deviation of evaluation and calculation can be reduced, and the calculation precision is improved.

Drawings

FIG. 1 is a schematic flow chart of a method for calculating risk of viral propagation based on Markov chain model according to an embodiment of the present invention;

FIG. 2 is a bus seat diagram of an application scenario according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a propagation velocity matrix provided by an embodiment of the present invention;

FIG. 4 is a schematic diagram of a particle size distribution of breath-generated droplets according to an embodiment of the present invention;

FIG. 5 is a flowchart illustrating an exemplary method for calculating risk of viral propagation based on Markov chain model according to one embodiment of the present invention;

fig. 6 is a schematic structural diagram of a virus propagation risk calculating device based on a markov chain model according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In order to solve the above problems, a method for calculating the risk of viral transmission based on the markov chain model according to the embodiments of the present application will be described and illustrated in detail in the following specific examples.

Referring to fig. 1, a flowchart of a method for calculating a virus propagation risk based on a markov chain model according to an embodiment of the present invention is shown.

In one embodiment, the method is applicable to a computer.

In an application scenario, the application site of the method may be a bus or a passenger car. In buses or passenger vehicles, 30-50 persons can sit generally, and the persons are close to each other and have dense activities; for example, the close contact behavior and the surface touch behavior of users within passenger buses, both of which have a significant impact on the risk of infection for susceptible people.

On this basis, although the window can be ventilated, in the process of high-speed running, a ventilation system in the window can be closed generally, so that a bus or a passenger car is in a closed environment.

In an application mode, the invention can construct a mathematical model based on the behavior data of passengers on a Markov chain and a closed public transport means aiming at the passenger bus environment. The influence of various factors (such as ventilation rate, virus dose response rate and the like) on the transmission of viruses (especially epidemic viruses) is quantified, the infection risk of susceptible people in different areas in the passenger bus environment is calculated, and the relative importance of the viruses in different transmission ways and the relation between the transmission risk and relevant environmental parameters are compared.

Wherein, as an example, the method for calculating the virus propagation risk based on the Markov chain model can comprise the following steps:

s11, acquiring parameter information, wherein the parameter information comprises: environmental characteristic parameters of the environment to be detected, specific parameters of viruses to be detected and behavior parameters of users in the environment to be detected.

Referring to fig. 2, a bus seat map of an application scenario provided by an embodiment of the present invention is shown.

In the above figures, the positions of the virus sources are 12A, and the positions of the infection sources are 13A, 9B, 5B, 1A and 6D, respectively. The empty position is 8B, the rest is normal seat.

In the normal running process of the bus, parameter information of the bus can be acquired, wherein the parameter information respectively comprises: environmental characteristic parameters of the environment to be detected, specific parameters of viruses to be detected and behavior parameters of users in the environment to be detected.

In one embodiment, the environmental characteristic parameters include: volume of bus, ventilation rate, etc.

In one implementation, the volume of the bus may be directly queried and the ventilation rate may be obtained by querying the literature. The bus ventilation rate data was also measured using the tracer concentration decay method on the same vehicle traveling on the same route.

Alternatively, the bus ventilation rate data may be monitored in real time, and in addition to the real-time monitoring, general data of such model buses, including driving routes and the like, may be considered to be searched from other materials on the network.

In one embodiment, the specificity parameters include: inactivation rate, transfer rate, and the like.

In one implementation, the parameters such as the inactivation rate and the transfer rate may be obtained by querying different documents.

For example, the following table may be referred to:

in one embodiment, the behavior parameters include: contact behavior, frequency of behavior, etc.

In an implementation manner, parameters such as contact behavior and behavior frequency can also be obtained by querying different documents.

For example, the bus passenger surface contact frequency may be as shown in the following table:

for another example, the surface area of the contact area of a bus occupant with a surface may be as shown in the following table:

and S12, carrying out dose calculation by adopting the parameter information based on a preset Markov chain model to obtain propagation dose values of viruses in different propagation paths, and calculating propagation risk values by adopting the propagation dose values.

In one embodiment, the virus may have a plurality of different propagation paths in a closed indoor environment, and in a practical application, the virus may propagate on a bus through three paths, namely, a contact propagation (direct and indirect contact) path, a spray propagation path and an aerosol propagation path.

The aerosol transmission route refers to the virus-carrying droplets which indicate the case of exhalation, and which contain a plurality of particles with small particle diameters. If the aerodynamic diameter of these particles after evaporation in air is less than 10 μm, aerosols are formed, suspended in air for a longer period of time, and as the airflow moves to a greater distance, it is inhaled by the susceptible person into the respiratory tract, resulting in aerosol propagation.

The spray transmission route means that the spray is generated by sneeze, cough, speaking and other activities, and the spray is directly inhaled into respiratory tract by a susceptible person or is adhered to mucous membrane (eyes, nostrils and lips) of the susceptible person to cause infection of the susceptible person, and the diameter of the inhaled spray is generally 10-100 μm. Droplets with a diameter greater than 100 μm adhere mainly to the mucosa of susceptible people and move over a horizontal distance of typically 1.5-2m.

Contact propagation pathways include direct and indirect contact propagation pathways. The direct contact propagation pathway refers to infection caused by human-to-human contact (e.g., handshaking). The indirect contact propagation pathway is also referred to as the contaminant contact propagation pathway and refers to infection by a susceptible person by contacting an environmental surface contaminated with the indicated case. The direct and indirect contact propagation pathways are difficult to distinguish because the hands can be contaminated by both pathways and the viral concentration on the hands of susceptible persons affects the exposure level. The two pathways may have been combined into one pathway.

In one operation mode, the dose values of the virus transmitted in different ways can be calculated respectively, and finally the transmission risk of the virus is estimated according to the dose values in different ways.

In an alternative embodiment, the method may comprise the following sub-steps with respect to the propagation dose values of the aerosol propagation path and the contact propagation path:

s21, after a plurality of transmission stages of virus transmission are determined, the transmission rate of the virus in each transmission stage in a plurality of different preset virus transmission states is obtained, wherein the preset virus transmission states are states in the process that a virus source is transmitted to a short distance or a long distance, and each transmission stage corresponds to an environment characteristic parameter of an environment to be detected in different time intervals.

Since the bus is continuously running, the indoor conditions are different during running, the ventilation rate may be increased during running, and personnel may be increased or reduced. Thus, the number of people and the indoor environment are different in different time periods.

In order to distinguish different situations, the whole driving process can be divided into different stages, so as to obtain a plurality of transmission stages, wherein each transmission stage corresponds to an environmental characteristic parameter of a bus in different time intervals.

For example, the bus is separated from the AA station in a at 12 and reaches the BB station in B at 15 by 30 at 10. Based on the bus video screen shots and the passenger questionnaire data, 2 passengers (e.g., 9B and 13A of fig. 2) take the bus for about 40-45 minutes, 4 passengers (e.g., 3A, 3B, 13C, 13E of fig. 2) get off the bus about 16 km before the BB station, and the rest of the passengers sit through the journey.

The whole bus driving process is divided into eight different stages according to the speed change according to the bus travel schedule, and the specific steps are shown in the following table.

Referring to the table above, the travel speed and ventilation rate are different for each propagation stage. Wherein two 15 minute time periods (11:55-12:10, 15:30-15:45) are allocated before and after the journey, respectively, during which passengers can get on or off the vehicle. Since the time for the bus to temporarily stop on the journey is unknown, two 5 minute time periods (12:50-12:55, 15:10-15:15) are allocated on the journey for passengers to get on and off.

The bus is of a fully-closed structure, no window is available for opening, the lower layer is a luggage compartment and a toilet, the upper layer is a passenger compartment, and the driver seat is arranged in the middle of the two decks. The length of the passenger cabin is 11.4m, the width is 2.5m, the height is 2m, and the volume is 60.42m ³ . The bus driving direction is taken as the front, 13 rows of seats are arranged on the left side of the passenger cabin, the space between each row of seats is 86cm, 10 rows of seats are arranged on the right side of the passenger cabin, the space between the front 2 rows and the 6 rows is 80cm, the space between the rear 4 rows is 102cm, and the aisle width between the two sides of seats is 50cm. The bus is equipped with an air conditioning system having only a cooling function, which is not opened during this trip. An air supply device is arranged on the ceiling of the rear-row area of the bus, and an exhaust device is arranged on the ceiling of the front-row area. In addition, the heating system is configured as a ground convection radiator on both sides of the bus, but only the radiator on the driver side is in operation at the time.

The bus ventilation rate varies with the speed of the bus, with higher ventilation rates at higher speeds. In the stage of getting on/off before and after the journey and temporary stopping, the bus waits for passengers to get on/off in standby, the engine is in an idle state, the speed is 0km/h, and the ventilation rate of the bus is 0.62/h. When the bus is driven from the city to the suburban area, the bus running speed is slower to 20-30km/h, and the ventilation rate of the bus is 2.46/h. When the bus is running in suburban area, the running speed of the bus is 30-40km/h, and the ventilation rate of the bus is 2.46/h. When the bus runs on the expressway, the running speed is 65-90km/h, and the ventilation rate of the bus is 6.02/h.

After determining a number of different propagation phases, the propagation rate of the virus in a number of different preset virus propagation states may be obtained in each propagation phase.

Since viruses spread at the virus source, there are various states, for example, adhering to the air, adhering to different objects, scattering, or being inhaled, etc. The propagation speed is different in different states, and the speed of the device in different states can be obtained.

Whereas a markov chain is a random process that transitions from one state to another in a state space. In a discrete-time markov chain, physical elements in the environment (e.g., air, surfaces, skin, mucous membranes, etc.) and virus removal mechanisms (e.g., inactivation, ventilation, filtration, etc.) may be represented as different "states". Viruses can be transferred between different "states" due to some physical mechanisms of draining, sedimentation, resuspension, filtration, and aeration. The dose or volume that it propagates can be determined from the change in its state transition.

For example, in a bus environment, viruses exhaled by an infected person may be expelled into the air, porous surfaces, and non-porous surfaces within the bus, indicating that the amount of virus carried in the aerosol generated by the case is N. The porous surface within a bus is generally referred to as the seat back surface and the non-porous surface is generally referred to as the armrest surface. The present invention uses markov chains to estimate exposure doses and associated risk of infection for susceptible persons in buses through the contact and aerosol propagation pathways of viral transmission, and then combines the different risks to estimate the overall risk of infection.

In one embodiment, the preset virus propagation state includes:

close range air state (abbreviated as state (1)), remote range air state (abbreviated as state (2)), close range non-porous surface state (abbreviated as state (3)), close range porous surface state (abbreviated as state (4)), remote range non-porous surface state (abbreviated as state (5)), remote range porous surface state (abbreviated as state (6)), close range susceptible person hand state (abbreviated as state (7)), close range susceptible person facial mucosa state (abbreviated as state (8)), close range susceptible person lower respiratory tract state (abbreviated as state (9)), remote range susceptible person hand state (abbreviated as state (10)), remote range susceptible person facial mucosa state (abbreviated as state (11)), remote range susceptible person lower respiratory tract state (abbreviated as state (12)), virus is discharged from the environment to be detected to external state (abbreviated as state (13)), virus inactivated state (abbreviated as state (14)); 14 species in total.

To calculate the velocity in the different states, assume that the volume of the short-range zone in the bus is V _sr The volume of the remote area is V _lr . The ventilation rate of the bus is Q (/ h). The air exchange rate of the short-distance area and the long-distance area is beta respectively ₁₂ And beta ₂₁ . The asymptomatic viral infected person discharges the virus into the air in a close range by breathing or the like.

In the Markov chain, state 13 (the virus is expelled from the bus to the outside) is a fully absorbed state, its P _13，13 =1. State 14 is viral inactivation and the exponential rate of viral transition from states 1, 2, 3, 4, 5, 6, 7, 10 to state 14 is λ _i，14 ＝η _i . Wherein eta _i Is the inactivation rate of the virus on different surfaces.

State 2 (remote zone air) viral passage index rate lambda _2，13 (λ _2，13 ＝Q/V _lr ) Move to state 13, or at an exponential rate λ _2，1 (λ _2，1 ＝β ₂₁ /V _lr ) Moving to state 1 (close range air), the virus is deposited at a rate λ _2，5 、λ _2，6 (λ _2，5 ＝λ _2，6 ＝Dep ₂ ) Deposited to State 5 (remote zone non-porous surface), state 6 (remote zone porous surface), virus at an exponential rate λ _2，12 (I _in Inhalation rate for susceptible person) into state 12 (lower respiratory tract of susceptible person in distant region), the virus is at exponential rate lambda _2，14 (λ _2，14 ＝η _n ) Deactivation. Thus, the total exponential rate of virus exit from state 2 is λ ₂ ＝λ _2，1 +λ _2，5 +λ _2，6 +λ _2，12 +λ _2，13 +λ _2，14 . Probability that it is still in state 2 for a time step +.>Then its probability of leaving state 2 is +.>The probability of the virus leaving state 2 to move to state 13 and state 1 is respectively

Since the air in the short-range area is not circulated with the outside air of the bus, the virus in state 1 cannot move directly to state 13 (needs to move to state 2 first) within a time step, then P _1，13 =0. The virus is at an exponential rate lambda _1，2 (λ _1，2 ＝β ₁₂ /V _sr Entering state 2, the virus is deposited at a rate λ _1，3 、λ _1，4 (λ _1，3 ＝λ _1，4 ＝Dep ₁ ) Deposited to State 3 (close range non-porous surface), state 4 (close range porous surface), virus at an exponential rate λ _1，9 (I _in Inhalation rate for susceptible person) into state 9 (lower respiratory tract of susceptible person in close range), the virus is at exponential rate lambda _1，14 (λ _1，14 ＝η _a ) Deactivation. Thus, the total exponential rate of virus exit from state 1 is λ ₁ ＝λ _1，2 +λ _1，3 +λ _1，4 +λ _1，9 +λ _1，14 . Thus, the probability that the virus is still in state 1 for a time step +.> Probability of it entering state 2->

State 3 (short-range non-porous surface) surface area A _sr1 State 4 (close range porous surface) surface area A _sr2 . State 5 (remote zone non-porous surface) surface area A _lr1 State 6 (remote zone porous surface) surface area A _lr2 。

Viruses can be transferred between states 3 and 7, states 4 and 7, and states 5 and 10, and states 6 and 10 in the remote region by hand contacting the surface. And the virus can be transferred from state 7 to state 8, from state 10 to state 11 by touching the mucosa with the hand. The present invention thus contemplates ten rates of viral transmission between the surfaces of different areas within the bus. The specific calculation is as follows:

The rate for the near zone is calculated as follows:

the velocity of the remote zone is calculated as follows:

/>

in the above formula, wherein,C _h，m representing the frequency of contact of the non-porous surface, porous surface and facial mucosa, respectively, by the hands of a susceptible person; />α _h，m Respectively representing the transfer efficiency of virus from hand to non-porous surface, hand to porous surface, non-porous surface to hand, hand to mucosa; a is that _h Representing the hand area; a is that _ch，s Representing the area of contact area of the hand with the surface; a is that _sr1 ，A _sr2 ，A _lr1 ，A _lr2 Representing the areas of the non-porous and porous surfaces in the near and far regions; a is that _ch，f Representing the contact area of the hand and the facial mucosa; a is that _m Representing the area of facial mucosa.

S22, calculating a state transition propagation rate between two states by using a plurality of propagation rates, wherein the state transition propagation rate is a propagation rate of virus transformed from a current preset virus propagation state to another preset virus propagation state.

Referring to fig. 3, a schematic diagram of a propagation velocity matrix according to an embodiment of the present invention is shown.

In one embodiment, lambda may be used _ij Representing the propagation rate or clearance rate of the virus between the two "states" of states i and j. As shown in fig. 3. The possible propagation path of the virus between the two states is the propagation rate lambda _ij 。

In the propagation velocity matrix, some lambda _ij The value is equal to 0. For example, in fig. 3, states 9 and 12, once the virus has been transferred or deposited into the respiratory tract of a susceptible individual, the virus cannot leave to transfer to the next state, which states are referred to as "absorbed states". Thus, states 13 and 14 are also "absorbed states", and the virus is discharged from the bus to the outside and the virus is inactivated, and the virus cannot be transferred to the next state. Total rate of virus leaving state i (lambda _i ) Is the rate constant (lambda) at which the virus leaves the state _ij ) Is a sum of (a) and (b).

Specifically, the total rate λ of virus leaving state i _i Is calculated as follows:

s23, constructing a Markov chain probability transition matrix corresponding to each propagation stage by utilizing a plurality of state transition propagation rates.

Referring to fig. 3, since there are n states, where n=14. Therefore, the Markov chain probability transition matrix p constructed using the 14 state transition propagation rates is an n×n probability matrix. And, since the rate refers to data within one propagation phase, each of the propagation phases corresponds to one markov chain probability transition matrix.

Considering that a virus is in state i at time t, in the next time step Δt, there is a certain probability that the pathogen remains in state i, denoted P _ii And moves to another state j with a certain probability, denoted as P _ij 。P _ij The sum of (for j=1, 2, … …, 12) is equal to 1.

Wherein P is _ii The calculation of (2) may be as follows:

P _ij the calculation of (2) may be as follows:

and S24, performing dose calculation by adopting a plurality of Markov chain probability transition matrixes to obtain a propagation dose value corresponding to the virus in an aerosol path or a contact path.

In this model, the Markov chain process repeats the calculation in each stage until the final rate value of the stage is used as the final calculation result of the stage, and the Markov chain probability transition matrix is generated according to the result.

After the calculation of one stage is completed, the calculation of the next stage is performed, then a new Markov chain probability transition matrix p is generated in the next stage, and the propagation dose value of the stage is calculated again until the simulation period is finished.

The propagation range is small and the amount of the transmitted virus is small due to the droplet propagation path, so that the transmission dosage is not considered in the invention.

In one embodiment, consider the time step (Δt) of the Markov chain process to be t seconds. If m time steps exist between two infected persons and the state space after releasing the viruses, the expected exposure dose of the viruses in each state after k times of virus discharge can be used as a transmission dose value, and k can represent the number of times of virus discharge in each state.

Specifically, the propagation dose value is D, and the calculation of the propagation dose value corresponding to the virus in the aerosol path or the contact path is shown as the following formula:

D _k ＝[D _k-1 ×P ^(m) ]+N _k ；

in the above, P ^(m) Mark of virus after m time stepsThe Kelvin probability transition matrix, expressed as m p multiplications, D _k Represents the distribution of viral dose in each state after k times of viral discharge, N _k The amount of virus entering each state at the time of the kth virus discharge is shown.

In one embodiment, the amount of virus entering each state upon expulsion of the virus is represented by the formula:

The evaporation time of the liquid drop in the air is less than 0.1 seconds, and the size of the liquid drop is reduced to one third when the liquid drop is evaporated. Thus, assuming that in all regions the inhaled aerosol droplets are evaporated, the diameter d will be one third of its original diameter, i.e. d=d ₀ /3。

After evaporation, the virus activity in the droplets drops sharply to one fourth of its original value and then drops slowly. It is therefore assumed that the final concentration of live virus in the aerosol droplets is one quarter of the initial concentration, i.e Wherein L is ₀ Is the initial concentration of live virus (TCID) ₅₀ /mL or genome copies/mL) and is assumed to be independent of the initial diameter of the expelled droplet.

Therefore, the calculation formula of the propagation dose value of the virus corresponding to the aerosol propagation path can be simplified into the following formula:

then N (T) can be taken as N _k Recalculating the propagation dose value。

After the calculation of one stage is completed, the calculation of the next stage is performed, and then a new Markov chain probability transition matrix p is generated in the next stage until the simulation period is finished.

After a plurality of Markov chain probability transition matrixes are obtained, the Markov chain probability transition matrixes can be utilized to calculate the dose, so that the propagation dose value of the virus corresponding to the aerosol path or the contact path is obtained.

By statistics, in practical applications, the virus may be inactivated in air, and in a preferred embodiment, the above state (9) and state (12) may be prioritized when calculating the propagation dose value corresponding to the aerosol route. Correspondingly, the above-mentioned state (8) and state 1 (1) may be prioritized when calculating the propagation dose value corresponding to the contact path.

In an alternative embodiment, with respect to calculating the propagation dose value of the contact propagation path, the manner of operation may further comprise the sub-steps of:

s31, extracting contact transition probability from the behavior parameters, wherein the contact transition probability is the probability of virus transition caused by contact of a hand of a user with different areas in an environment to be detected.

In an embodiment, the behavior of the user on the bus may be counted in advance to calculate the probability of touching different places, so as to obtain the behavior parameters. And directly extracting from the behavior parameters to obtain the contact transition probability. The contact transition probability may be the probability that a user is within a bus, with his hands in contact with different areas, resulting in a viral transition.

For example, assume that the total bus volume V is 60.02m3, while the short-range bus volume V _sr Estimated to be 7.08m3, remote zone volume V _lr Estimated to be 53.34m3.

The passenger in the bus seat can touchMost of the surfaces that are touched are the seat back (porous surface) and the armrests (non-porous surface). Assume that the area A of the seat back (porous surface) _s2 Area A of the handrail (non-porous surface) of 0.027m2 _s1 0.005m2.

The relevant parameters of the finished bus are shown in the following table:

the relevant parameters of the user contact area are shown in the following table:

The air exchange rate beta between the near and far areas in the bus changes with the change of the bus ventilation rate. When the bus speed is 0km/h, the ventilation rate of the bus is 0.62/h, and the air exchange rate beta from the short-distance area to the long-distance area is equal to the air exchange rate beta ₁₂ 0.1419m ³ S, air exchange rate beta from long distance zone to short distance zone ₂₁ 0.1296m ³ And/s. When the bus is 20-30km/h, the ventilation rate of the bus is 2.46/h, beta ₁₂ 0.1419m ³ /s，β ₂₁ 0.1296m ³ And/s. The bus running speed is 65-90km/h, the ventilation rate of the bus is 6.02/h, beta ₁₂ 0.2044m ³ /s，β ₂₁ 0.2218m ³ /s。

Specifically, the air exchange rate between different areas of the bus may be as shown in the following table:

when viral infectivity is lost, the virus may transition from states 1-7, 10 to state 14. The inactivation rate of the present invention with respect to viruses was estimated based on the survival time of SARS-CoV-2 Omicron variant and original strain.

In one embodiment, the inactivation rates of viruses at different sites can be as shown in the following table:

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according to the above table, the inactivation rate of the original strain of virus on the porous surface was 0.32/h, and the inactivation rate on the non-porous surface was 0.043/h. The inactivation rate of the variant of the virus was 0.083/h on the porous surface and 0.0091/h on the non-porous surface. The inactivation rate of the virus original strain of the hand skin is 0.21/h, and the inactivation rate of the virus original strain in the air is 0.22/h. Since the inactivation rates of the virus variants have not been studied clearly, since the inactivation rates of the virus variants on porous and non-porous surfaces are about 1/4 of the inactivation rate of the original virus strain, respectively, in one embodiment, the present invention simply assumes that the inactivation rates of the omacron variants on hands and in air are about 1/4 of the inactivation rate of the original virus strain, i.e., 0.05/h and 0.055/h.

During contact, the transfer rate (defined as the proportion of virus transferred between the contact site and the contact surface) varies greatly between the hand and the surrounding surface of the different materials. There is currently no data on the transfer rate of viruses between hands and hands/surface. Thus, the present invention uses the parameter values of the transfer rates of SARS-CoV and Influenza A H1N1 as a substitute for viruses.

The transfer rate of virus from hand to non-porous surface ranged from 13.6% to 44.6%, average value was 27%, and the transfer rate of virus from non-porous surface to hand ranged from 15% to 61.5%, average value was 29% ^[34] . In the process of contacting the mucosa by hands of the susceptible people, the transfer rate of the virus from the hands to the mucosa is 36 percent, and the range is 34 percent to 41 percent ^[35] 。

The probability of contact transfer between different surfaces of a virus can be shown in the following table:

the above table summarizesThe frequency of passenger surface contact and the surface area of the passenger surface contact area on the bus. During the bus driving process, passengers on the bus cannot walk at will. Thus, reference can be made to the simulation of a person in the cabin, assuming the frequency C at which the passenger touches the front seat back in the bus _h，s2 Frequency C of touching the handrail 3 times/h _h，s1 Frequency C of touching facial mucosa at 5 times/h _h，m 16 times/h. Hand contact area A _ch，s Assume 40cm ² Area of mucosa A _m Assume 10cm ² Finger contact area A _ch，f Estimated to be 2cm ² 。

In one embodiment, the bus passenger surface contact frequency may be as shown in the following table:

in one embodiment, the surface area of the contact area between the bus occupant and the surface may be as shown in the following table:

in one embodiment, the lung ventilation rate varies from age to age due to susceptibility within the bus. Assuming that passengers on bus are between 20 and 60 years old, their lung ventilation rate p is 0.67m ³ It can be assumed that the deposition coefficient k of the droplets on the surface indicating case exhalation is 0.14/h.

In particular, the deposition coefficient of the droplets on the surface and the lung ventilation rate can be shown in the following table:

s32, carrying out dose calculation by adopting the contact transfer probability and a propagation dose value corresponding to the virus in an aerosol propagation path to obtain a propagation dose value corresponding to the virus in the contact propagation path.

In an implementation manner, the calculation of the propagation dose value corresponding to the contact propagation path may determine whether the contact transition probability is greater than a preset value, if so, determine the corresponding velocity according to the propagation velocity matrix of fig. 3, and calculate the propagation dose value according to the calculation formula of the propagation dose value corresponding to the virus in the aerosol propagation path.

Specifically, the propagation dose value corresponding to the aerosol propagation and the propagation dose value corresponding to the contact propagation can be calculated according to the propagation velocity matrix of fig. 3 and the calculation formula of the propagation dose value corresponding to the virus in the aerosol propagation path.

For example, aerosol propagation pathways: in state 9 (short-range lower respiratory tract) the dose of the aerosol propagation path in the short-range region can be calculated, and in state 12 (long-range lower respiratory tract) the propagation dose value of the aerosol propagation path in the long-range region can be calculated.

The dose of the aerosol transmission route of the bus in the whole without distinguishing the far and near distance regions is obtained by averaging the doses of all the susceptible persons in the bus, for example (4 person at near distance 9 dose+10 person at far distance 12 dose)/14 person=the transmission dose value of the aerosol transmission route.

Contact propagation route: in state 8 (near-distance susceptible facial mucosa), the dose of the contact propagation path in the near-distance region can be calculated, and in state 11 (far-distance susceptible facial mucosa), the dose of the contact propagation path in the far-distance region can be calculated. The overall dose delivered is calculated in the same way as the aerosol delivery route.

After the propagation doses corresponding to different routes are calculated, the propagation risk can be calculated according to a negative index dose response model.

In an embodiment, the transmission risk may represent the extent to which the virus affects the user during the transmission process, and may also represent the risk or probability of the user infecting the virus.

In an embodiment, said calculating a propagation risk value using said propagation dose value comprises:

s41, calculating an in-vivo estimated dose value of the user after virus inhalation by using the preset breathing model and adopting the propagation dose value.

S42, calculating a transmission risk value by using the in-vivo predicted dose value by using a negative index dose response model.

Specifically, users expel viruses into the environment primarily through respiratory activity such as respiration, speech, coughing, and sneezing. Persistent asymptomatic/latent asymptomatic infected persons account for approximately 57.5% of the viral transmission. Persistent asymptomatic infected persons are defined as subsequent definitive viral infections, but still without symptoms throughout the course of the infection or after 14 days of follow-up; asymptomatic patients in latency are defined as those who have not developed symptoms upon initial screening of positive test results, and, over a period of latency, develop associated clinical manifestations as the course of the disease progresses.

In the study, the virus concentration L in the droplets in persistent asymptomatic/latent asymptomatic infection _a Set to 10 ⁵ To 10 ⁹ Each RNA copies/mL. In the process of virus discharge to the environment, the invention mainly considers that: 1) The frequency of respiratory motion; 2) The number of droplets discharged per respiratory movement; 3) The particle size distribution of the droplet particles; 4) Virus concentration in the droplets.

For the first point, the frequency of respiratory motion:

in persistent asymptomatic/latent asymptomatic infections, the infected person expels the virus into the environment, mainly by breathing. Assume that during bus operation, the respiratory rate of an infected person is 12/min ^[65] 。

The number of droplets discharged per respiratory movement for the second point and the size distribution of the droplet particles for the third point.

Due to the b.l.o (Bronchiolar/laryngel/Oral) model of aerosol concentration size distribution during breathing, speaking and coughing in healthy adults. This model relates to three different processes: one occurs deep in the lower respiratory tract, another occurs in the laryngeal region and a third occurs in the upper respiratory tract, including the oral cavity. The BLO model distribution follows the sum of three lognormal distributions, with only breathing being applicable to B-mode. During bus operation, an infected person mainly discharges viruses to the environment through respiration, so the invention takes the virus as a preset respiration model, wherein the preset respiration model can be shown as follows:

Wherein the following table is the model parameters of the aerosols produced by the volunteers when breathing and speaking.

In the above table, cn _i Is the concentration; CMD (CMD) _i The median diameter is expressed as the particle size of 50% of the total number of particles, when the particles are ordered by particle size, the number of particles larger than the particle size and smaller than the particle size; GSD (GSD) _i Is the geometric standard deviation

Referring to fig. 4, a schematic diagram of the particle size distribution of breath-generated droplets is shown, according to an embodiment of the present invention.

In one embodiment, the size distribution of the droplet particles is shown in FIG. 4. When an infected person breathes, 2 minutes of breathing, about 8000 droplets can be produced by nasal ingress and egress, ranging in size from 0 μm to 20 μm. Thus, the saliva volume per 2 minutes breath can be estimated as follows:

regarding the fourth point, the virus concentration in the droplets was reported to be 3.3X10 median in samples of the salivary virus concentration of the infected person ⁶ RNA copies/mL (range: 9.9X10) ² Up to 1.2X10 ⁸ RNA copies/mL). Saliva SARS-CoV-2 at an average concentration of 10 ^5.58 RNA copies/mL in the range of 10 ⁴ To 10 ¹⁰ RNA copies/mL. In the present invention, it is considered that when the virus is less than 10 ⁵ At RNA copies/mL, the risk of infection is very low, so the viral concentration in the droplets discharged by patients with persistent asymptomatic/latent asymptomatic infection is set to 10 ⁵ To 10 ⁹ Each RNA copies/mL.

After the user inhales the virus, the in-vivo predicted dose value of the user is calculated in the mode, and the transmission risk value can be calculated by using the negative index dose response model and the in-vivo predicted dose value.

In one embodiment, the in vivo predicted dose value may comprise: the dose of virus in the upper respiratory tract and mucosa of the user.

It is noted that a negative index dose response model is used to estimate the risk of transmission or infection, which means that a single virus can cause an infection, and that all viral particles are independent of each other. The risk of infection for both near zone and far zone predisposers during exposure can be calculated according to the following equation:

in the above formula, eta _l And eta _m The dose response rates of the lower respiratory tract and mucosa, respectively. D (D) _a 、D _c The predicted dose value of the virus in the upper respiratory tract and mucosa of the user, respectively. In the present invention, it can be assumed that the dose response rate of the original strain and the omacron variant strain is the same. Let eta _l And eta _m Although the ratio of lower respiratory tract and mucosal infection dose response rate 100:1 may be overestimated, the present invention sets the ratio of 100:1 based primarily on human influenza virus data, η for influenza virus _l And eta _m May=1000:1.

In the present invention, it can be assumed that the lower respiratory dose response rate η of the virus _l For 0.2460/mRNA copy, mucosal dose response rate η _m 0.00246/mRNA copy. Lower respiratory dose response rate eta of virus _l For 2.4755/mRNA copy, mucosal dose response rate η _m 0.00247/mRNA copy. For the virus Influenza A H1N1, it can be assumed that the lower respiratory dose response rate η of Influenza A H1N1 _l For 1.0354/mRNA copy, mucosal dose response rate η _m 0.001386/mRNA copy.

In particular, the dose response rates of the lower respiratory tract and mucosa of different viruses can be shown in the following table:

in order to explore effective measures for controlling viruses on buses, in an alternative embodiment, the invention adjusts partial parameters in the model within a certain range to obtain the change relation between the infection risk and the parameters of susceptible people on the buses, and provides the measures for controlling various transmission ways on the basis.

Specifically, the summary of the model parameter change conditions can be shown in the following table:

the difference of infection risks of susceptible people in a short-distance area and a long-distance area in the passenger bus and the relation between the infection risks and related environmental parameters are discussed, and the differences of the transmission capacities of the original virus strain and the variant strain, the severe acute respiratory syndrome virus and the influenza A virus are compared.

To facilitate further investigation and risk assessment of the virus, in one embodiment, after the step of obtaining the propagation dose values of the virus in different propagation paths, the method further comprises:

s51, carrying out propagation analysis by adopting the propagation dosage value, wherein the propagation analysis comprises the following steps: infection contribution ratio, risk comparison, infectious viral load, and viral transmission factor for each pathway.

A comparison analysis can be made of the risk and overall risk for each pathway.

For example, the risk of infection and overall infection for each pathway depends on the concentration of virus in saliva produced by the indicated case.

For another example, the infection contribution ratio of each pathway may be analyzed.

The primary infection route during persistent asymptomatic/latent asymptomatic infection is the same for both strains. Wherein the aerosol propagation path contributes the most, ranging from 92% to 100%, and the contact propagation path contributes less, ranging from 0% to 8%. As the concentration of virus in saliva increases, the contribution of infection by the contact transmission pathway of the variant strain increases compared to the original strain.

For the original strain, in the close range, the aerosol transmission path infection contribution range is 99% -100%, and the contact transmission path infection contribution is 0% -1%. In the distant region, aerosol transmission path infection contribution ranges from 94% to 98%, and contact transmission path infection contribution ranges from 2% to 6%. For variants, in the close range, aerosol transmission pathway infection contributes 99% -100% and contact transmission pathway infection contributes 0% -1%. In the distant region, the aerosol transmission path infection contribution ranges from 92% to 98%, and the contact transmission path infection contribution ranges from 2% to 8%.

For another example, the risk of infection in the near and far regions of different dose response rates may be analyzed.

To evaluate the effect of dose-response rate on the results, different dose-response rates were set for the variants. At baseline, the dose response rates for the mucosa and lower respiratory tract were respectively: η (eta) _m = 0.00246 and η _l =0.246. Since the infectivity of the mutant strain is higher than that of the original strain, eta is set _m ＝0.005,η _l =0.5, and η _m ＝0.01,η _l Two different dose response rates =1.

As dose response rates increase, the risk of infection of both the variant aerosol transmission pathway and the contact transmission pathway increases. For the aerosol transmission route, as the dose response rate increases, the rate of increase of the risk of infection of the aerosol transmission route gradually decreases and approaches 1 as the salivary virus concentration gradually increases.

As the dose response rate increases, the risk of infection by the contact transmission route increases as the concentration in the salivary virus increases. The risk of infection in the near and far regions also increases with the dose response rate.

For another example, a comparison analysis of risk of infection between a mask being worn and a mask not being worn may be performed.

It is assumed that all passengers wear the mask throughout the bus travel. The virus filtration rate of these masks was assumed to be 95%, similar to the effect of surgical masks on virus filtration ^[59] While assuming that the mask covers only the oral and nasal areas, the coverage is 54%. The risk of infection of passengers in buses when wearing and not wearing the masks can be found as shown in the following table:

for another example, the amount of infectious virus in the near and far areas at different stages can be analyzed.

The virus amount of the non-porous surface, hands of the susceptible person, facial mucosa and lower respiratory tract of the near-distance area and the far-distance area gradually increases with the time. The viral load of the lower respiratory tract of the susceptible person in the near-distance region is the most, the viral load of the hands of the susceptible person is the least, the viral load of the nonporous surface in the far-distance region is the most, and the viral load of the hands of the susceptible person is the least. The virus growth rate of each stage is different, the virus amount of the nonporous surface and the porous surface of the near-distance region and the far-distance region is the highest in the stage 2, and the virus amount of the lower respiratory tract of the susceptible person and the facial mucosa of the susceptible person is the highest in the stage 4.

The SARS-CoV-2 viral load at the end of the stroke was 0.28RNA copies/mL, 0.12RNA copies/mL, 0.01RNA copies/mL, 0.10RNA copies/mL, 0.35RNA copies/mL in the short-distance region, and the viral load at the end of the stroke was 1.96RNA copies/mL, 0.83RNA copies/mL, 0.07RNA copies/mL, 0.67RNA copies/mL, 0.32RNA copies/mL in the long-distance region.

Also for example, the effect of deactivation rate on aerosol pathway transmission.

The risk of infection of a susceptible person by aerosol route, contact route and the trend of the total risk of infection when the rate of inactivation of viruses in air on passenger buses varies. For the aerosol route, increased viral inactivation in air is effective to reduce the risk of infection in short-range, long-range, and the whole. When the viral inactivation rate in air increased from 0.11/hr to 2.2/hr, the risk of infection in the short-range region of the aerosol route decreased from 17.62% to 4.61%, the risk of infection in the long-range region of the aerosol route decreased from 16.42% to 4.61%, and the total risk of infection decreased from 16.87% to 3.92%.

As another example, the effects of inactivation rate, transfer rate, and contact pathway propagation.

The effect of the inactivation rate of viruses on non-porous surfaces, porous surfaces and in air on passenger buses, and the transfer rate of viruses from non-porous surface to hand, hand to non-porous surface, hand to porous surface on the risk of infection of susceptible persons was analyzed.

The risk of infection in the short-range area of the contact pathway, the risk of infection in the long-range area of the contact pathway, and the total risk of infection decrease with increasing inactivation rate of the virus on the non-porous surface. When the viral inactivation rate on non-porous surfaces increased from 0.0215/hr to 0.43/hr, the risk of infection in the short-range zone of the contact pathway was from 4.907X 10 ^-4 Reduced to 1.778×10 ^-4 The risk of infection in distant areas of the contact route is 3.388 ×10 ^-3 Reduced to 1.22×10 ^-3 The total infection risk was reduced from 14.42% to 14.26%.

Referring to fig. 5, an operation flowchart of a method for calculating a virus propagation risk based on a markov chain model according to an embodiment of the present invention is shown.

Specifically, the operation of the markov chain model-based virus propagation risk calculation method may include the steps of:

the first step is to obtain the environmental characteristic parameters of the environment to be detected, the specific parameters of the virus to be detected and the behavior parameters of the user in the environment to be detected respectively.

Secondly, inputting the environmental characteristic parameters, the specificity parameters and the behavior parameters into a Markov chain model for calculation to respectively obtain aerosol transmission dose and contact transmission dose;

third, the risk is calculated using the aerosol-and contact-delivered doses.

The risk of infection and the overall risk of infection for each pathway, as well as the infection contribution ratio for each pathway, can be calculated at different virus concentrations, at different dose response rates; calculating the infection risk and the overall infection risk of each path in a short-distance area and a long-distance area in the bus and the infection contribution ratio of each path respectively; comparing the infection risk of the wearing mask with the infection risk of the non-wearing mask; the virus concentration in the short-distance and long-distance areas under different driving stages of the bus; the effect of viral inactivation rate and transfer rate on different transmission pathways and total infection risk, etc.

In this embodiment, the present invention provides a markov chain model-based virus propagation risk calculation method, which has the following beneficial effects: according to the invention, the environmental characteristic parameters, the specific parameters and the behavior parameters can be respectively acquired, the Markov chain model is utilized to comprehensively evaluate and calculate the environmental, virus specific characteristics and the behavior parameters, the doses of viruses in different transmission paths are obtained through calculation, and then the risks of the viruses are evaluated by combining the doses of the viruses in different paths, so that the deviation of evaluation and calculation can be reduced, and the calculation precision is improved.

The embodiment of the invention also provides a virus propagation risk calculating device based on the Markov chain model, and referring to FIG. 6, a schematic diagram of a virus propagation risk calculating device based on the Markov chain model is shown.

Wherein, as an example, the markov chain model-based virus propagation risk calculating means may comprise:

an obtaining module 601, configured to obtain parameter information, where the parameter information includes: environmental characteristic parameters of the environment to be detected, specific parameters of viruses to be detected and behavior parameters of users in the environment to be detected;

The risk calculation module 602 is configured to perform dose calculation by using the parameter information based on a preset markov chain model, obtain propagation dose values of the virus in different propagation paths, and calculate a propagation risk value by using the propagation dose values.

Optionally, the calculating the dose by using the parameter information based on the preset markov chain model to obtain the propagation dose values of the virus in different propagation paths includes:

Optionally, the calculation of the propagation dose value of the virus corresponding to the aerosol route or the contact route is shown as follows:

D _k ＝[D _k-1 ×P ^(m) ]+N _k ；

Optionally, the amount of virus entering each state when the virus is expelled is as follows:

in the above, d _o Is the initial diameter of the droplet, d _a Is the largest initial straight of aerosol dropletsDiameter L _a Is the concentration of live virus in the inhaled droplets of the susceptible individual, f (d) is the probability distribution function of the particle size of the initially expelled droplets, n _a Is the number of droplets produced by respiratory activity per unit time, T being the time.

Optionally, the preset virus propagation state includes:

Optionally, the calculating the propagation risk value using the propagation dose value includes:

Optionally, the preset breathing model is shown in the following formula:

Optionally, after the step of obtaining the propagation dose value of the virus in different propagation paths, the method further comprises:

It will be clearly understood by those skilled in the art that, for convenience and brevity, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

Further, an embodiment of the present application further provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed implements a markov chain model-based virus propagation risk calculation method as described in the above embodiments.

Further, the embodiment of the application also provides a computer readable storage medium, which stores a computer executable program for causing a computer to execute the method for calculating the virus propagation risk based on the markov chain model according to the embodiment.

It will be appreciated by those skilled in the art that embodiments of the present application may also provide a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), devices and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.

Claims

1. A method for calculating risk of viral transmission based on a markov chain model, the method comprising:

2. The method for calculating the virus propagation risk based on the markov chain model according to claim 1, wherein the calculating the dose based on the preset markov chain model by using the parameter information to obtain the propagation dose values of the virus in different propagation paths comprises:

3. The method for calculating the risk of viral propagation based on a markov chain model according to claim 2, wherein the calculation of the propagation dose value of the virus corresponding to the aerosol route or the contact route is represented by the following formula:

D _k ＝[D _k-1 ×P(m)]+N _k ；

4. The method for calculating the risk of viral propagation based on a markov chain model according to claim 2, wherein the preset viral propagation state includes:

5. The method for calculating a risk of viral propagation based on a markov chain model according to claim 1, wherein calculating a risk of viral propagation value using the propagation dose value comprises:

6. The method for calculating the risk of viral propagation based on a markov chain model according to claim 5, wherein the predetermined breathing model is represented by the following formula:

7. The method for calculating the risk of viral propagation based on a markov chain model according to any one of claims 1 to 6, wherein after the step of obtaining the values of the propagation dose of the virus in different propagation paths, the method further comprises:

8. A markov chain model-based virus propagation risk calculation device, the device comprising:

9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements a markov chain model based virus propagation risk calculation method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer-executable program for causing a computer to execute the markov chain model-based virus propagation risk calculation method according to any one of claims 1 to 7.